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"We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy. ",
"The method has been tested for various data sets, and proven to be significantly more efficient than most existing compressing techniques in the deep learning literature. ",
"For many popular data sets such as MNIST and CIFAR-10, more than 95% of the weights can be zeroed out without losing accuracy.",
"In particular, we are able to make ResNet18 with 95% sparsity to have an accuracy that is comparable to that of a much larger model ResNet50 with the best 60% sparsity as reported in the literature.",
"In recent decades, deep neural network models have achieved unprecedented success and state-ofthe-art performance in various tasks of machine learning or artificial intelligence, such as computer vision, natural language processing and reinforcement learning BID11 .",
"Deep learning models usually involve a huge number of parameters to fit variant kinds of datasets, and the number of data may be much less than the amount of parameters BID9 .",
"This may implicate that deep learning models have too much redundancy.",
"This can be validated by the literatures from the general pruning methods BID18 to the compressing models BID6 .While",
"compressed sensing techniques have been successfully applied in many other problems, few reports could be found in the literature for their application in deep learning. The idea",
"of sparsifying machine learning models has attracted much attention in the last ten years in machine learning BID2 ; BID22 . When considering",
"the memory and computing cost for some certain applications such as Apps in mobile, the sparsity of parameters plays a very important role in model compression BID6 ; BID0 . The topic of computing",
"sparse neural networks can be included in the bigger topic on the compression of neural networks, which usually further involves the speedup of computing the compressed models.There are many sparse methods in machine learning models such as FOBOS method BID3 , also known as proximal stochastic gradient descent (prox-SGD) methods BID16 , proposed for general regularized convex optimization problem, where 1 is a common regularization term. One drawback of prox-SGD",
"is that the thresholding parameters will decay in the training process, which results in unsatisfactory sparsity BID22 . Apart from that, the regularized",
"dual averaging (RDA) method BID22 , proposed to obtain better sparsity, has been proven to be convergent with specific parameters in convex optimization problem, but has not been applied in deep learning fields.In this paper, we analyze the relation between simple dual averaging (SDA) method BID17 and the stochastic gradient descent (SGD) method BID19 , as well as the relation between SDA and RDA. It is well-known that SGD and its",
"variants work quite well in deep learning problems. However, there are few literatures",
"in applying pure training algorithms to deep CNNs for model sparsification. We propose an iterative RDA (iRDA)",
"method for training sparse CNN models, and prove the convergence under convex conditions. Numerically, we compare prox-SGD with",
"iRDA, where the latter can achieve better sparsity results while keeping satisfactory accuracy on MNIST, CIFAR-10 and CIFAR-100. We also show iRDA works for different",
"CNN models such as VGG BID21 and BID9 . Finally, we compare the performance of",
"iRDA with some other state-of-the-art compression methods. BID0 reviews the work on compressing neural",
"network models, and categorizes the related methods into four schemes: parameter pruning and sharing, low-rank factorization, transfered/compact convolutional filters and knowledge distillation. Among them, BID14 uses sparse decomposition",
"on the convolutional filters to get sparse neural networks, which could be classified to the second scheme. Apart from that, BID7 prunes redundant connections",
"by learning only the important parts. BID15 starts from a Bayesian point of view, and removes",
"large parts of the network through sparsity inducing priors. BID23 BID10 combines reinforcement learning methods to",
"compression. BID13 considers deep learning as a discrete-time optimal",
"control problem, and obtains sparse weights on ternary networks. Recently, BID4 applies RDA to fully-connected neural network",
"models on MNIST.",
"In comparison with many existing rule-based heuristic approaches, the new approach is based on a careful and iterative combination of 1 regularization and some specialized training algorithms.",
"We find that the commonly used training algorithms such as SGD methods are not effective.",
"We thus develop iRDA method that can be used to achieve much better sparsity.",
"iRDA is a variant of RDA methods that have been used for some special types of online convex optimization problems in the literature.",
"New elements in the iRDA mainly consist of judicious initialization and iterative retraining.",
"In addition, iRDA method is carefully analyzed on its convergence for convex objective functions.Many deep neural networks trained by iRDA can achieve good sparsity while keeping the same validation accuracy as those trained by SGD with momentum on many popular datasets.",
"This result shows iRDA is a powerful sparse optimization method for image classification problems in deep learning fields.",
"One of the differences between RDA Xiao (2010) and iRDA is that the former one takes w 1 = arg min w h(w) whereas the latter one chooses w 1 randomly.",
"In the following, we will prove the convergence of iRDA Step 1 for convex problem.",
"The proofs use Lemma 9, Lemma 10, Lemma 11 directly and modify Theorem 1 and Theorem 2 in BID22 .",
"For clarity, we have some general assumptions:• The regularization term Ψ(w) is a closed convex function with convexity parameter σ and domΨ is closed.•",
"For each t ≥ 1, f t (w) is convex and subdifferentiable on domΨ.•",
"h(w) is strongly convex on domΨ and subdifferentiable on rint(domΨ) and also satisfies DISPLAYFORM0 Without loss of generality, assume h(w) has convexity parameter 1 and min w h(w) = 0.• There exist a constant G such that DISPLAYFORM1 • Require {β} t≥1 be a nonnegative and nondecreasing sequence and DISPLAYFORM2 Moreover, we could always choose β 1 ≥ σ such that β 0 = β 1 .•",
"For a random choosing w 1 , we assume DISPLAYFORM3 First of all, we define two functions: DISPLAYFORM4 DISPLAYFORM5 The maximum in (37) is always achieved because F D = {w ∈ domΨ|h(w) ≤ D 2 } is a nonempty compact set. Because",
"of (35), we have σt+β t ≥ β 0 > 0 for all t ≥ 0, which means tΨ(w)+β t h(w) are all strongly convex, therefore the maximum in (38) is always achieved and unique. As a result",
", we have domU t = domV t = E * for all t ≥ 0. Moreover,",
"by the assumption (33), both of the functions are nonnegative. Let s t denote",
"the sum of the subgradients obtained up to time t in iRDA Step 1, that is DISPLAYFORM6 and π t (s) denotes the unique maximizer in the definition of V t (s) DISPLAYFORM7 which then gives DISPLAYFORM8 Lemma A.1 For any s ∈ E",
"* and t ≥ 0, we have DISPLAYFORM9 For a proof, see Lemma 9 in Xiao (2010).Lemma A.2 The",
"function",
"V t is convex and differentiable. Its gradient",
"is given by DISPLAYFORM10 and the gradient Lipschitz continuous with constant 1/(σt + β t ), that is DISPLAYFORM11 Moreover, the following inequality holds: DISPLAYFORM12 The results are from Lemma 10 in BID22 .Lemma A.3 For",
"each t ≥",
"1, we have DISPLAYFORM13 Since h(w t+1 ) ≥ 0 and the sequence {β t } t≥1 is nondecreasing, we have DISPLAYFORM14 DISPLAYFORM15 To prove this lemma, we refer to the Lemma 11 in Xiao (2010). What's more,",
"from the assumption 35, we could always choose β 1 ≥ σ such that β 1 = β 0 and DISPLAYFORM16 The learner's regret of online learning is the difference between his cumulative loss and the cumulative loss of the optimal fixed hypothesis, which is defined by DISPLAYFORM17 and bounded by DISPLAYFORM18 Lemma A.4 Let the sequence",
"{w t } t≥1 and {g t } t≥1 be generated by iRDA Step 1, and assume FORMULA2 and FORMULA2 hold. Then for any t ≥",
"1 and any DISPLAYFORM19 Proof First, we define the following gap sequence which measures the quality of the solutions w 1 , .., w t : DISPLAYFORM20",
"and δ t is an upper bound on the regret R t (w) for all w ∈ F D , to see this, we use the convexity of f t (w) in the following: DISPLAYFORM21 Then, We are going to derive an upper bound on δ t . For this purpose, we subtract t τ =1 g τ , w 0 in (53), which leads to DISPLAYFORM22 the maximization term in (55) is in fact U t (−s t ), therefore, by applying Lemma A.1, we have DISPLAYFORM23 Next, we show that ∆ t is an upper bound for the right-hand side of inequality (56). We consider τ ≥ 2 and τ = 1 respectively. For any τ ≥ 2, we have DISPLAYFORM24 where FORMULA3 , FORMULA2 , FORMULA3 and FORMULA2 are used. Therefore, we have DISPLAYFORM25 , ∀τ ≥ 2.For τ = 1, we have a similar inequality by using (49) DISPLAYFORM26 Summing the above inequalities for τ = 1, ..., t and noting that V 0 (−s 0 ) = V 0 = 0, we arrive at DISPLAYFORM27 Since Ψ(w t+1 ) ≥ 0, we subtract it from the left hand side and add Ψ(w 1 ) to both sides of the above inequality yields DISPLAYFORM28 Combing FORMULA3 , FORMULA4 , (57) and using assumption (34) and (36)we conclude DISPLAYFORM29 Lemma A.5 Assume there exists an",
"optimal solution w to the problem (3) that satisfies h(w ) ≤ D 2 for some D > 0, and let φ = φ(w ). Let the sequences {w t",
"} t≥1 be generated by iRDA Step 1, and assume g t * ≤ G for some constant G. Then for any t ≥ 1, the expected cost associated with the random variablew t is bounded as DISPLAYFORM30 Proof First, from the definition (50), we have the regret at w DISPLAYFORM31 Let z[t] denote the collection of i.i.d. random variables (z , ..., z t ). We note that the random",
"variable w τ , where 1 ≤ w ≥ t, is a function of (z 1 , ..., z τ −1 ) and is independent of (z τ , ..., z t ). Therefore DISPLAYFORM32",
"and DISPLAYFORM33 Since φ = φ(w ) = min w φ(w), we have the expected regret DISPLAYFORM34 Then, by convexity of φ, we have DISPLAYFORM35 Finally, from FORMULA4 and FORMULA4 , we have DISPLAYFORM36 Then the desired follows from that of Lemma A.4. Proof of Theorem 3.1 From",
"Lemma A.5, the expected cost associated with the random variablew t is bounded as DISPLAYFORM37 Here, we consider 1 regularization function Ψ(w) = λ w 1 and it is a convex but not strongly convex function, which means σ = 0. Now, we consider how to choose",
"β t for t ≥ 1 and β 0 = β 1 . First if β t = γt, we have 1 t",
"· γtD 2 = γD 2 , which means the expected cost does not converge. Then assume β t = γt α , α > 0",
"and α = 1, the right hand side of the inequality (60) becomes DISPLAYFORM38 From above, we see that if 0 < α < 1, the expected cost converges and the optimal convergence rate O(t We have shown why prox-SGD will give poor sparsity, and although √ t-prox-SGD may introduce greater sparsity, it is not convergent. Finally, iRDA gives the best result",
", on both the top-1 accuracy and the sparsity. iRDA ("
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"A sparse optimization algorithm for deep CNN models."
] |
[
"Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics.",
"Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation.",
"Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon.",
"Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training.",
"We propose a simple alternative that still uses RL, but does not require learning a reward function.",
"The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states.",
"We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior.",
"Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm.",
"Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.",
"Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo.",
"This paper is a proof of concept that illustrates how a simple imitation method based on RL with constant rewards can be as effective as more complex methods that use learned rewards.",
"Many sequential decision-making problems can be tackled by imitation learning: an expert demonstrates near-optimal behavior to an agent, and the agent attempts to replicate that behavior in novel situations (Argall et al., 2009 ).",
"This paper considers the problem of training an agent to imitate an expert, given expert action demonstrations and the ability to interact with the environment.",
"The agent does not observe a reward signal or query the expert, and does not know the state transition dynamics.",
"Standard approaches based on behavioral cloning (BC) use supervised learning to greedily imitate demonstrated actions, without reasoning about the consequences of actions (Pomerleau, 1991) .",
"As a result, compounding errors cause the agent to drift away from the demonstrated states (Ross et al., 2011) .",
"The problem with BC is that, when the agent drifts and encounters out-of-distribution states, the agent does not know how to return to the demonstrated states.",
"Recent methods based on inverse reinforcement learning (IRL) overcome this issue by training an RL agent not only to imitate demonstrated actions, but also to visit demonstrated states (Ng et al., 2000; Wulfmeier et al., 2015; Finn et al., 2016b; Fu et al., 2017) .",
"This is also the core idea behind generative adversarial imitation learning (GAIL) (Ho & Ermon, 2016) , which implements IRL using generative adversarial networks (Goodfellow et al., 2014; Finn et al., 2016a) .",
"Since the true reward function for the task is unknown, these methods construct a reward signal from the demonstrations through adversarial training, making them difficult to implement and use in practice (Kurach et al., 2018) .",
"The main idea in this paper is that the effectiveness of adversarial imitation methods can be achieved by a much simpler approach that does not require adversarial training, or indeed learning a reward function at all.",
"Intuitively, adversarial methods encourage long-horizon imitation by providing the agent with (1) an incentive to imitate the demonstrated actions in demonstrated states, and (2) an incentive to take actions that lead it back to demonstrated states when it encounters new, out-ofdistribution states.",
"One of the reasons why adversarial methods outperform greedy methods, such as BC, is that greedy methods only do (1), while adversarial methods do both (1) and (2).",
"Our approach is intended to do both (1) and (2) without adversarial training, by using constant rewards instead of learned rewards.",
"The key idea is that, instead of using a learned reward function to provide a reward signal to the agent, we can simply give the agent a constant reward of r = +1 for matching the demonstrated action in a demonstrated state, and a constant reward of r = 0 for all other behavior.",
"We motivate this approach theoretically, by showing that it implements a regularized variant of BC that learns long-horizon imitation by",
"(a) imposing a sparsity prior on the reward function implied by the imitation policy, and",
"(b) incorporating information about the state transition dynamics into the imitation policy.",
"Intuitively, our method accomplishes",
"(a) by training the agent using an extremely sparse reward function -+1 for demonstrations, 0 everywhere else -and accomplishes",
"(b) by training the agent with RL instead of supervised learning.",
"We instantiate our approach with soft Q-learning (Haarnoja et al., 2017) by initializing the agent's experience replay buffer with expert demonstrations, setting the rewards to a constant r = +1 in the demonstration experiences, and setting rewards to a constant r = 0 in all of the new experiences the agent collects while interacting with the environment.",
"Since soft Q-learning is an off-policy algorithm, the agent does not necessarily have to visit the demonstrated states in order to experience positive rewards.",
"Instead, the agent replays the demonstrations that were initially added to its buffer.",
"Thus, our method can be applied in environments with stochastic dynamics and continuous states, where the demonstrated states are not necessarily reachable by the agent.",
"We call this method soft Q imitation learning (SQIL).",
"The main contribution of this paper is SQIL: a simple and general imitation learning algorithm that is effective in MDPs with high-dimensional, continuous observations and unknown dynamics.",
"We run experiments in four image-based environments -Car Racing, Pong, Breakout, and Space Invadersand three low-dimensional environments -Humanoid, HalfCheetah, and Lunar Lander -to compare SQIL to two prior methods: BC and GAIL.",
"We find that SQIL outperforms BC and achieves competitive results compared to GAIL.",
"Our experiments illustrate two key benefits of SQIL: (1) that it can overcome the state distribution shift problem of BC without adversarial training or learning a reward function, which makes it easier to use, e.g., with images, and (2) that it is simple to implement using existing Q-learning or off-policy actor-critic algorithms.",
"Related work.",
"Concurrently with SQIL, two other imitation learning algorithms that use constant rewards instead of a learned reward function were developed (Sasaki et al., 2019; Wang et al., 2019) .",
"We see our paper as contributing additional evidence to support this core idea, rather than proposing a competing method.",
"First, SQIL is derived from sparsity-regularized BC, while the prior methods are derived from an alternative formulation of the IRL objective (Sasaki et al., 2019) and from support estimation methods (Wang et al., 2019) , showing that different theoretical approaches independently lead to using RL with constant rewards as an alternative to adversarial training -a sign that this idea may be a promising direction for future work.",
"Second, SQIL is shown to outperform BC and GAIL in domains that were not evaluated in Sasaki et al. (2019) or Wang et al. (2019) -in particular, tasks with image observations and significant shift in the state distribution between the demonstrations and the training environment.",
"Summary.",
"We contribute the SQIL algorithm: a general method for learning to imitate an expert given action demonstrations and access to the environment.",
"Simulation experiments on tasks with high-dimensional, continuous observations and unknown dynamics show that our method outperforms BC and achieves competitive results compared to GAIL, while being simple to implement on top of existing off-policy RL algorithms.",
"Limitations and future work.",
"We have not yet proven that SQIL matches the expert's state occupancy measure in the limit of infinite demonstrations.",
"One direction for future work would be to rigorously show whether or not SQIL has this property.",
"Another direction would be to extend SQIL to recover not just the expert's policy, but also their reward function; e.g., by using a parameterized reward function to model rewards in the soft Bellman error terms, instead of using constant rewards.",
"This could provide a simpler alternative to existing adversarial IRL algorithms.",
"(s, a) ) .",
"Splitting up the squared soft Bellman error terms for D demo and D samp in Equation 8,",
"Setting γ 1 turns the inner sum in the first term into a telescoping sum:",
"Since s T is assumed to be absorbing, V (s T ) is zero.",
"Thus,",
"In our experiments, we have that all the demonstration rollouts start at the same initial state s 0 ."
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] | S1xKd24twB | true | [
"A simple and effective alternative to adversarial imitation learning: initialize experience replay buffer with demonstrations, set their reward to +1, set reward for all other data to 0, run Q-learning or soft actor-critic to train."
] |
[
"Generating visualizations and interpretations from high-dimensional data is a\n",
"common problem in many fields.",
"Two key approaches for tackling this problem \n",
"are clustering and representation learning.",
"There are very performant deep\n",
"clustering models on the one hand and interpretable representation learning techniques, \n",
"often relying on latent topological structures such as self-organizing maps,\n",
"on the other hand.",
"However, current methods do not yet successfully combine\n",
"these two approaches.",
"We present a new deep architecture for probabilistic clustering, \n",
"VarPSOM, and its extension to time series data, VarTPSOM, composed of VarPSOM \n",
"modules connected by LSTM cells.",
"We show that they achieve superior \n",
"clustering performance compared to current deep clustering methods on static \n",
"MNIST/Fashion-MNIST data as well as medical time series, while inducing an\n",
"interpretable representation.",
"Moreover, on the medical time series, VarTPSOM\n",
"successfully predicts future trajectories in the original data space."
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"We present a new deep architecture, VarPSOM, and its extension to time series data, VarTPSOM, which achieve superior clustering performance compared to current deep clustering methods on static and temporal data."
] |
[
"Many computer vision applications require solving multiple tasks in real-time.",
"A neural network can be trained to solve multiple tasks simultaneously using 'multi-task learning'.",
"This saves computation at inference time as only a single network needs to be evaluated.",
"Unfortunately, this often leads to inferior overall performance as task objectives compete, which consequently poses the question: which tasks should and should not be learned together in one network when employing multi-task learning?",
"We systematically study task cooperation and competition and propose a framework for assigning tasks to a few neural networks such that cooperating tasks are computed by the same neural network, while competing tasks are computed by different networks.",
"Our framework offers a time-accuracy trade-off and can produce better accuracy using less inference time than not only a single large multi-task neural network but also many single-task networks.\n",
"Many applications, especially robotics and autonomous vehicles, are chiefly interested in using multi-task learning to reduce the inference time and computational complexity required to estimate many characteristics of visual input.",
"For example, an autonomous vehicle may need to detect the location of pedestrians, determine a per-pixel depth, and predict objects' trajectories, all within tens of milliseconds.",
"In multi-task learning, multiple tasks are solved at the same time, typically with a single neural network.",
"In addition to reduced inference time, solving a set of tasks jointly rather than independently can, in theory, have other benefits such as improved prediction accuracy, increased data efficiency, and reduced training time.",
"Unfortunately, the quality of predictions are often observed to suffer when a network is tasked with making multiple predictions.",
"This is because learning objectives can have complex and unknown dynamics and may compete.",
"In fact, multi-task performance can suffer so much that smaller independent networks are often superior (as we will see in the experiments section).",
"We refer to any situation in which the competing priorities of the network cause poor task performance as crosstalk.",
"On the other hand, when task objectives do not interfere much with each other, performance on both tasks can be maintained or even improved when jointly trained.",
"Intuitively, this loss or gain of quality seems to depend on the relationship between the jointly trained tasks.",
"Prior work has studied the relationship between tasks for transfer learning (Zamir et al. (2018) ).",
"However, we find that transfer relationships are not highly predictive of multi-task relationships.",
"In addition to studying multi-task relationships, we attempt to determine how to produce good prediction accuracy under a limited inference time budget by assigning competing tasks to separate networks and cooperating tasks to the same network.",
"More concretely, this leads to the following problem: Given a set of tasks, T , and a computational budget b (e.g., maximum allowable inference time), what is the optimal way to assign tasks to networks with combined cost ≤ b such that a combined measure of task performances is maximized?",
"To this end, we develop a computational framework for choosing the best tasks to group together in order to have a small number of separate deep neural networks that completely cover the task set and that maximize task performance under a given computational budget.",
"We make the intriguing Figure 1 : Given five tasks to solve, there are many ways that they can be split into task groups for multitask learning.",
"How do we find the best one?",
"We propose a computational framework that, for instance, suggests the following grouping to achieve the lowest total loss, using a computational budget of 2.5 units: train network A to solve Semantic Segmentation, Depth Estimation, and Surface Normal Prediction; train network B to solve Keypoint Detection, Edge Detection, and Surface Normal Prediction; train network C with a less computationally expensive encoder to solve Surface Normal Prediction alone; including Surface Normals as an output in the first two networks were found advantageous for improving the other outputs, while the best Normals were predicted by the third network.",
"This task grouping outperforms all other feasible ones, including learning all five tasks in one large network or using five dedicated smaller networks.",
"observation that the inclusion of an additional task in a network can potentially improve the accuracy of the other tasks, even though the performance of the added task might be poor.",
"This can be viewed as regularizing or guiding the loss of one task by adding an additional loss, as often employed in curriculum learning or network regularization Bengio et al. (2009) .",
"Achieving this, of course, depends on picking the proper regularizing task -our system can take advantage of this phenomenon, as schematically shown in Figure 1 .",
"This paper has two main contributions.",
"In Section 3, we outline a framework for systematically assigning tasks to networks in order to achieve the best total prediction accuracy with a limited inference-time budget.",
"We then analyze the resulting accuracy and show that selecting the best assignment of tasks to groups is critical for good performance.",
"Secondly, in Section 6, we analyze situations in which multi-task learning helps and when it doesn't, quantify the compatibilities of various task combinations for multi-task learning, compare them to the transfer learning task affinities, and discuss the implications.",
"Moreover, we analyze the factors that influence multi-task affinities.",
"We describe the problem of task compatibility as it pertains to multi-task learning.",
"We provide an algorithm and computational framework for determining which tasks should be trained jointly and which tasks should be trained separately.",
"Our solution can take advantage of situations in which joint training is beneficial to some tasks but not others in the same group.",
"For many use cases, this framework is sufficient, but it can be costly at training time.",
"Hence, we offer two strategies for coping with this issue and evaluate their performance.",
"Our methods outperform single-task networks, a multi-task network with all tasks trained jointly, as well as other baselines.",
"Finally, we use this opportunity to analyze how particular tasks interact in a multi-task setting and compare that with previous results on transfer learning task interactions."
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"We analyze what tasks are best learned together in one network, and which are best to learn separately. "
] |
[
"Search engine has become a fundamental component in various web and mobile applications.",
"Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries.",
"In this paper, we explore a vector space search framework for document retrieval.",
"Specifically, we trained a deep semantic matching model so that each query and document can be encoded as a low dimensional embedding.",
"Our model was trained based on BERT architecture.",
"We deployed a fast k-nearest-neighbor index service for online serving.",
"Both offline and online metrics demonstrate that our method improved retrieval performance and search quality considerably, particularly for tail queries",
"Search engine has been widely applied in plenty of areas on the internet, which receives a query provided by users and returns a list of relevant documents within sub-seconds, helping users obtain their desired information instantaneously.",
"Numerous technologies have been developed and utilized in real-world search engine systems .",
"However, the existing semantic gap between search queries and documents, makes it challenging to retrieve the most relevant documents from tens of millions of documents.",
"Therefore, there is still a large proportion of search requests that can not be satisfied perfectly, especially for long tail queries.A search engine system is usually composed of three main modules, -query understanding module -retrieval module -ranking module The query understanding module first parses the original query string into a structured query object BID32 .",
"More specifically, the query understanding module includes several subtasks, such as word segmentation, query correction, term importance analyze, query expansion, and query rewrite, etc.",
"After the query string was parsed, an index module accepts the parsed query, and then retrieve the candidate documents.We call this stage the retrieval stage or the first round stage.",
"Most web-scale search engine systems use the term inverted index for document retrieval, where term is the most basic unit in the whole retrieval procedure.",
"In the first round stage, the retrieved documents are ranked by a simple relevance model, eg TF-IDF, BM25, and the top-N documents with the highest score are submitted to the next stage for ranking.",
"Finally, the documents scored largest by a ranking function are returned to users eventually.",
"For a search system described above, the final retrieval performance is highly enslaved by these query understanding module.",
"Take word segmentation as an example: this task segments raw continuous query string into a list of segmented terms.",
"Since the word segmentation algorithm has the risk of wrong segmentation.",
"If the error segmented term does not appear in the document space, then no document could be retrieved in the first round stage, and it will return a result page without any document which damages the user's experience seriously.There is a lot of work focused on better understanding queries to retrieve more relevant documents.",
"However, since the final performance is influenced by all parts of the query understanding module.",
"Attempts to optimize only one part is usually hard to contribute to a significant enhancement.",
"To avoid the problems mentioned above, we propose a novel complementary retrieval sys-tem that retrieves documents without the traditional term-based retrieval framework.",
"That is, instead of parse raw query into a structured query, we directly map both queries and documents into a low dimension of embedding.",
"Then in the online serving, the k-nearest-neighbor documents of the given query in the latent embedding space are searched for retrieval.Recently, we have witnessed tremendous successful applications of deep learning techniques in information retrieval circle, like query document relevance matching BID14 BID34 BID33 , query rewriting BID13 , and search result ranking BID12 BID10 .",
"However, it is still hard to directly retrieve relevant documents using an end2end fashion based on knearest-neighbor search in latent space, especially for long tail queries.The latest far-reaching advancement in natural language processing with deep learning, BERT BID8 , provides a turning point to make end2end retrieval realizable.",
"In this paper, we present a document retrieval framework as a supplement to the traditional inverted index based retrieval system.",
"We design a new architecture to retrieve documents without a traditional term-based query understanding pipeline, which avoids performance decay by each subtask of query understanding.",
"We use BERT architecture as the general encoder of query and document strings, then we fine-tuned the pre-trained BERT model with human annotated data and negative sampling technique.",
"Finally, we conduct both offline and online experiments to verify our proposed method.",
"To sum up, our main contributions are described below:1.",
"We design a novel end2end document retrieval framework ，which is a supplement to traditional term-based methods.2.",
"Our model is trained on transformer architecture, and a series of training techniques are developed for performance enhancement.3.",
"The proposed techniques can not only be used in document retrieval but also have a significant improvement for search ranking.The rest of the paper is organized as follows.",
"We concisely review the related work in Section 2.",
"Sections 3 mainly describes our proposed methods.",
"Offline and online experiments are detailed given in Section 4 and Section 5 respectively.",
"Finally, we conclude and discuss future work in Section 6.",
"In this paper, we present an architecture for semantic document retrieval.",
"In this architecture, we first train a deep representation model for query and document embedding, then we build our semantic index using a fast k-nearest-neighbor vector search engine.",
"Both offline and online experiments have shown that retrieval performance is greatly enhanced by our method.",
"For the future work, we would like to explore a more general framework that could use more signals involved for semantic retrievals, like document quality features, recency features, and other text encoding models."
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"A deep semantic framework for textual search engine document retrieval"
] |
[
"Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training.",
"SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful results on standard benchmark tasks such as image classification.",
"In this work, we consider the general setting of SSL problem where the labeled and unlabeled data come from the same underlying probability distribution.",
"We propose a new approach that adopts an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures to provide pseudo-labels for the unlabeled data, which can then be used in conjunction with the initial labeled data to train the CNN model in an SSL manner.",
"We have evaluated and compared our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our SSL algorithm.",
"Recent developments in CNNs have provided promising results for many applications in machine learning and computer vision Krizhevsky et al. (2012) ; Zagoruyko & Komodakis (2016) .",
"However, the success of CNN models requires a vast amount of well-annotated training data, which is not always feasible to perform manually Krizhevsky et al. (2012) .",
"There are essentially two different solutions that are usually used to deal with this problem:",
"1) Transfer Learning (TL) and",
"2) SemiSupervised Learning (SSL).",
"In TL methods Tan et al. (2018) , the learning of a new task is improved by transferring knowledge from a related task which has already been learned.",
"SSL methods Oliver et al. (2018) , however, tend to learn discriminative models that can make use of the information from an input distribution that is given by a large amount of unlabeled data.",
"To make use of unlabeled data, it is presumed that the underlying distribution of data has some structure.",
"SSL algorithms make use of at least one of the following structural assumptions: continuity, cluster, or manifold Chapelle et al. (2009) .",
"In the continuity assumption, data which are close to each other are more likely to belong to the same class.",
"In the cluster assumption, data tends to form discrete clusters, and data in the same cluster are more likely to share the same label.",
"In the manifold assumption, data lies approximately on a manifold of much lower dimension than the input space which can be classified by using distances and densities defined on the manifold.",
"Thus, to define a natural similarity distance or divergence between probability measures on a manifold, it is important to consider the geometrical structures of the metric space in which the manifold exists Bronstein et al. (2017) .",
"There are two principal directions that model geometrical structures underlying the manifold on which the discrete probability measures lie.",
"The first direction is based on the principal of invariance, which relies on the criterion that the geometry between probability measures should be invariant under invertible transformations of random variables.",
"This perspective is the foundation of the theory of information geometry, which operates as a base for the statistical inference Amari (2016) .",
"The second direction is established by the theory of Optimal Transport (OT), which exploits prior geometric knowledge on the base space in which random variables are valued Villani (2008) .",
"Computing OT or Wasserstein distance between two random variables equals to achieving a coupling between these two variables that is optimal in the sense that the expectation of the transportation cost between the first and second variables is minimal.",
"The Wasserstein distance between two probability measures considers the metric properties of the base space on which a structure or a pattern is defined.",
"However, traditional information-theoretic divergences such as the Hellinger divergence and the Kullback-Leibler (KL) divergence are not able to properly capture the geometry of the base space.",
"Thus, the Wasserstein distance is useful for the applications where the structure or geometry of the base space plays a significant role Amari & Nagaoka (2007) .",
"In this work, similar to other SSL methods, we make a structural assumption about the data in which the data are represented by a CNN model.",
"Inspired by the Wasserstein distance, which exploits properly the geometry of the base space to provide a natural notion of similarity between the discrete empirical measures, we use it to provide pseudo-labels for the unlabeled data to train a CNN model in an SSL fashion.",
"Specifically, in our SSL method, labeled data belonging to each class is a discrete measure.",
"Thus, all the labeled data create a measure of measures and similarly, the pool of unlabeled data is also a measure of measures constructed by data belonging to different classes.",
"Thus, we design a measure of measures OT plan serving as a similarity metric between discrete empirical measures to map the unlabeled measures to the labeled measures based on which, the pseudo-labels for the unlabeled data are inferred.",
"Our SSL method is based on the role of Wasserstein distances in the hierarchical modeling Nguyen et al. (2016) .",
"It stems from the fact that the labeled and unlabeled datasets hierarchically create a measure of measures in which each measure is constructed by the data belonging to the same class.",
"Computing the exact Wasserstein distance, however, is computationally expensive and usually is solved by a linear program (Appendix A and D ).",
"Cuturi (2013) introduced an interesting method which relaxes the OT problem using the entropy of the solution as a strong convex regularizer.",
"The entropic regularization provides two main advantageous: 1) The regularized OT problem relies on Sinkhorns algorithm Sinkhorn (1964) that is faster by several orders of magnitude than the exact solution of the linear program.",
"2) In contrast to exact OT, the regularized OT is a differentiable function of their inputs, even when the OT problem is used for discrete measures.",
"These advantages have caused that the regularized OT to receive a lot of attention in machine learning applications such as generating data ; Gulrajani et al. (2017) , designing loss function Frogner et al. (2015) , domain adaptation Damodaran et al. (2018) ; Courty et al. (2017) , clustering Cuturi & Doucet (2014) ; Mi et al. (2018) and low-rank approximation Seguy & Cuturi (2015) .",
"We proposed a new SSL method based on the optimal transportation technique in which unlabeled data masses are transported to a set of labeled data masses, each of which is constructed by data belonging to the same class.",
"In this method, we found a mapping between the labeled and unlabeled masses which was used to infer pseudo-labels for the unlabeled data so that we could use them to train our CNN model.",
"Finally, we experimentally evaluated our SSL method to indicate its potential and effectiveness for leveraging the unlabeled data when labels are limited during the training."
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"We propose a new algorithm based on the optimal transport to train a CNN in an SSL fashion."
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[
"Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks.",
"In many cases it indeed decreases the number of parameter updates required to achieve low training error.",
"However, it also reduces robustness to small adversarial input perturbations and noise by double-digit percentages, as we show on five standard datasets.",
"Furthermore, substituting weight decay for batch norm is sufficient to nullify the relationship between adversarial vulnerability and the input dimension.",
"Our work is consistent with a mean-field analysis that found that batch norm causes exploding gradients.",
"Batch norm is a standard component of modern deep neural networks, and tends to make the training process less sensitive to the choice of hyperparameters in many cases (Ioffe & Szegedy, 2015) .",
"While ease of training is desirable for model developers, an important concern among stakeholders is that of model robustness to plausible, previously unseen inputs during deployment.The adversarial examples phenomenon has exposed unstable predictions across state-of-the-art models (Szegedy et al., 2014) .",
"This has led to a variety of methods that aim to improve robustness, but doing so effectively remains a challenge BID0 Schott et al., 2019; Hendrycks & Dietterich, 2019; Jacobsen et al., 2019) .",
"We believe that a prerequisite to developing methods that increase robustness is an understanding of factors that reduce it.Presented at the ICML 2019 Workshop on Identifying and Understanding Deep Learning Phenomena.",
"Copyright 2019 by the author(s).",
"We found that there is no free lunch with batch norm: the accelerated training properties and occasionally higher clean test accuracy come at the cost of robustness, both to additive noise and for adversarial perturbations.",
"We have shown that there is no inherent relationship between the input dimension and vulnerability.",
"Our results highlight the importance of identifying the disparate mechanisms of regularization techniques, especially when concerned about robustness.Bjorck, N., Gomes, C. P., Selman, B., and Weinberger, K. Q.Understanding"
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"Batch normalization reduces adversarial robustness, as well as general robustness in many cases, particularly to noise corruptions."
] |
[
"This paper presents preliminary ideas of our work for auto- mated learning of Hierarchical Goal Networks in nondeter- ministic domains.",
"We are currently implementing the ideas expressed in this paper.",
"Many domains are amenable to hierarchical problem-solving representations whereby complex problems are represented and solved at different levels of abstraction.",
"Examples include (1) some navigation tasks where hierarchical A* has been shown to be a natural solution solving the navigation problem over different levels of abstraction BID29 BID66 ; (2) dividing a reinforcement learning task into subtasks where policy control is learned for subproblems and combined to form a solution for the overall problem BID9 BID10 BID12 ; (3) abstraction planning, where concrete problems are transformed into abstract problem formulations, these abstract problems are solved as abstract plans, and in turn these abstract plans are refined into concrete solutions BID31 BID4 ; and (4) hierarchical task network (HTN) planning where complex tasks are recursively decomposed into simpler tasks BID8 BID68 BID15 BID43 .",
"These paradigms have in common a divideand-conquer method to problem solving that is amenable to stratified representation of the subproblems.Among the various formalisms, HTN planning has been a recurrent research focus over the years.",
"An HTN planner formulates a plan using actions and HTN methods.",
"The latter describe how and when to reduce complex tasks into simpler subtasks.",
"HTN methods are used to recursively decompose tasks until so-called primitive tasks are reached corresponding to actions that can be performed directly in the world.",
"The HTN planners SHOP and SHOP2 have routinely demonstrated impressive gains in performance (runtime and otherwise) over standard planners.",
"The primary reason for these performance gains is because of the capability of HTN planners to exploit domain-specific knowledge BID67 .",
"HTNs provide a natuCopyright c 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org).",
"All rights reserved.",
"ral knowledge-modeling representation for many domains , including military planning BID38 BID40 , strategy formulation in computer games BID23 BID22 , manufacturing processes BID47 BID61 , project planning BID62 BID63 , story-telling BID6 , web service composition , and UAV planning BID20 Despite these successes, HTN planning suffers from a representational flaw centered around the notion of task.",
"A task is informally defined as a description of an activity to be performed (e.g., find the location of robot r15) (e.g., the task \"dislodge red team from Magan hill\" in some adversarial game) and syntactically represented as a logical atom (e.g., (locate r15)).",
"(e.g., \"(dislodge redteam Magan)\").",
"Beyond this syntax, there is no explicit semantics of what tasks actually mean in HTN representations.",
"HTN planners obviate this issue by requiring that a complete collection of tasks and methods is given, one that decomposes every complex task in every plausible situation.",
"However, the knowledge engineering effort of creating a complete set of tasks and methods can be significant BID17 .",
"Furthermore, researchers have pointed out that the lack of tasks' semantics make using HTNs problematic for execution monitoring problems BID13 BID14 ).",
"Unlike goals, which are conditions that can be evaluated against the current state of the world, tasks have no explicit semantics other than decomposing them using methods.For example, suppose that a team of robots is trying to locate r15 and, using HTN planning, it generates a plan calling for the different robots to ascertain r15's location.",
"While executing the plan generate a complex plan in a gaming task to dislodge red team from Magan hill, the HTN planner might set a complex plan to cutoff access to Magan, surround it, weaken the defenders with artillery fire and then proceed to assault it.",
"If sometime while executing the plan, the opponent abandons the hill, the plan would continue to be executed despite the fact that the task is already achieved.",
"This is due to the lack of task semantics, so their fulfillment cannot be checked against the current state; instead their fulfillment is only guaranteed when the execution of the generated plans is completed.Hierarchical Goal Networks (HGNs) solve these limitations by representing goals (not tasks) at all echelons of the hierarchy BID56 .",
"Hence, goal fulfillment can be directly checked against the current state.",
"In particular, even when a goal g is decomposed into other goals (i.e., in HGN, HGN methods decompose goals into subgoals), the question if the goal is achieved can be answered directly by checking if it is valid in the current state.",
"So in the previous example, when the opponent abandons the hill, an agent executing the plan knows this goal has been achieved regardless of how far it got into executing the said plan.Another advantage of HGNs is that it relaxes the complete domain requirement of HTN planning BID57 ; in HTN planning a complete set of HTN methods for each task is needed to generate plans.",
"Even if the HGN methods are incomplete, it is still possible to generate solution plans by falling back to standard planning techniques such as heuristic planning BID24 to achieve any open goals.",
"Nevertheless, having a collection of well-crafted HGN methods can lead to significant improvement in performance over standard planning techniques BID59 .When",
"the HGN domain is complete (i.e., there is no need to revert to standard planning techniques to solve any problem in the domain), its expressiveness is equivalent to Simple Hierarchical Ordered Planning BID59 , which is the particular variant of HTN planning used by the widely used SHOP and SHOP2 BID45 ) HTN planners. SHOP",
"requires the user to specify a total order of the tasks; SHOP2 drops this requirement allowing partial-order between the tasks BID44 . Both",
"have the same representation capabilities although SHOP2 is usually preferred since it doesn't force the user to provide a total order for the method's subtasks BID44 .In this",
"work, we propose the automated learning of HGNs for ND domains extending our previous work on learning HTNs for deterministic domains BID21 . While work",
"exists on learning goal hierarchies BID53 BID32 BID49 , these works are based on formalisms that have more limited representations than HGNs and in fact predate them."
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"Learning HGNs, ND domains"
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[
"Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.",
"In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve.",
"However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task.",
"We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. ",
"In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. ",
"On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.",
"The ability to learn quickly is a key characteristic that distinguishes human intelligence from its artificial counterpart.",
"Humans effectively utilize prior knowledge and experiences to learn new skills quickly.",
"However, artificial learners trained with traditional supervised-learning or reinforcementlearning methods generally perform poorly when only a small amount of data is available or when they need to adapt to a changing task.",
"Meta-learning seeks to resolve this deficiency by broadening the learner's scope to a distribution of related tasks.",
"Rather than training the learner on a single task (with the goal of generalizing to unseen samples from a similar data distribution) a meta-learner is trained on a distribution of similar tasks, with the goal of learning a strategy that generalizes to related but unseen tasks from a similar task distribution.",
"Traditionally, a successful learner discovers a rule that generalizes across data points, while a successful meta-learner learns an algorithm that generalizes across tasks.",
"Many recently-proposed meta-learning methods demonstrate improved performance at the expense of being hand-designed at either the architectural or algorithmic level.",
"Some have been engineered with a particular application in mind, while others have aspects of a particular high-level strategy already built into them.",
"However, the optimal strategy for an arbitrary range of tasks may not be obvious to the humans designing a meta-learner, in which case the meta-learner should have the flexibility to learn the best way to solve the tasks it is presented with.",
"Such a meta-learner would need to have an expressive, versatile model architecture, in order to learn a range of strategies in a variety of domains.",
"Meta-learning can be formalized as a sequence-to-sequence problem; in existing approaches that adopt this view, the bottleneck is in the meta-learner's ability to internalize and refer to past experience.",
"Thus, we propose a class of model architectures that addresses this shortcoming: we combine temporal convolutions, which enable the meta-learner to aggregate contextual information from past experience, with causal attention, which allow it to pinpoint specific pieces of information within that context.",
"We evaluate this Simple Neural AttenIve Learner (SNAIL) on several heavily-benchmarked meta-learning tasks, including the Omniglot and mini-Imagenet datasets in supervised learning, and multi-armed bandits, tabular Markov Decision processes (MDPs), visual navigation, and continuous control in reinforcement learning.",
"In all domains, SNAIL achieves state-of-the-art performance by significant margins, outperforming methods that are domain-specific or rely on built-in algorithmic priors.",
"We presented a simple and generic class of architectures for meta-learning, motivated by the need for a meta-learner to quickly incorporate and refer to past experience.",
"Our simple neural attentive learner (SNAIL) utilizes a novel combination of temporal convolutions and causal attention, two building blocks of sequence-to-sequence models that have complementary strengths and weaknesses.",
"We demonstrate that SNAIL achieves state-of-the-art performance by significant margins on all of the most-widely benchmarked meta-learning tasks in both supervised and reinforcement learning, without relying on any application-specific architectural components or algorithmic priors.Although we designed SNAIL with meta-learning in mind, it would likely excel at other sequence-tosequence tasks, such as language modeling or translation; we plan to explore this in future work.Another interesting idea would be to train an meta-learner that can attend over its entire lifetime of experience (rather than only a few recent episodes, as in this work).",
"An agent with this lifelong memory could learn faster and generalize better; however, to keep the computational requirements practical, it would also need to learn how to decide what experiences are worth remembering."
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"a simple RNN-based meta-learner that achieves SOTA performance on popular benchmarks"
] |
[
"Knowledge Graph Embedding (KGE) has attracted more attention in recent years.",
"Most of KGE models learn from time-unaware triples.",
"However, the inclusion of temporal information beside triples would further improve the\n",
"performance of a KGE model.",
"In this regard, we propose LiTSE, a temporal KGE model which incorporates time information\n",
"into entity/relation representations by using linear time series decomposition.",
"Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal\n",
"KGs into the space of multi-dimensional Gaussian distributions.",
"The mean of each entity/ relation embedding at a time step shows the current expected position, whereas its covariance (which is stationary over time) represents\n",
"its temporal uncertainty.",
"Experiments show that LiTSE not only achieves the state-of- the-art on link prediction in temporal KGs, but also has the ability to predict the occurrence time of facts with missing time annotations, as well as the existence of future events.",
"To the best of our knowledge, no other model is capable to perform all these tasks.",
"Knowledge Graphs (KGs) are being used for gathering and organizing scattered human knowledge into structured knowledge systems.",
"YAGO (Suchanek et al., 2007) , NELL BID3 , DBpedia BID0 and Freebase BID1 are among existing KGs that have been successfully used in various applications including question answering, assistant systems, information retrieval, etc.",
"In these KGs, knowledge can be represented as RDF triples (s, p ,o) in which s (subject) and o (object) are entities (nodes), and p (predicate) is the relation (edge) between them.KG embedding attempts to learn the representations of entities and relations in high-dimensional latent feature spaces while preserving certain properties of the original graph.",
"Recently, KGE has become a very active research topic due to the wide ranges of downstream applications.",
"Different KGE models have been proposed so far to efficiently learn the representations of KGs and perform KG completion as well as inferencing BID2 BID22 BID23 BID20 BID5 .Most",
"of existing KGE models solely learn from time-unknown facts and ignore the useful temporal information in KGs. In fact",
", there are many time-aware facts (or events) in some temporal KGs. For instance",
", (Obama, wasBornIn, Hawaii) happened at August 4, 1961, and (Obama, presidentOf, USA) was true from 2009 to 2017. These temporal",
"KGs, e.g. ICEWS BID9 , YAGO3 BID11 , store such temporal information either explicitly or implicitly. Traditional KGE",
"models such as TransE learn only from time-unknown facts and consequently cannot distinguish relations with similar semantic meaning. For instance, they",
"often confuse relations such as wasBornIn and diedIn when predicting (person,?,location) .To tackle this problem",
", Temporal KGE models BID4 BID6 BID18 encode time information in their embeddings. Temporal KGE models outperform",
"traditional KGE models on link prediction over temporal KGs. It justifies that incorporation",
"of time information can further improve the performance of a KGE model. Some existing temporal KGE models",
"encode time information in a latent space e.g. representing time as a vector BID4 BID10 . These models cannot capture some",
"prop-erties of time information such as the length of time interval as well as order of two time points. Moreover, some exiting temporal",
"graph embedding models BID18 BID19 consider the changes of entity representations over time as a kind of temporal evolution process, while they ignore the uncertainty during the temporal evolution. We argue that the evolution of",
"entity representations has randomness, because the features of an entity at a certain time are not completely determined by the past information. For example, (Steve Jobs, diedIn",
", California) happened on 2011-10-05. The semantic characteristics of",
"this entity should have a sudden change at this time point. However, due to the incompleteness",
"of knowledge in KGs, this change can not be predicted only according to its past evolutionary trend. Therefore, the representation of Steve",
"Jobs is supposed to include some random components to handle this uncertainty, e.g. a Gaussian noise component.To address the above problems, we propose a new temporal KGE model based on linear time series decomposition (LiTSE) that captures the evolution process of KG representations. LiTSE fits the evolution process of an",
"entity or relation as a linear function of time with a Gaussian random noise. Inspired by , our approach represents",
"each entity and relation as a multi-dimensional Gaussian distribution at each time step to introduce a random component. The mean of an entity/relation representation",
"at a certain time step indicates its current expected position, which is obtained from its initial representation, its evolutionary direction vector which represents the long-term trend of its evolution and the current time. The covariance which describes the temporal uncertainty",
"during its evolution, is denoted as a constant diagonal matrix for computing efficiency. Our contributions are as follows.• Learning the representations",
"for temporal KGs is a relatively",
"unexplored problem because most of existing KGE models only learn from time-unknown facts. We propose LiTSE, a new KGE model to incorporate the time information",
"into the KG representations.• Different from the previous temporal KGE models which use time encoding",
"to incorporate time information, LiTSE fits the evolution process of KG representations as a linear function of time. This enables us to observe and predict the time information directly from",
"entity/relation representations. In particular, we can predict the occurrence of a fact in a future time,",
"according to the known evolution trends of KG representations learned from the past information.• We specially consider the temporal uncertainty during the evolution process",
"of KG representation. Thus, we model each entity as a Gaussian distribution at each time step and use",
"KL-divergence between two Gaussian distributions to compute the scores of facts for optimization.• Beside performing link prediction in temporal KGs, our models are proved to be",
"capable of estimating the occurrence time of a fact with missing time annotation, and predicting future events.The rest of the paper is organized as follows: Section 2 reviews related works. Our model is introduced in the section 3. The proposed model is evaluated and compared",
"with state-of-the-art models in the section",
"4. Finally, the paper is concluded in the last section.",
"We introduce LiTSE, a temporal KGE model that incorporates time information into KG representations by using linear time series decomposition.",
"LiTSE fits the temporal evolution of KG representations over time as linear time series, which enables itself to estimate time information of a triple with the missing time annotation and predict the occurrence of a future event.",
"Considering the uncertainty during the temporal evolution of KG representations, LiTSE maps the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.",
"The covariance of an entity/relation representation represents its randomness component.",
"Experimental results demonstrate that our method significantly outperforms the state-of-the-art methods on link prediction and future event prediction.",
"Besides, our method can effectively predict the occurrence time of a fact.",
"Our work establishes a previously unexplored connection between relational processes and time series analysis with a potential to open a new direction of research on reasoning over time.",
"In the future, we will explore to use other time series analysis techniques to model the temporal evolution of KG representations.",
"Along with considering the temporal uncertainty, another benefit of using time series analysis is to enable the embedding model to encode temporal rules.",
"For instance, given two quadruple (s, p, o, t p ) and (s, q, o, t q ), there exists a temporal constraint t p < t q .",
"Since the time information is represented as a numerical variable in a time series model, it is feasible to incorporate such temporal rules into our models.",
"We will investigate the possibility of encoding temporal rules into our proposed models.",
"DISPLAYFORM0 regularize the covariances for each entity and relation with constraint 6.",
"18.",
"end loop TAB10 shows the statistics of datasets which are anew split for future event prediction, denoted as ICEWS14-F and ICEWS05-15F.",
"As mentioned in Section 4.2, all of the facts in test set occur after the facts in training set and validation set, and the facts of validation set occur after the facts in training set.",
"The time spans of training sets, validation sets and test sets of ICEWS14 and ICEWS05-15 are reported in TAB10 .",
"t e represents the end time of the dataset.",
"For instance, t e of the training set of ICEWS14 is 2014/10/20 and t e of the validation set of ICEWS14 is 2014/11/22, which means the time stamps of quadruples in the validation set of ICEWS14 are between 2014/10/21 and 2014/11/22."
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"Submitted in EMNLP"
] |
[
"We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics.",
"We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation.",
"Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control.",
"In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior.",
"We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.",
"Competitive games have been grand challenges for artificial intelligence research since at least the 1950s BID38 BID45 BID6 .",
"In recent years, a number of breakthroughs in AI have been made in these domains by combining deep reinforcement learning (RL) with self-play, achieving superhuman performance at Go and Poker Moravk et al., 2017) .",
"In continuous control domains, competitive games possess a natural curriculum property, as observed in , where complex behaviors have the potential to emerge in simple environments as a result of competition between agents, rather than due to increasing difficulty of manually designed tasks.",
"Challenging collaborative-competitive multi-agent environments have only recently been addressed using end-to-end RL by BID21 , which learns visually complex first-person 2v2 video games to human level.",
"One longstanding challenge in AI has been robot soccer BID23 , including simulated leagues, which has been tackled with machine learning techniques BID37 BID29 but not yet mastered by end-to-end reinforcement learning.We investigate the emergence of co-operative behaviors through multi-agent competitive games.",
"We design a simple research environment with simulated physics in which complexity arises primarily through competition between teams of learning agents.",
"We introduce a challenging multi-agent soccer environment, using MuJoCo BID46 which embeds soccer in a wider universe of possible environments with consistent simulated physics, already used extensively in the machine learning research community BID16 BID5 BID44 .",
"We focus here on multi-agent interaction by using relatively simple bodies with a 3-dimensional action space (though the environment is scalable to more agents and more complex bodies).",
"1 We use this environment to examine continuous multiagent reinforcement learning and some of its challenges including coordination, use of shaping rewards, exploitability and evaluation.We study a framework for continuous multi-agent RL based on decentralized population-based training (PBT) of independent RL learners BID20 , where individual agents learn off-policy with recurrent memory and decomposed shaping reward channels.",
"In contrast to some recent work where some degree of centralized learning was essential for multi-agent coordinated behaviors (e.g. BID28 BID9 , we demonstrate that end-to-end PBT can lead to emergent cooperative behaviors in our soccer domain.",
"While designing shaping rewards that induce desired cooperative behavior is difficult, PBT provides a mechanism for automatically evolving simple shaping rewards over time, driven directly by competitive match results.",
"We further suggest to decompose reward into separate weighted channels, with individual discount factors and automatically optimize reward weights and corresponding discounts online.",
"We demonstrate that PBT is able to evolve agents' shaping rewards from myopically optimizing dense individual shaping rewards through to focusing relatively more on long-horizon game rewards, i.e. individual agent's rewards automatically align more with the team objective over time.",
"Their behavior correspondingly evolves from random, through simple ball chasing early in the learning process, to more co-operative and strategic behaviors showing awareness of other agents.",
"These behaviors are demonstrated visually and we provide quantitative evidence for coordination using game statistics, analysis of value functions and a new method of analyzing agents' counterfactual policy divergence.Finally, evaluation in competitive multi-agent domains remains largely an open question.",
"Traditionally, multi-agent research in competitive domains relies on handcrafted bots or established human baselines BID21 , but these are often unavailable and difficult to design.",
"In this paper, we highlight that diversity and exploitability of evaluators is an issue, by observing non-transitivities in the agents pairwise rankings using tournaments between trained teams.",
"We apply an evaluation scheme based on Nash averaging BID2 and evaluate our agents based on performance against pre-trained agents in the support set of the Nash average.",
"We have introduced a new 2v2 soccer domain with simulated physics for continuous multi-agent reinforcement learning research, and used competition between agents in this simple domain to train teams of independent RL agents, demonstrating coordinated behavior, including repeated passing motifs.",
"We demonstrated that a framework of distributed population-based-training with continuous control, combined with automatic optimization of shaping reward channels, can learn in this environment end-to-end.",
"We introduced the idea of automatically optimizing separate discount factors for the shaping rewards, to facilitate the transition from myopically optimizing shaping rewards towards alignment with the sparse long-horizon team rewards and corresponding cooperative behavior.",
"We have introduced novel method of counterfactual policy divergence to analyze agent behavior.",
"Our evaluation has highlighted non-transitivities in pairwise match results and the practical need for robustness, which is a topic for future work.",
"Our environment can serve as a platform for multiagent research with continuous physical worlds, and can be easily scaled to more agents and more complex bodies, which we leave for future research.In our soccer environment the reward is invariant over player and we can drop the dependence on i.SVG requires the critic to learn a differentiable Q-function.",
"The true state of the game s and the identity of other agents π \\i , are not revealed during a game and so identities must be inferred from their behavior, for example.",
"Further, as noted in BID10 , off-policy replay is not always fully sound in multi-agent environments since the effective dynamics from any single agent's perspective changes as the other agent's policies change.",
"Because of this, we generally model Q as a function of an agents history of observations -typically keeping a low dimensional summary in the internal state of an LSTM: Q π θ (·, ·; ψ) : X × A → R, where X denotes the space of possible histories or internal memory state, parameterized by a neural network with weights ψ.",
"This enables the Q-function to implicitly condition on other players observed behavior and generalize over the diversity of players in the population and diversity of behaviors in replay, Q is learned using trajectory data stored in an experience replay buffer B, by minimizing the k-step return TD-error with off-policy retrace correction BID33 , using a separate target network for bootstrapping, as is also described in BID13 ; .",
"Specifically we minimize: DISPLAYFORM0 where ξ := ((s t , a t , r t )) i+k t=i is a k-step trajectory snippet, where i denotes the timestep of the first state in the snippet, sampled uniformly from the replay buffer B of prior experience, and Q retrace is the off-policy corrected retrace target: DISPLAYFORM1 where, for stability,Q(·, ·;ψ) : X ×A → R andπ are target network and policies BID30 periodically synced with the online action-value critic and policy (in our experiments we sync after every 100 gradient steps), and c s := min(1, π(as|xs) β(as|xs) ), where β denotes the behavior policy which generated the trajectory snippet ξ sampled from B, and i s=i+1 c s := 1.",
"In our soccer experiments k = 40.",
"Though we use off-policy corrections, the replay buffer has a threshold, to ensure that data is relatively recent.When modelling Q using an LSTM the agent's internal memory state at the first timestep of the snippet is stored in replay, along with the trajectory data.",
"When replaying the experience the LSTM is primed with this stored internal state but then updates its own state during replay of the snippet.",
"LSTMs are optimized using backpropagation through time with unrolls truncated to length 40 in our experiments."
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"We introduce a new MuJoCo soccer environment for continuous multi-agent reinforcement learning research, and show that population-based training of independent reinforcement learners can learn cooperative behaviors"
] |
[
"Program synthesis is the task of automatically generating a program consistent with\n",
"a specification.",
"Recent years have seen proposal of a number of neural approaches\n",
"for program synthesis, many of which adopt a sequence generation paradigm similar\n",
"to neural machine translation, in which sequence-to-sequence models are trained to\n",
"maximize the likelihood of known reference programs.",
"While achieving impressive\n",
"results, this strategy has two key limitations.",
"First, it ignores Program Aliasing: the\n",
"fact that many different programs may satisfy a given specification (especially with\n",
"incomplete specifications such as a few input-output examples).",
"By maximizing\n",
"the likelihood of only a single reference program, it penalizes many semantically\n",
"correct programs, which can adversely affect the synthesizer performance.",
"Second,\n",
"this strategy overlooks the fact that programs have a strict syntax that can be\n",
"efficiently checked.",
"To address the first limitation, we perform reinforcement\n",
"learning on top of a supervised model with an objective that explicitly maximizes\n",
"the likelihood of generating semantically correct programs.",
"For addressing the\n",
"second limitation, we introduce a training procedure that directly maximizes the\n",
"probability of generating syntactically correct programs that fulfill the specification.\n",
"We show that our contributions lead to improved accuracy of the models, especially\n",
"in cases where the training data is limited.",
"The task of program synthesis is to automatically generate a program that is consistent with a specification such as a set of input-output examples, and has been studied since the early days of Artificial Intelligence BID34 .",
"There has been a lot of recent progress made on neural program induction, where novel neural architectures inspired from computation modules such as RAM, stack, CPU, turing machines, and GPU BID10 BID17 BID20 BID11 BID31 BID18 have been proposed to train these architectures in an end-to-end fashion to mimic the behavior of the desired program.",
"While these approaches have achieved impressive results, they do not return explicit interpretable programs, tend not to generalize well on inputs of arbitrary length, and require a lot of examples and computation for learning each program.",
"To mitigate some of these limitations, neural program synthesis approaches BID16 BID28 BID7 have been recently proposed that learn explicit programs in a Domain-specific language (DSL) from as few as five input-output examples.",
"These approaches, instead of using a large number of input-output examples to learn a single program, learn a large number of different programs, each from just a few input-output examples.",
"During training, the correct program is provided as reference, but at test time, the learnt model generates the program from only the input-output examples.While neural program synthesis techniques improve over program induction techniques in certain domains, they suffer from two key limitations.",
"First, these approaches use supervised learning with reference programs and suffer from the problem of Program Aliasing: For a small number of input-output examples, there can be many programs that correctly transform inputs to outputs.",
"The problem is the discrepancy between the single supervised reference program and the multitude of correct programs.",
"FIG0 shows an example of this: if maximizing the probability of ground truth program, predicting Program B would be assigned a high loss even though the two programs are semantically equivalent for the input-output example.",
"Maximum likelihood training forces the model to learn to predict ground truth programs, which is different from the true objective of program synthesis: predicting any consistent program.",
"To address this problem, we alter the optimization objective: instead of maximum likelihood, we use policy gradient reinforcement learning to directly encourage generation of any program that is consistent with the given examples.The second limitation of neural program synthesis techniques based on sequence generation paradigm BID7 ) is that they often overlook the fact that programs have a strict syntax, which can be checked efficiently.",
"Similarly to the work of BID28 , we explore a method for leveraging the syntax of the programming language in order to aggressively prune the exponentially large search space of possible programs.",
"In particular, not all sequences of tokens are valid programs and syntactically incorrect programs can be efficiently ignored both during training and at test time.",
"A syntax checker is an additional form of supervision that may not always be present.To address this limitation, we introduce a neural architecture that retains the benefits of aggressive syntax pruning, even without assuming access to the definition of the grammar made in previous work BID28 .",
"This model is jointly conditioned on syntactic and program correctness, and can implicitly learn the syntax of the language while training.We demonstrate the efficacy of our approach by developing a neural program synthesis system for the Karel programming language BID29 , an educational programming language, consiting of control flow constructs such as loops and conditionals, making it more complex than the domains tackled by previous neural program synthesis works.This paper makes the following key contributions:• We show that Reinforcement Learning can directly optimize for generating any consistent program and improves performance compared to pure supervised learning.•",
"We introduce a method for pruning the space of possible programs using a syntax checker and show that explicit syntax checking helps generate better programs.•",
"In the absence of a syntax checker, we introduce a model that jointly learns syntax and the production of correct programs. We",
"demonstrate this model improves performance in instances with limited training data."
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"Using the DSL grammar and reinforcement learning to improve synthesis of programs with complex control flow."
] |
[
"Time series forecasting plays a crucial role in marketing, finance and many other quantitative fields.",
"A large amount of methodologies has been developed on this topic, including ARIMA, Holt–Winters, etc.",
"However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods.",
"In this paper, we propose a novel state space time series model, with the capability to capture the structure of change points and anomaly points, as well as trend and seasonality.",
"To infer all the hidden variables, we develop a Bayesian framework, which is able to obtain distributions and forecasting intervals for time series forecasting, with provable theoretical properties.",
"For implementation, an iterative algorithm with Markov chain Monte Carlo (MCMC), Kalman filter and Kalman smoothing is proposed.",
"In both synthetic data and real data applications, our methodology yields a better performance in time series forecasting compared with existing methods, along with more accurate change point detection and anomaly detection.",
"Time series forecasting has a rich and luminous history, and is essentially important in most of business operations nowadays.",
"The main aim of time series forecasting is to carefully collect and rigorously study the past observations of a time series to develop an appropriate model which could describe the inherent structure of the series, in order to generate future values.",
"For instance, the internet companies are interested in the number of daily active users (DAU) , say, what is DAU after certain period of time, or when will reach their target DAU goal.Time series forecasting is a fruitful research area with many existing methodologies.",
"The most popular and frequently used time series model might be the Autoregressive Integrated Moving Average (ARIMA) BID4 BID31 BID8 BID16 .",
"Taking seasonality into consideration, BID4 proposed the Seasonal ARIMA.",
"The Holt-Winters method BID30 ) is also very popular by using exponential smoothing.",
"State space model BID10 BID24 BID5 also attracts much attention, which is a linear function of an underlying Markov process plus additive noise.",
"Exponential Smoothing State Space Model (ETS) decomposes times series into error, trend, seasonal that change over time.",
"Recently, deep learning is applied for time-series trend learning using LSTM BID26 , bidirectional dynamic Boltzmann machine BID23 is applied for time-series long-term dependency learning, and coherent probabilistic forecast BID25 ) is proposed for a hierarchy or an aggregation-level comprising a set of time series.",
"Orthogonal to these works, this paper focuses on robust ways of time series forecasting in presence of change points and anomalies.In Internet time series forecasting, Google develops the Bayesian structure time series (BSTS) model BID5 BID24 to capture the trend, seasonality, and similar components of the target series.",
"Recently, Facebook proposes the Prophet approach BID27 based on a decomposable model with interpretable parameters that can be intuitively adjusted by analyst.However, as in the DAU example, some special events like Christmas Holiday or President Election, newly launched apps or features, may cause short period or long-term change of DAU, leading to weird forecasting of those traditional models.",
"The aforementioned special cases are well known as• Anomaly points.",
"The items, events or observations that don't conform to an expected pattern or other items in the dataset, leading to a sudden spike or decrease in the series.•",
"Change points. A",
"market intervention, such as a new product launch or the onset of an advertising (or ad) campaign, may lead to the level change of the original series.Time series forecasting without change/anomaly point detection and adjustment may also lead to bizarre forecasting since these models might learn the abrupt changes in the past. There",
"are literatures on detecting anomaly or change points individually, examples can be found in BID29 ; BID22 ; BID3 ; BID21 BID29 . However",
", the aforementioned change point detection models could not support detection in the presence of seasonality, while the presence of trend/change point is not handled by the anomaly detection models. Most importantly",
", there is a discrepancy between anomaly/change points detection and adjustment, and commonly used manually adjustment might be a bit arbitrary. Unfortunately,",
"the forecasting gap caused by abnormal and change points, to the best of our knowledge, has not been given full attention and no good solution has been found so far. This paper is",
"strongly motivated by bridging this gap.In this paper, to overcome the limitations of the most (if not all) current models that the anomaly points and change points are not properly considered, we develop a state space time series forecasting model in the Bayesian framework that can simultaneously detect anomaly and change points and perform forecasting. The learned structure",
"information related to anomaly and change points is automatically incorporated into the forecasting process, which naturally enhances the model prediction based on the feedback of state-space model. To solve the resultant",
"optimization problem, an iterative algorithm based on Bayesian approximate inference with Markov chain Monte Carlo (MCMC), Kalman filter and Kalman smoothing is proposed. The novel model could",
"explicitly capture the structure of change points, anomaly points, trend and seasonality, as also provide the distributions and forecasting intervals due to Bayesian forecasting framework. Both synthetic and real",
"data sets show the better performance of proposed model, in comparison with existing baseline. Moreover, our proposed",
"model outperforms state-of-the-art models in identifying anomaly and change points.To summarize, our work has the following contributions.• We proposed a robust",
"1 Bayesian state-space time series forecasting model that is able to explicitly capture the structures of change points and anomalies (which are generally ignored in most current models), and therefore automatically adapt for forecasting by incorporating the prior information of trend, seasonality, as well as change points and anomalies using state space modeling. Due to the enhancement",
"of model description capability, the results of model prediction and abnormal and change points detection are mutually improved.• To solve the resultant",
"optimization problem, an effective algorithm based on approximate inference using Markov chain Monte Carlo (MCMC) is proposed with theoretical guaranteed forecasting paths.• Our proposed method outperforms",
"the state-of-the-art methods in time series forecasting in presence of change points and anomalies, and detects change points and anomalies with high accuracy and low false discovery rate on both tasks, outperforming popular change point and anomaly detection methods. Our method is flexible to capture",
"the structure of time series under various scenarios with any component combinations of trend, seasonality, change points and anomalies. Therefore our method can be applied",
"in many settings in practice."
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"We propose a novel state space time series model with the capability to capture the structure of change points and anomaly points, so that it has a better forecasting performance when there exist change points and anomalies in the time series."
] |
[
"The complex world around us is inherently multimodal and sequential (continuous).",
"Information is scattered across different modalities and requires multiple continuous sensors to be captured.",
"As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area.",
"Arguably, modeling arbitrarily distributed spatio-temporal dynamics within and across modalities is the biggest challenge in this research area.",
"In this paper, we present a new transformer model, called the Factorized Multimodal Transformer (FMT) for multimodal sequential learning.",
"FMT inherently models the intramodal and intermodal (involving two or more modalities) dynamics within its multimodal input in a factorized manner.",
"The proposed factorization allows for increasing the number of self-attentions to better model the multimodal phenomena at hand; without encountering difficulties during training (e.g. overfitting) even on relatively low-resource setups.",
"All the attention mechanisms within FMT have a full time-domain receptive field which allows them to asynchronously capture long-range multimodal dynamics.",
"In our experiments we focus on datasets that contain the three commonly studied modalities of language, vision and acoustic.",
"We perform a wide range of experiments, spanning across 3 well-studied datasets and 21 distinct labels.",
"FMT shows superior performance over previously proposed models, setting new state of the art in the studied datasets.",
"In many naturally occurring scenarios, our perception of the world is multimodal.",
"For example, consider multimodal language (face-to-face communication), where modalities of language, vision and acoustic are seamlessly used together for communicative intent (Kottur et al., 2019) .",
"Such scenarios are widespread in everyday life, where continuous sensory perceptions form multimodal sequential data.",
"Each modality within multimodal data exhibits exclusive intramodal dynamics, and presents a unique source of information.",
"Modalities are not fully independent of each other.",
"Relations across two (bimodal) or more (trimodal, . . . ) of them form intermodal dynamics; often asynchronous spatio-temporal dynamics which bind modalities together .",
"Learning from multimodal sequential data has been an active, yet challenging research area within the field of machine learning (Baltrušaitis et al., 2018) .",
"Various approaches relying on graphical models or RNNs have been proposed for multimodal sequential learning.",
"Transformer models are a new class of neural models that rely on a carefully designed non-recurrent architecture for sequential modeling (Vaswani et al., 2017) .",
"Their superior performance is attributed to a self-attention mechanism, which is uniquely capable of highlighting related information across a sequence.",
"This self-attention is a particularly appealing mechanism for multimodal sequential learning, as it can be modified into a strong neural component for finding relations between different modalities (the cornerstone of this paper).",
"In practice, numerous such relations may simultaneously exist within multimodal data, which would require increasing the number of attention units (i.e. heads).",
"Increasing the number of attentions in an efficient and semantically meaningful way inside a transformer model, can boost the performance in modeling multimodal sequential data.",
"In this paper, we present a new transformer model for multimodal sequential learning, called Factorized Multimodal Transformer (FMT) .",
"FMT is capable of modeling asynchronous intramodal and intermodal dynamics in an efficient manner, within one single transformer network.",
"It does so by specifically accounting for possible sets of interactions between modalities (i.e. factorizing based on combinations) in a Factorized Multimodal Self-attention (FMS) unit.",
"We evaluate the performance of FMT on multimodal language: a challenging type of multimodal data which exhibits idiosyncratic and asynchronous spatio-temporal relations across language, vision and acoustic modalities.",
"FMT is compared to previously proposed approaches for multimodal sequential learning over multimodal sentiment analysis (CMU-MOSI) (Zadeh et al., 2016) , multimodal emotion recognition (IEMOCAP) (Busso et al., 2008) , and multimodal personality traits recognition (POM) (Park et al., 2014) .",
"The results of sentiment analysis experiments on CMU-MOSI dataset are presented in Table 1 .",
"FMT achieves superior performance than the previously proposed models for multimodal sentiment analysis.",
"We use two approaches for calculating BA and F1 based on negative vs. non-negative sentiment (Zadeh et al., 2018b) on the left side of /, and negative vs. positive (Tsai et al., 2019) on the right side.",
"MAE and Corr are also reported.",
"For multimodal emotion recognition, experiments on IEMOCAP are reported in Table 2 .",
"The performance of FMT is superior than other baselines for multimodal emotion recognition (with the exception of Happy emotion).",
"The results of experiments for personality traits recognition on POM dataset are reported in Table 3 .",
"We report MA5 and MA7, depending on the label.",
"FMT outperforms baselines across all personality traits.",
"We study the importance of the factorization in FMT.",
"We first remove the unimodal, bimodal and trimodal attentions from the FMT model, resulting in 3 alternative implementations of FMT.",
"demonstrates the results of this ablation experiment over CMU-MOSI dataset.",
"Furthermore, we use only one modality as input for FMT, to understand the importance of each modality (all other factors removed).",
"We also replace the summarization networks with simple vector addition operation.",
"All factors, modalities, and summarization components are needed for achieving best performance.",
"We also perform experiments to understand the effect of number of FMT units within each MTL.",
"Table 5 shows the performance trend for different number of FMT units.",
"The model with 6 number of FMS (42 attentions in total) achieves the highest performance (6 is also the highest number we experimented with).",
"Tsai et al. (2019) reports the best performance for CMU-MOSI dataset is achieved when using 40 attentions per cross-modal transformer (3 of each, therefore 120 attention, without counting the subsequent unimodal transformers).",
"FMT uses fewer number of attentions than MulT, yet achieves better performance.",
"We also experiment with number of heads for original transformer model (Vaswani et al., 2017) and compare to FMT (Appendix A.3).",
"In this paper, we presented the Factorized Multimodal Transformer (FMT) model for multimodal sequential learning.",
"Using a Factorized Multimodal Self-attention (FMS) within each Multimodal Transformer Layer (MTL), FMT is able to model the intra-model and inter-modal dynamics within asynchronous multimodal sequences.",
"We compared the performance of FMT to baselines approaches over 3 publicly available datasets for multimodal sentiment analysis (CMU-MOSI, 1 label), emotion recognition (IEMOCAP, 4 labels) and personality traits recognition (POM, 16 labels).",
"Overall, FMT achieved superior performance than previously proposed models across the studied datasets.",
"A APPENDIX"
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] | BJxD11HFDS | true | [
"A multimodal transformer for multimodal sequential learning, with strong empirical results on multimodal language metrics such as multimodal sentiment analysis, emotion recognition and personality traits recognition. "
] |
[
"We develop a novel and efficient algorithm for optimizing neural networks inspired by a recently proposed geodesic optimization algorithm.",
"Our algorithm, which we call Stochastic Geodesic Optimization (SGeO), utilizes an adaptive coefficient on top of Polyak's Heavy Ball method effectively controlling the amount of weight put on the previous update to the parameters based on the change of direction in the optimization path.",
"Experimental results on strongly convex functions with Lipschitz gradients and deep Autoencoder benchmarks show that SGeO reaches lower errors than established first-order methods and competes well with lower or similar errors to a recent second-order method called K-FAC (Kronecker-Factored Approximate Curvature).",
"We also incorporate Nesterov style lookahead gradient into our algorithm (SGeO-N) and observe notable improvements.",
"First order methods such as Stochastic Gradient Descent (SGD) with Momentum (Sutskever et al., 2013) and their variants are the methods of choice for optimizing neural networks.",
"While there has been extensive work on developing second-order methods such as Hessian-Free optimization (Martens, 2010) and Natural Gradients (Amari, 1998; Martens & Grosse, 2015) , they have not been successful in replacing them due to their large per-iteration costs, in particular, time and memory.",
"Although Nesterov's accelerated gradient and its modifications have been very effective in deep neural network optimization (Sutskever et al., 2013) , some research have shown that Nesterov's method might perform suboptimal for strongly convex functions (Aujol et al., 2018) without looking at local geometry of the objective function.",
"Further, in order to get the best of both worlds, search for optimization methods which combine the the efficiency of first-order methods and the effectiveness of second-order updates is still underway.",
"In this work, we introduce an adaptive coefficient for the momentum term in the Heavy Ball method as an effort to combine first-order and second-order methods.",
"We call our algorithm Geodesic Optimization (GeO) and Stochastic Geodesic Optimization (SGeO) (for the stochastic case) since it is inspired by a geodesic optimization algorithm proposed recently (Fok et al., 2017) .",
"The adaptive coefficient effectively weights the momentum term based on the change in direction on the loss surface in the optimization process.",
"The change in direction can contribute as implicit local curvature information without resorting to the expensive second-order information such as the Hessian or the Fisher Information Matrix.",
"Our experiments show the effectiveness of the adaptive coefficient on both strongly-convex functions with Lipschitz gradients and general non-convex problems, in our case, deep Autoencoders.",
"GeO can speed up the convergence process significantly in convex problems and SGeO can deal with illconditioned curvature such as local minima effectively as shown in our deep autoencoder benchmark experiments.",
"SGeO has similar time-efficiency as first-order methods (e.g. Heavy Ball, Nesterov) while reaching lower reconstruction error.",
"Compared to second-order methods (e.g., K-FAC), SGeO has better or similar reconstruction errors while consuming less memory.",
"The structure of the paper is as follows: In section 2, we give a brief background on the original geodesic and contour optimization introduced in Fok et al. (2017) , neural network optimization methods and the conjugate gradient method.",
"In section 3, we introduce our adaptive coefficient specifically designed for strongly-convex problems and then modify it for general non-convex cases.",
"In section 4, we discuss some of the related work in the literature.",
"Section 5 illustrates the algorithm's performance on convex and non-convex benchmarks.",
"More details and insights regarding the algorithm and the experiments can be found in the Appendix.",
"We proposed a novel and efficient algorithm based on adaptive coefficients for the Heavy Ball method inspired by a geodesic optimization algorithm.",
"We compared SGeO against SGD with Nesterov's Momentum and regular momentum (Heavy Ball) and a recently proposed second-order method, K-FAC, on three deep autoencoder optimization benchmarks and three strongly convex functions with Lipschitz gradients.",
"We saw that SGeO is able to outperform all first-order methods that we compared to, by a notable margin.",
"SGeO is easy to implement and the computational overhead it has over the first-order methods, which is calculating the dot product, is marginal.",
"It can also perform as effectively as or better than second-order methods (here, K-FAC) without the need for expensive higher-order operations in terms of time and memory.",
"We believe that SGeO opens new and promising directions in high dimensional optimization research and in particular, neural network optimization.",
"We are working on applying SGeO to other machine learning paradigms such as CNNs, RNNs and Reinforcement Learning.",
"It remains to analyse the theoretical properties of SGeO such as its convergence rate in convex and non-convex cases which we leave for future work."
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"We utilize an adaptive coefficient on top of regular momentum inspired by geodesic optimization which significantly speeds up training in both convex and non-convex functions."
] |
[
"We introduce the masked translation model (MTM) which combines encoding and decoding of sequences within the same model component.",
"The MTM is based on the idea of masked language modeling and supports both autoregressive and non-autoregressive decoding strategies by simply changing the order of masking.",
"In experiments on the WMT 2016 Romanian-English task, the MTM shows strong constant-time translation performance, beating all related approaches with comparable complexity.",
"We also extensively compare various decoding strategies supported by the MTM, as well as several length modeling techniques and training settings.",
"Neural machine translation (NMT) has been developed under the encoder-decoder framework (Sutskever et al., 2014) with an intermediary attention mechanism (Bahdanau et al., 2015) .",
"The encoder learns contextualized representations of source tokens, which are used by the decoder to predict target tokens.",
"These two components have individual roles in the translation process, and they are connected via an encoder-decoder attention layer (Bahdanau et al., 2015) .",
"Many advances in NMT modeling are based on changes in the internal layer structure (Gehring et al., 2017; Wang et al., 2017; Vaswani et al., 2017; Dehghani et al., 2019; , tweaking the connection between the layers (Zhou et al., 2016; Shen et al., 2018; Bahar et al., 2018; Li et al., 2019a) , or appending extra components or latent variables (Gu et al., 2016; Zhang et al., 2016; Shah & Barber, 2018; ) -all increasing the overall architectural complexity of the model while keeping the encoder and decoder separated.",
"Our goal is to simplify the general architecture of machine translation models.",
"For this purpose, we propose the masked translation model (MTM) -a unified model which fulfills the role of both the encoder and decoder within a single component.",
"The MTM gets rid of the conventional decoder as well as the encoder-decoder attention mechanism.",
"Its architecture is only a sequence encoder with self-attention layers, trained with an objective function similar to masked language modeling (Devlin et al., 2019) .",
"In order to model the translation problem, the MTM is given the concatenation of the source and target side from a parallel sentence pair.",
"This approach is similar to the translation language model presented by Lample & Conneau (2019) , but focuses on the target side, i.e. the masking is applied to some selected positions in the target sentence.",
"The MTM is trained to predict the masked target words relying on self-attention layers which consider both the source sentence and a masked version of the target sentence.",
"Trained in this way, the model is perfectly suitable for non-autoregressive decoding since the model learned to predict every position in parallel, removing the dependency on decisions at preceding target positions.",
"Within its extremely simple architecture, one can realize various decoding strategies, e.g., using left-to-right, non-autoregressive, or iterative decoding by merely adjusting the masking schemes in search.",
"We present a unified formulation of the MTM for different decoding concepts by factorizing the model probability over a set of masked positions.",
"The MTM has several advantages over the conventional encoder-decoder framework:",
"• A simpler architecture",
"In this work we simplify the existing Transformer architecture by combining the traditional encoder and decoder elements into a single component.",
"The resulting masked translation model is trained by concatenating source and target and applying BERT-style masking to the target sentence.",
"The novel training strategy introduced with the MTM requires a rethinking of the search process and allows for various new decoding strategies to be applied in the theoretical framework we developed in this work.",
"A detailed comparison shows that unmasking the sequence one-by-one gives the overall best performance, be it left-to-right, right-to-left, or confidence-based.",
"Unveiling a constant number of tokens based on confidence in each decoding step, however, can achieve reasonable performance with a fixed, much smaller number of iterations.",
"We show that there is a potential of at least 1.5 % BLEU improvement that can be achieved by more elaborate length models, which yields itself as a good start for further research.",
"Furthermore, we plan to extend the decoding strategies to work with beam search and verify our observations on further language pairs."
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"We use a transformer encoder to do translation by training it in the style of a masked translation model."
] |
[
"We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model.",
"We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class.",
"Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class.",
"We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity.",
"Experimentally, we show that our method is capable of detecting when a pre-trained model is extrapolating on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.",
"As machine learning is deployed in increasingly vital areas, there is increasing demand for metrics that draw attention to potentially unreliable predictions.",
"One important source of unreliability is extrapolation.",
"Extrapolation can be formalized in a number of ways: it can refer to making predictions on inputs outside the support of the training data, making predictions with high Bayesian or Frequentist uncertainty, or making predictions that depend strongly on arbitrary choices outside of the learning problem specification (e.g., a random seed).",
"In this paper, we develop a method for detecting this last form of extrapolation.",
"Specifically, we say that a trained model is extrapolating on a test input if the prediction at this input is underdetermined -meaning that many different predictions are all equally consistent with the constraints posed by the training data and the learning problem specification (i.e., the model architecture and the loss function).",
"Underdetermination is just one form of extrapolation, but it is particularly relevant in the context of overparameterized model classes (e.g. deep neural networks).",
"Recently, simple (but computationally expensive) ensembling methods (Lakshminarayanan et al., 2017) , which train many models on the same data from different random seeds, have proven highly effective at uncertainty quantification tasks (Ovadia et al., 2019) .",
"This suggests that underdetermination is a key threat to reliability in deep learning, and motivates flexible methods that can detect underdetermined predictions cheaply.",
"With this motivation, we present local ensembles, a post-hoc method for measuring the extent to which a pre-trained model's prediction is underdetermined for a particular test input.",
"Given a trained model, our method returns an extrapolation score that measures the variability of test predictions across a local ensemble, i.e. a set of local perturbations of the trained model parameters that fit the training data equally well.",
"Local ensembles are a computationally cheap, post-hoc alternative to fully trained ensembles, and do not require special training procedures of approximate ensembling methods that measure related, but distinct, notions of uncertainty (Gal & Ghahramani, 2015; Blundell et al., 2015) .",
"Local ensembles also address a gap in approximate methods for estimating prediction uncertainty.",
"Specifically, whereas exact Bayesian or Frequentist uncertainty includes underdetermination as one component, approximate methods such as Laplace approximations (MacKay, 1992) or influence function-based methods (Schulam & Saria, 2019) break down when underdetermination is present.",
"In contrast, our method leverages the pathology that makes these methods struggle (an ill-conditioned Hessian).",
"Our contributions in this paper are as follows:",
"• We present local ensembles, a test-time method for detecting underdetermination-based extrapolation in overparameterized models.",
"• We demonstrate theoretically that our method approximates the variance of a trained ensemble with local second-order information.",
"• We give a practical method for tractably approximating this quantity, which is simpler and cheaper than alternative second-order reliability methods.",
"• Through experiments aimed at testing underdetermination, we show our method approximates the behavior of trained ensembles, and can detect extrapolation in a range of scenarios.",
"We present local ensembles, a post-hoc method for detecting extrapolation due to underdetermination in a trained model.",
"Our method uses local second-order information to approximate the variance of an ensemble.",
"We give a tractable implementation using the Lanczos iteration to estimate the largest eigenvectors of the Hessian, and demonstrate its practical flexibility and utility.",
"Although this method is not a full replacement for ensemble methods, which can characterize more complexity (e.g. multiple modes), we believe it fills an important role in isolating one component of prediction unreliability.",
"In future work, we hope to scale these methods to larger models and to further explore the properties of different stopping points m.",
"We also hope to explore applications in fairness and interpretability, where understanding model and training bias is of paramount importance.",
"Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson.",
"Estimating the support of a high-dimensional distribution.",
"Neural computation, 13 (7)"
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"We present local ensembles, a method for detecting extrapolation in trained models, which approximates the variance of an ensemble using local-second order information."
] |
[
"Coalition operations are essential for responding to the increasing number of world-wide incidents that require large-scale humanitarian assistance.",
"Many nations and non-governmental organizations regularly coordinate to address such problems but their cooperation is often impeded by limits on what information they are able to share.",
"In this paper, we consider the use of an advanced cryptographic technique called secure multi-party computation to enable coalition members to achieve joint objectives while still meeting privacy requirements",
". Our particular focus is on a multi-nation aid delivery scheduling task that involves coordinating when and where various aid provider nations will deliver relief materials after the occurrence of a natural disaster",
". Even with the use of secure multi-party computation technology, information about private data can leak",
". We describe how the emerging field of quantitative information flow can be used to help data owners understand the extent to which private data might become vulnerable as the result of possible or actual scheduling operations, and to enable automated adjustments of the scheduling process to ensure privacy requirements",
"Coalition operations are becoming an increasing focus for many nations.",
"The need for collaboration derives from increasing awareness of mutual interests among both allies and nations that traditionally have not worked closely together.",
"For example, coalition operations for Humanitarian Assistance and Disaster Relief (HADR) have increased substantially in recent years in both numbers and scope.",
"With the impact of global warming, it is anticipated that there will be even more large-scale humanitarian crises resulting from adverse weather-related events and sea-level rises.",
"These coalitions often involve not just government and military organizations but also non-governmental organizations (NGOs) and commercial entities.A key challenge facing coalitions is how to collaborate without releasing information that could jeopardize national (or organizational) interests.",
"Information security mechanisms for the military to date have focused on safeguarding information by limiting its access and use.",
"This approach has lead to a significant undersharing problem, which impedes Distribution Statement A: Approved for Public Release; Distribution is Unlimited.effective joint operations.",
"Recent work on defining access control mechanisms is useful for enabling selective sharing of information that is safe to release BID4 .",
"However, many coalition tasks require information that participants do not wish to make available to others.For example, consider the problem of scheduling the delivery of aid (food, water, medicine, fuel) to impacted nations after a natural disaster.",
"Historically, international response has involved dozens of countries and NGOs, each with the desire to contribute in interconnected and potentially conflicting ways.",
"Ideally coordination would be achieved by directly sharing information about the amount of aid each contributor has available or can produce, where that aid is situated, the position and storage capacity of ships for delivering the aid, harbor facilities where aid ships could dock, etc.",
"But the owners of this data may be unwilling to directly share it with coalition partners, for fear of revealing information that impacts national security (e.g., ship locations) or competitive advantages (e.g., a company's backlog of inventory).To",
"address this problem of coalition collaboration without revealing private information, we exploit a cryptographic technology called secure multi-party computation (MPC) BID6 . MPC",
"protocols enable mutually distrusting parties to perform joint computations on private information while it remains encrypted. In",
"our work, we use MPC to enable privacy-preserving computations over several types of coordination tasks related to scheduling aid delivery.While participants cannot directly learn others' private inputs from the process/protocol of a secure multi-party computation, they may be able to infer something about private data based on the results of the computation. For",
"this reason, our privacy-aware scheduling solution makes use of a complementary technology called quantitative information flow (QIF) to measure the degree to which private data used in the scheduling task might leak to other participants. Insights",
"gained from this analysis are folded back into the scheduling process via an adaptive workflow capability, to ensure that the vulnerability of private data stays within acceptable thresholds.The paper is organized as follows. We begin",
"by summarizing our aid distribution task, including a description of the core scheduling problem and the data (private and non-private) belonging to the various coalition members. We then",
"provide a short overview of secure multi-party computation followed by a description of how we employ it within a broader adaptive workflow framework to address the aid delivery scheduling task. Next, we",
"describe our use of quantitative information flow to assess the vulnerability of private information as the scheduling progresses and to adapt the scheduling workflow in light of those assessments to ensure adherence to predefined privacy requirements. We conclude",
"by identifying directions for future work and summarizing our contributions.",
"A key challenge facing coalitions is how to collaborate without releasing information that could jeopardize national (or organizational) interests.",
"In this paper, we consider this challenge for a realistic scheduling problem tied to aid delivery.",
"Our work makes several contributions.",
"First, we show how state-of-the-art secure multi-party computation can be used to safeguard private information with an overall distributed scheduling solution to the aid delivery problem.",
"A second contribution relates to the use of quantitative information flow (QIF): even with secure multi-party computation, scheduling outputs can reveal information about coalition members' private data.",
"We show how QIF can be applied to assess the vulnerability of providate data for both prospective (i.e., where results are not known) and actual (i.e., where results are known) computations.",
"As a third contribution, these assessments can be used to adapt the scheduling algorithm to ensure it remains within accepted vulnerability thresholds established by data owners."
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"Privacy can be thought about in the same way as other resources in planning"
] |
[
"The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition.",
"Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence between the representation and modifying the regularization term in the variational lower bound.",
"However recent work by Locatello et al. (2018) has demonstrated that the proposed methods are heavily influenced by randomness and the choice of the hyper-parameter.",
"This work is built upon the same framework in Stage 1 (Li et al., 2019), but with different settings; to make it self-contained, we provide this manuscript, which is unavoidably very similar to the report for Stage 1.",
"In detail, in this work, instead of designing a new regularization term, we adopt the FactorVAE but improve the reconstruction performance and increase the capacity of network and the training step.",
"The strategy turns out to be very effective in achieving disentanglement.",
"The great success of unsupervised learning heavily depends on the representation of the feature in the real-world.",
"It is widely believed that the real-world data is generated by a few explanatory factors which are distributed, invariant, and disentangled (Bengio et al., 2013) .",
"The challenge of learning disentangled representation boils down into a competition 1 to build the best disentangled model.",
"The key idea in disentangled representation is that the perfect representation should be a one-to-one mapping to the ground truth disentangled factor.",
"Thus, if one factor changed and other factors fixed, then the representation of the fixed factor should be fixed accordingly, while others' representation changed.",
"As a result, it is essential to find representations that",
"(i) are independent of each other, and",
"(ii) align to the ground truth factor.",
"Recent line of works in disentanglement representation learning are commonly focused on enforcing the independence of the representation by modifying the regulation term in the (Kumar et al., 2018) and FactorVAE (Kim and Mnih, 2018) .",
"See Appendix A for more details of these model.",
"To evaluate the performance of disentanglement, several metrics have been proposed, including the FactorVAE metric (Kim and Mnih, 2018) , Mutual Information Gap (MIG) (Chen et al., 2018) , DCI metric (Eastwood and Williams, 2018) , IRS metric (Suter et al., 2019) , and SAP score (Kumar et al., 2018) .",
"However, one of our findings is that these methods are heavily influenced by randomness and the choice of the hyper-parameter.",
"This phenomenon was also discovered by Locatello et al. (2018) .",
"Therefore, rather than designing a new regularization term, we simply use FactorVAE but at the same time improve the reconstruction performance.",
"We believe that, the better the reconstruction, the better the alignment of the ground-truth factors.",
"Therefore, the more capacity of the encoder and decoder network, the better the result would be.",
"Furthermore, after increasing the capacity, we also try to increase the training step which also shows a significant improvement of evaluation metrics.",
"The final architecture of FactorVAE is given in Figure 1 .",
"Note that, this work is built upon the same framework in stage 1 (Li et al., 2019) , but with different settings; to make it self-contained, we provide this manuscript, which is unavoidably very similar to the report for Stage 1.",
"Overall, our contribution can be summarized as follow: (1) we found that the performance of the reconstruction is also essential for learning disentangled representation, and (2) we achieve state-of-the-art performance in the competition.",
"In this work, we conducted an empirical study on disentangled learning.",
"We first conduct several experiments with different disentangle learning methods and select the FactorVAE as the base model; and second we improve the performance of the reconstruction, by increasing the capacity of the model and the training step.",
"Finally, our results appear to be competitive.",
"(VAE) (Kingma and Welling, 2013 ), a generative model that maximize the following evidence lower bound to approximate the intractable distribution p θ (x|z) using q φ (z|x),",
"where q φ (z|x) denote Encoder with parameter φ and p θ (x|z) denote Decoder with parameter θ.",
"As shown in Table 4 , all the lower bound of variant VAEs can be described as Reconstruction Loss+ Regularization where all the Regularization term and the hyper-parameters are given in this table."
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"disentangled representation learning"
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"We propose a generative adversarial training approach for the problem of clarification question generation.",
"Our approach generates clarification questions with the goal of eliciting new information that would make the given context more complete.",
"We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question.",
"We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.\n",
"A goal of natural language processing is to develop techniques that enable machines to process naturally occurring language.",
"However, not all language is clear and, as humans, we may not always understand each other BID10 ; in cases of gaps or mismatches in knowledge, we tend to ask questions BID9 .",
"In this work, we focus on the task of automatically generating clarification questions: questions that ask for information that is missing from a given linguistic context.",
"Our clarification question generation model builds on the sequence-to-sequence approach that has proven effective for several language generation tasks BID37 BID39 BID5 .",
"Unfortunately, training a sequence-to-sequence model directly on context/question pairs yields generated questions that are highly generic 1 , corroborating a common finding in dialog systems BID17 .",
"Our goal is to be able to generate questions that are useful and specific.To achieve this, we begin with a recent observation of BID30 , who considered the task of question reranking: the system should learn to generate clarification questions whose answers have high utility, which they defined as the likelihood that this question would lead to an answer that will make the context more complete ( §2.3).",
"Inspired by this, we construct a question generation model that first generates a question given a context, and then generates a hypothetical answer to that question.",
"Given this (context, question, answer) tuple, we train a utility calculator to estimate the usefulness of this question.",
"We then show that this utility calculator can be generalized using ideas for generative adversarial networks BID8 for text BID40 , wherein the utility predictor plays the role of the \"discriminator\" and the question generator is the \"generator\" ( §2.2), which we train using the MIXER algorithm BID29 .We",
"evaluate our approach on two question generation datasets: for posts on Stack Exchange and for Amazon product descriptions (Figure 1 ). Using",
"both automatic metrics and human evaluation, we demonstrate that our adversarially trained model generates a more diverse set of questions than all the baseline models. Furthermore",
", we find that although all models generate questions that are relevant to the context at hand, our adversarially-trained model generates questions that are more specific to the context.",
"In this work, we describe a novel approach to the problem of clarification question generation.",
"Given a context, we use the observation of BID30 that the usefulness of a clarification question can be measured by the value of updating the context with an answer to the question.",
"We use a sequence-to-sequence model to generate a question given a context and a second sequenceto-sequence model to generate an answer given the context and the question.",
"Given the (context, predicted question, predicted answer) triple we calculator the utility of this triple and use it as a reward to retrain the question generator using reinforcement learning based MIXER model.",
"Further, to improve upon the utility function, we reinterpret it as a discriminator in an adversarial setting and train both the utility function and the MIXER model in a minimax fashion.",
"We find that our adversarial training approach produces more diverse questions compared to both a model trained using maximum likelihood objective and a model trained using utility reward based reinforcement learning.",
"There are several avenues of future work in this area.",
"Following BID24 , we could combine text input with image input to generate more relevant questions.",
"Because some questions can be answered by looking at the product image in the Amazon dataset BID22 , this could help generate more relevant and useful questions.",
"As in most One significant research challenge in the space of free text generation problems when the set of possible outputs is large, is that of automatic evaluation BID20 : in our results we saw some correlation between human judgments and automatic metrics, but not enough to trust the automatic metrics completely.",
"Lastly, integrating such a question generation model into a real world platform like StackExchange or Amazon to understand the real utility of such models and to unearth additional research questions."
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"We propose an adversarial training approach to the problem of clarification question generation which uses the answer to the question to model the reward. "
] |
[
"Pattern databases are the foundation of some of the strongest admissible heuristics for optimal classical planning.",
"Experiments showed that the most informative way of combining information from multiple pattern databases is to use saturated cost partitioning.",
"Previous work selected patterns and computed saturated cost partitionings over the resulting pattern database heuristics in two separate steps.",
"We introduce a new method that uses saturated cost partitioning to select patterns and show that it outperforms all existing pattern selection algorithms.",
"A * search BID10 with an admissible heuristic BID23 ) is one of the most successful methods for solving classical planning tasks optimally.",
"An important building block of some of the strongest admissible heuristics are pattern database (PDB) heuristics.",
"A PDB heuristic precomputes all goal distances in a simplified state space obtained by projecting the task to a subset of state variables, the pattern, and uses these distances as lower bounds on the true goal distances.",
"PDB heuristics were originally introduced for solving the 15-puzzle BID2 and have later been generalized to many other combinatorial search tasks (e.g., BID21 BID7 and to the setting of domainindependent planning BID3 .Using a single PDB heuristic of reasonable size is usually not enough to cover sufficiently many aspects of challenging planning tasks. It is therefore often beneficial to compute multiple PDB heuristics and to combine their estimates admissibly BID15 . The simplest approach for this is to choose the PDB with the highest estimate in each state. Instead of this maximization scheme, we would like to sum estimates, but this renders the resulting heuristic inadmissible in general. However, if two PDBs are affected by disjoint sets of operators, they are independent and we can admissibly add their estimates BID19 BID7 . BID11 later generalized this idea by introducing the canonical heuristic for PDBs, which computes all maximal subsets of pairwise independent PDBs and then uses the maximum over the sums of independent PDBs as the heuristic value.Cost partitioning BID17 BID40 ) is a generalization of the independence-based methods above.",
"It makes the sum of heuristic estimates admissible by distributing the costs of each operator among the heuristics.",
"The literature contains many different cost partitioning algorithms such as zero-one cost partitioning BID4 BID11 ), uniform cost partitioning BID17 , optimal cost partitioning BID17 BID16 BID18 BID25 , posthoc optimization BID26 and delta cost partitioning BID6 .In",
"previous work BID34 , we showed experimentally for the benchmark tasks from previous International Planning Competitions (IPC) that saturated cost partitioning (SCP) BID30 BID37 is the cost partitioning algorithm of choice for PDB heuristics. Saturated",
"cost partitioning considers an ordered sequence of heuristics. Iteratively",
", it gives each heuristic the minimum amount of costs that the heuristic needs to justify all its estimates and then uses the remaining costs for subsequent heuristics until all heuristics have been served this way.Before we can compute a saturated cost partitioning over pattern database heuristics, we need to select a collection of patterns. The first",
"domain-independent automated pattern selection algorithm is due to BID3 . It partitions",
"the state variables into patterns via best-fit bin packing. BID5 later used",
"a genetic algorithm to search for a pattern collection that maximizes the average heuristic value of a zero-one cost partitioning over the PDB heuristics. BID11 proposed",
"an algorithm that performs a hill-climbing search in the space of pattern collections (HC). HC evaluates a",
"collection C by estimating the search effort of the canonical heuristic over C based on a model of IDA * runtime BID20 . BID8 presented",
"the Complementary PDBs Creation (CPC) method, that combines bin packing and genetic algorithms to create a pattern collection minimizing the estimated search effort of an A * search BID22 . BID28 repeatedly",
"compute patterns using counterexample-guided abstraction refinement (CEGAR): starting from a random goal variable, their CEGAR algorithm iteratively finds solutions in the corresponding projection and executes them in the original state space. Whenever a solution",
"cannot be executed due to a violated precondition, it adds the missing precondition variable to the pattern.Finally, BID26 systematically generate all interesting patterns up to a given size X (SYS-X). Experiments showed",
"that cost-partitioned heuristics over SYS-2 and SYS-3 yield accurate estimates BID26 BID34 , but using all interesting patterns of larger sizes is usually infeasible.We introduce SYS-SCP, a new pattern selection algorithm based on saturated cost partitioning that potentially considers all interesting patterns, but only selects useful ones. SYS-SCP builds multiple",
"pattern sequences that together form the resulting pattern collection. For each sequence σ, it",
"considers the interesting patterns in increasing order by size and adds a pattern P to σ if P is not part of an earlier sequence and the saturated cost partitioning heuristic over σ plus P is more informative than the one over σ alone.",
"We introduced a new pattern selection algorithm based on saturated cost partitioning and showed that it outperforms Table 6 : Number of tasks solved by different planners."
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"Using saturated cost partitioning to select patterns is preferable to all existing pattern selection algorithms."
] |
[
"State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion.",
"Vaswani et.",
"al. (2017) propose a new architecture that avoids recurrence and convolution completely.",
"Instead, it uses only self-attention and feed-forward layers.",
"While the proposed architecture achieves state-of-the-art results on several machine translation tasks, it requires a large number of parameters and training iterations to converge.",
"We propose Weighted Transformer, a Transformer with modified attention layers, that not only outperforms the baseline network in BLEU score but also converges 15-40% faster.",
"Specifically, we replace the multi-head attention by multiple self-attention branches that the model learns to combine during the training process.",
"Our model improves the state-of-the-art performance by 0.5 BLEU points on the WMT 2014 English-to-German translation task and by 0.4 on the English-to-French translation task.",
"Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs) BID12 , form an important building block for many tasks that require modeling of sequential data.",
"RNNs have been successfully employed for several such tasks including language modeling BID23 BID24 BID25 , speech recognition BID9 BID19 , and machine translation BID34 .",
"RNNs make output predictions at each time step by computing a hidden state vector h t based on the current input token and the previous states.",
"This sequential computation underlies their ability to map arbitrary input-output sequence pairs.",
"However, because of their auto-regressive property of requiring previous hidden states to be computed before the current time step, they cannot benefit from parallelization.Variants of recurrent networks that use strided convolutions eschew the traditional time-step based computation BID15 BID20 BID4 BID7 BID6 BID16 .",
"However, in these models, the operations needed to learn dependencies between distant positions can be difficult to learn BID13 BID11 .",
"Attention mechanisms, often used in conjunction with recurrent models, have become an integral part of complex sequential tasks because they facilitate learning of such dependencies BID22 BID26 BID27 BID17 .In",
"BID33 , the authors introduce the Transformer network, a novel architecture that avoids the recurrence equation and maps the input sequences into hidden states solely using attention. Specifically",
", the authors use positional encodings in conjunction with a multi-head attention mechanism. This allows",
"for increased parallel computation and reduces time to convergence. The authors",
"report results for neural machine translation that show the Transformer networks achieves state-of-the-art performance on the WMT 2014 English-to-German and English-to-French tasks while being orders-of-magnitude faster than prior approaches.Transformer networks still require a large number of parameters to achieve state-of-the-art performance. In the case",
"of the newstest2013 English-to-German translation task, the base model required 65M parameters, and the large model required 213M parameters. We propose",
"a variant of the Transformer network which we call Weighted Transformer that uses self-attention branches in lieu of the multi-head attention. The branches",
"replace the multiple heads in the attention mechanism of the original Transformer network, and the model learns to combine these branches during training. This branched",
"architecture enables the network to achieve comparable performance at a significantly lower computational cost. Indeed, through",
"this modification, we improve the state-of-the-art performance by 0.5 and 0.4 BLEU scores on the WMT 2014 English-to-German and English-to-French tasks, respectively. Finally, we present",
"evidence that suggests a regularizing effect of the proposed architecture.",
"We present the Weighted Transformer that trains faster and achieves better performance than the original Transformer network.",
"The proposed architecture replaces the multi-head attention in the Transformer network by a multiple self-attention branches whose contributions are learned as a part of the training process.",
"We report numerical results on the WMT 2014 English-to-German and English-to-French tasks and show that the Weighted Transformer improves the state-of-the-art BLEU scores by 0.5 and 0.4 points respectively.",
"Further, our proposed architecture trains 15 − 40% faster than the baseline Transformer.",
"Finally, we present evidence suggesting the regularizing effect of the proposal and emphasize that the relative improvement in BLEU score is observed across various hyper-parameter settings for both small and large models."
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"Using branched attention with learned combination weights outperforms the baseline transformer for machine translation tasks."
] |
[
"Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world.",
"However, policies learned with imitation learning have limited flexibility to accommodate varied goals at test time.",
"Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time.",
"However, MBRL suffers from two shortcomings.",
"First, the model does not help to choose desired or safe outcomes -- its dynamics estimate only what is possible, not what is preferred.",
"Second, MBRL typically requires additional online data collection to ensure that the model is accurate in those situations that are actually encountered when attempting to achieve test time goals.",
"Collecting this data with a partially trained model can be dangerous and time-consuming.",
"In this paper, we aim to combine the benefits of imitation learning and MBRL, and propose imitative models: probabilistic predictive models able to plan expert-like trajectories to achieve arbitrary goals.",
"We find this method substantially outperforms both direct imitation and MBRL in a simulated autonomous driving task, and can be learned efficiently from a fixed set of expert demonstrations without additional online data collection.",
"We also show our model can flexibly incorporate user-supplied costs at test-time, can plan to sequences of goals, and can even perform well with imprecise goals, including goals on the wrong side of the road.",
"Reinforcement learning (RL) algorithms offer the promise of automatically learning behaviors from raw sensory inputs with minimal engineering.",
"However, RL generally requires online learning: the agent must collect more data with its latest strategy, use this data to update a model, and repeat.",
"While this is natural in some settings, deploying a partially-trained policy on a real-world autonomous system, such as a car or robot, can be dangerous.",
"In these settings the behavior must be learned offline, usually with expert demonstrations.",
"How can we incorporate such demonstrations into a flexible robotic system, like an autonomous car?",
"One option is imitation learning (IL), which can learn policies that stay near the expert's distribution.",
"Another option is model-based RL (MBRL) BID8 BID2 , which can use the data to fit a dynamics model, and can in principle be used with planning algorithms to achieve any user-specified goal at test time.",
"However, in practice, model-based and model-free RL algorithms are vulnerable to distributional drift BID32 BID24 : when acting according to the learned model or policy, the agent visits states different from those seen during training, and in those it is unlikely to determine an effective course of action.",
"This is especially problematic when the data intentionally excludes adverse events, such as crashes.",
"A model ignorant to the possibility of a crash cannot know how to prevent it.",
"Therefore, MBRL algorithms usually require online collection and training BID6 BID12 .",
"Imitation learning algorithms use expert demonstration data and, despite similar drift shortcomings BID26 , can sometimes learn effective policies without additional online data collection BID35 .",
"However, standard IL offers little task flexibility since it only predicts low-level behavior.",
"While several works augmented IL with goal conditioning BID4 BID1 , these goals must be specified in advance during training, and are typically simple (e.g., turning left or right).Figure",
"1: We apply our approach to navigation in CARLA BID5 . Columns",
"1,2: Images depicting the current scene. The overhead",
"image depicts a 50 m 2 area. Column 3: LIDAR",
"input and goals are provided to our deep imitative trajectory model, and plans to the goals are computed under the model's likelihood objective, and colored according to their ranking under the objective, with red indicating the best plan. The red square",
"indicates the chosen high-level goal, and the yellow cross indicates a point along our plan used as a setpoint for a PID controller. The LIDAR map",
"is 100 m 2 , and each goal is ≥20 m away from the vehicle. Column 4: Our",
"model can incorporate arbitrary test-time costs, and use them to adjust its planning objective and plan ranking.Figure 2: A brief taxonomy of learning-based control methods. In our scenario",
", we avoid online data collection, specifically from the policy we seek to imitate. We structure our",
"imitation learner with a model to make it flexible to new tasks at test time. We compare against",
"other offline approaches (front face).The goal in our work",
"is to devise a new algorithm that combines the advantages of IL and MBRL, affording both the flexibility to achieve new user-specified goals at test time and the ability to learn entirely from offline data. By learning a deep probabilistic",
"predictive model from expert-provided data, we capture the distribution of expert behaviors without using manually designed reward functions. To plan to a goal, our method infers",
"the most probable expert state trajectory, conditioned on the current position and reaching the goal. By incorporating a model-based representation",
", our method can easily plan to previously unseen user-specified goals while respecting rules of the road, and can be flexibly repurposed to perform a wide range of test-time tasks without any additional training. Inference with this model resembles trajectory",
"optimization in model-based reinforcement learning, and learning this model resembles imitation learning. Our method's relationship to other work is illustrated",
"in Fig. 2 . We demonstrate our method on a simulated autonomous driving",
"task (see FIG0 . A high-level route planner provides navigational goals, which",
"our model uses to automatically generate plans that obey the rules of the road, inferred entirely from data. In contrast to IL, our method produces an interpretable distribution",
"over trajectories and can follow a variety of goals without additional training. In contrast to MBRL, our method generates human-like behaviors without",
"additional data collection or learning. In our experiments, our approach substantially outperforms both MBRL and",
"IL: it can efficiently learn near-perfect driving through the static-world CARLA simulator from just 7,000 trajectories obtained from 19 hours of driving. We also show that our model can flexibly incorporate and achieve goals not",
"seen during training, and is robust to errors in the high-level navigation system, even when the high-level goals are on the wrong side of the road. Videos of our results are available.",
"We proposed a method that combines elements of imitation learning and model-based reinforcement learning (MBRL).",
"Our method first learns what preferred behavior is by fitting a probabilistic model to the distribution of expert demonstrations at training time, and then plans paths to achieve userspecified goals at test time while maintaining high probability under this distribution.",
"We demonstrated several advantages and applications of our algorithm in autonomous driving scenarios.",
"In the context of MBRL, our method mitigates the distributional drift issue by explicitly preferring plans that stay close to the expert demonstration data.",
"This implicitly allows our method to enforce basic safety properties: in contrast to MBRL, which requires negative examples to understand the potential for adverse outcomes (e.g., crashes), our method automatically avoids such outcomes specifically because they do not occur (or rarely occur) in the training data.",
"In the context of imitation learning, our method provides a flexible, safe way to generalize to new goals by planning, compared to prior work on black-box, model-free conditional imitation learning.",
"Our algorithm produces an explicit plan within the distribution of preferred behavior accompanied with a score: the former offers interpretability, and the latter provides an estimate of the feasibility of the plan.",
"We believe our method is broadly applicable in settings where expert demonstrations are available, flexibility to new situations is demanded, and safety is critical.",
"Figure 8 : Tolerating bad waypoints.",
"The planner prefers waypoints in the distribution of expert behavior: on the road at a reasonable distance.",
"Columns 1,2: Planning with 1 /2 decoy waypoints.",
"Columns 3,4: Planning with all waypoints on the wrong side of the road."
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"Hybrid Vision-Driven Imitation Learning and Model-Based Reinforcement Learning for Planning, Forecasting, and Control"
] |
[
"Uncertainty estimation and ensembling methods go hand-in-hand.",
"Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance.",
"At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation.",
"In this work, we focus on in-domain uncertainty for image classification.",
"We explore the standards for its quantification and point out pitfalls of existing metrics.",
"Avoiding these pitfalls, we perform a broad study of different ensembling techniques.",
"To provide more insight in the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.",
"Deep neural networks (DNNs) have become one of the most popular families of machine learning models.",
"The predictive performance of DNNs for classification is often measured in terms of accuracy.",
"However, DNNs have been shown to yield inaccurate and unreliable probability estimates, or predictive uncertainty (Guo et al., 2017) .",
"This has brought considerable attention to the problem of uncertainty estimation with deep neural networks.",
"There are many faces to uncertainty estimation.",
"Different desirable uncertainty estimation properties of a model require different settings and metrics to capture them.",
"Out-of-domain uncertainty of the model is measured on data that does not follow the same distribution as the training dataset (out-of-domain data).",
"Out-of-domain data can include images corrupted with rotations or blurring, adversarial attacks (Szegedy et al., 2013) or data points from a completely different dataset.",
"The model is expected to be resistant to data corruptions and to be more uncertain on out-of-domain data than on in-domain data.",
"This setting was explored in a recent study by (Ovadia et al., 2019) .",
"On the contrary, in-domain uncertainty of the model is measured on data taken from the training data distribution, i.e. data from the same domain.",
"In this case, a model is expected to provide correct probability estimates: it should not be overconfident in the wrong predictions, and should not be too uncertain about the correct predictions.",
"Ensembles of deep neural networks have become a de-facto standard for uncertainty estimation and improving the quality of deep learning models (Hansen & Salamon, 1990; Krizhevsky et al., 2009; Lakshminarayanan et al., 2017) .",
"There are two main directions in the field of training ensembles of DNNs: training stochastic computation graphs and obtaining separate snapshots of neural network weights.",
"Methods based on the paradigm of stochastic computation graphs introduce noise over weights or activations of deep learning models.",
"When the model is trained, each sample of the noise corresponds to a member of the ensemble.",
"During test time, the predictions are averaged across the noise samples.",
"These methods include (test-time) data augmentation, dropout (Srivastava et al., 2014; Gal & Ghahramani, 2016) , variational inference (Blundell et al., 2015; Kingma et al., 2015; Louizos & Welling, 2017) , batch normalization (Ioffe & Szegedy, 2015; Teye et al., 2018; Atanov et al., 2019) , Laplace approximation (Ritter et al., 2018) and many more.",
"Snapshot-based methods aim to obtain sets of weights for deep learning models and then to average the predictions across these weights.",
"The weights can be trained independently (e.g., deep ensembles (Lakshminarayanan et al., 2017) ), collected on different stages of a training trajectory (e.g., snapshot ensembles (Huang et al., 2017) and fast geometric ensembles (Garipov et al., 2018) ), or obtained from a sampling process (e.g., MCMC-based methods (Welling & Teh, 2011; Zhang et al., 2019) ).",
"These two paradigms can be combined.",
"Some works suggest construction of ensembles of stochastic computation graphs (Tomczak et al., 2018) , while others make use of the collected snapshots to construct a stochastic computation graph (Wang et al., 2018; Maddox et al., 2019) .",
"In this paper, we focus on assessing the quality of in-domain uncertainty estimation.",
"We show that many common metrics in the field are either not comparable across different models or fail to provide a reliable ranking, and then address some of stated pitfalls.",
"Following that, we perform a broad evaluation of modern DNN ensembles on CIFAR-10/100 and ImageNet datasets.",
"To aid interpretatability, we introduce the deep ensemble equivalent score that essentially measures the number of \"independent\" models in an ensemble of DNNs.",
"We draw a set of conclusions with regard to ensembling performance and metric reliability to guide future research practices.",
"For example, we find that methods specifically designed to traverse different \"optima\" of the loss function (snapshot ensembles and cyclical SGLD) come close to matching the performance of deep ensembles while methods that only explore the vicinity of a single \"optimum\" (Dropout, FGE, K-FAC Laplace and variational inference) fall far behind.",
"We have explored the field of in-domain uncertainty estimation and performed an extensive evaluation of modern ensembling techniques.",
"Our main findings can be summarized as follows:",
"• Temperature scaling is a must even for ensembles.",
"While ensembles generally have better calibration out-of-the-box, they are not calibrated perfectly and can benefit from the procedure.",
"Comparison of log-likelihoods of different ensembling methods without temperature scaling might not provide a fair ranking especially if some models happen to be miscalibrated.",
"• Many common metrics for measuring in-domain uncertainty are either unreliable (ECE and analogues) or cannot be used to compare different methods (AUC-ROC, AUC-PR for misclassification detection; accuracy-confidence curves).",
"In order to perform a fair comparison of different methods, one needs to be cautious of these pitfalls.",
"• Many popular ensembling techniques require dozens of samples for test-time averaging, yet are essentially equivalent to a handful of independently trained models.",
"Deep ensembles dominate other methods given a fixed test-time budget.",
"The results indicate in particular that exploration of different modes in the loss landscape is crucial for good predictive performance.",
"• Methods that are stuck in a single mode are unable to compete with methods that are designed to explore different modes of the loss landscape.",
"Would more elaborate posterior approximations and better inference techniques shorten this gap?",
"• Test-time data augmentation is a surprisingly strong baseline for in-domain uncertainty estimation and can significantly improve other methods without increasing training time or model size since data augmentation is usually already present during training.",
"Our takeaways are aligned with the take-home messages of (Ovadia et al., 2019 ) that relate to indomain uncertainty estimation.",
"We also observe a stable ordering of different methods in our experiments, and observe that deep ensembles with few members outperform methods based on stochastic computation graphs.",
"A large number of unreliable metrics inhibits a fair comparison of different methods.",
"Because of this, we urge the community to aim for more reliable benchmarks in the numerous setups of uncertainty estimation.",
"Implied probabilistic model Conventional neural networks for classification are usually trained using the average cross-entropy loss function with weight decay regularization hidden inside an optimizer in a deep learning framework like PyTorch.",
"The actual underlying optimization problem can be written as follows:",
"where",
"is the training dataset of N objects x i with corresponding labels y * i , λ is the weight decay scale andp(y * i = j | x i , w) denotes the probability that a neural network with parameters w assigns to class j when evaluated on object x i .",
"The cross-entropy loss defines a likelihood function p(y * | x, w) and weight decay regularization, or L 2 regularization, corresponds to a certain Gaussian prior distribution p(w).",
"The whole optimization objective then corresponds to maximum a posteriori inference in the following probabilistic model:",
"log p(y",
"As many of the considered methods are probabilistic in nature, we use the same probabilistic model for all of them.",
"We use the SoftMax-based likelihood for all models, and use the fully-factorized zero-mean Gaussian prior distribution with variances σ 2 = (N λ) −1 , where the number of objects N and the weight decay scale λ are dictated by the particular datasets and neural architectures, as defined in the following paragraph.",
"In order to make the result comparable across all ensembling techniques, we use the same prababilistic model for all methods, choosing fixed weight decay parameters for each architecture.",
"Conventional networks On CIFAR-10/100 datasets all networks were trained by SGD optimizer with batch size of 128, momentum 0.9 and model-specific parameters i.e., initial learning rate (lr init ), weight decay (wd), and number of optimization epoch (epoch).",
"The specific hyperparameters are shown in Table 2 .",
"The models used a unified learning rate scheduler that is shown in equation 10.",
"All models have been trained using data augmentation that consists of horizontal flips, random crop of size 32 with padding 4.",
"The standard data normalization has also been applied.",
"Weight decays, initial learning rates, and the learning rate scheduler were taken from (Garipov et al., 2018) paper.",
"Compared with hyperparameters of (Garipov et al., 2018) , the number of optimization epochs has been increased since we found that all models were underfitted.",
"While original WideResNet28x10 includes number of dropout layers with p = 0.3 and 200 training epoch, in this setting we find that WideResNet28x10 underfits, and requires a longer training.",
"Thus, we used p = 0, effectively it does not affect the final performance of the model in our experiments, but reduces training time.",
"On ImageNet dataset we used ResNet50 examples with a default hyperparameters from PyTorch examples 5 .",
"Specifically SGD optimizer with momentum 0.9, batch size of 256, initial learning rate 0.1, and with decay 1e-4.",
"The training also includes data augmentation random crop of size 224 × 224, horizontal flips, and normalization, and learning rate scheduler lr = lr init · 0.1 epoch//30 , where // denotes integer division.",
"We only deviated from standard parameters by increasing the number of training epochs from 90 to 130.",
"Or models achived top-1 error of 23.81 ± 0.15 that closely matches accuracy of the ResNet50 probided by PyTorch which is 23.85",
"6 .",
"Training of one model on a single NVIDIA Tesla V100 GPU takes approximately 5.5 days.",
"Deep Ensembles Deep ensembles (Lakshminarayanan et al., 2017) average the predictions across networks trained independently starting from different initializations.",
"To obtain Deep Ensemble we repeat the procedure of training standard networks 128 times for all architectures on CIFAR-10 and CIFAR-100 datasets (1024 networks over all) and 50 times for ImageNet dataset.",
"Every single member of Deep Ensembles were actually trained with exactly the same hyperparameters as conventional models of the same arhitecture.",
"Dropout The binary dropout (or MC dropout) (Srivastava et al., 2014; Gal & Ghahramani, 2016) is one of the most known ensembling techniques.",
"It puts a multiplicative Bernoulli noise with parameter p over activations of ether fully-connected or convolutional layer, averaging predictions of the network w.r.t. the noise during test.",
"The dropout layers have been applied to VGG, and WideResNet networks on CIFAR-10 and CIFAR-100 datasets.",
"For VGG the dropout has been applied to fully-connected (fc) layers with p = 0.5, overall two dropout layers, one before the first fc-layer and one before the second one.",
"While original version of VGG for CIFARs (Zagoruyko, 2015) exploits more dropout layers, we observed that any additional dropout layer deteriorates the performance on the model in ether deterministic or stochastic mode.",
"For WideResNet network we applied dropout consistently with the original paper (Zagoruyko & Komodakis, 2016) with p = 0.3.",
"The dropout usually increases the time to convergence, thus, VGG and WideResNet networks with dropout was trained for 400 epoch instead of 300 epoch for deterministic case.",
"The all other hyperparameters was the same as in case of conventional models.",
"Variational Inference The VI approximates a true posterior distribution p(w | Data) with a tractable variational approximation q θ (w), by maximizing so-called variational lower bound L (eq. 11) w.r.t. parameters of variational approximation θ.",
"We used fully-factorized Gaussian approximation q(w), and Gaussian prior distribution p(w).",
"In the case of such a prior p(w) the probabilistic model remains consistent with conventional training which corresponds to MAP inference in the same probabilistic model.",
"We used variational inference for both convolutional and fully-connected layers, where variances of the weights was parameterized by log σ.",
"For fully-connected layers we applied the LRT (Kingma et al., 2015) .",
"While variational inference provide a theoretical grounded way to approximate a true posterior, on practice, it tends to underfit deep learning models (Kingma et al., 2015) .",
"The following tricks are applied to deal with it: pre-training (Molchanov et al., 2017) Consistently with the practical tricks we use a pre-training, specifically, we initialize µ with a snapshot of the weights of pretrained conventional model, and initialize log σ with model-specific constant log σ init .",
"The KL-divergence -except the term that corresponds to a weight decay -was scaled on model specific parameter β.",
"The weigh decay term was implemented as a part of the optimizer.",
"We used a fact that KL-divergence between two Gaussian distributions can be rewritten as two terms one of which is equal to wd regularization.",
"On CIFAR-10 and CIFAR-100 we used β 1e-4 for VGG, ResNet100 and ResNet164 networks, and β 1e-5 for WideResNet.",
"The initialization of log-variance log σ init was set to −5 for all models.",
"Parameters µ were optimized with conventional SGD (with the same parameters as conventional networks, except initial learning rate lr init that was set to 1e-3).",
"We used a separate Adam optimizer with constant learning rate 1e-3 to optimize log-variances of the weights log σ.",
"The training was held for 100 epochs, that corresponds to 400 epochs of training (including pre-training).",
"On ImageNet we used β = 1e-3, lr init = 0.01, log σ init = −6, and held training for a 45 epoch form a per-trained model."
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"We highlight the problems with common metrics of in-domain uncertainty and perform a broad study of modern ensembling techniques."
] |
[
"Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.",
"Although most techniques developed so far requires knowledge of the architecture of the machine learning model and remains hard to scale to complex prediction pipelines, the method of randomized smoothing has been shown to overcome many of these obstacles.",
"By requiring only black-box access to the underlying model, randomized smoothing scales to large architectures and is agnostic to the internals of the network.",
"However, past work on randomized smoothing has focused on restricted classes of smoothing measures or perturbations (like Gaussian or discrete) and has only been able to prove robustness with respect to simple norm bounds.",
"In this paper we introduce a general framework for proving robustness properties of smoothed machine learning models in the black-box setting.",
"Specifically, we extend randomized smoothing procedures to handle arbitrary smoothing measures and prove robustness of the smoothed classifier by using $f$-divergences.",
"Our methodology achieves state-of-the-art}certified robustness on MNIST, CIFAR-10 and ImageNet and also audio classification task, Librispeech, with respect to several classes of adversarial perturbations.",
"Predictors obtained from machine learning algorithms have been shown to be vulnerable to making errors when the inputs are perturbed by carefully chosen small but imperceptible amounts (Szegedy et al., 2014; Biggio et al., 2013) .",
"This has motivated significant amount of research in improving adversarial robustness of a machine learning model (see, e.g. Goodfellow et al., 2015; Madry et al., 2018) .",
"While significant advances have been made, it has been shown that models that were estimated to be robust have later been broken by stronger attacks (Athalye et al., 2018; Uesato et al., 2018) .",
"This has led to the need for methods that offer provable guarantees that the predictor cannot be forced to misclassify an example by any attack algorithm restricted to produce perturbations within a certain set (for example, within an p norm ball).",
"While progress has been made leading to methods that are able to compute provable guarantees for several image and text classification tasks (Wong & Kolter, 2018; Wong et al., 2018; Raghunathan et al., 2018; Dvijotham et al., 2018; Katz et al., 2017; Huang et al., 2019; Jia et al., 2019) , these methods require extensive knowledge of the architecture of the predictor and are not easy to extend to new models or architectures, requiring specialized algorithms for each new class of models.",
"Further, the computational complexity of these methods grows significantly with input dimension and model size.",
"Consequently, to deal with these obstacles, recent work has proposed the randomized smoothing strategy for verifying the robustness of classifiers.",
"Specifically, Lecuyer et al. (2019) ; Cohen et al. (2019) have shown that robustness properties can be more easily verified for the smoothed version of a base classifier h: h s (x) = arg max y∈Y P X∼µ(x) [h(X) = y] ,",
"where the labels returned by the smoothed classifier h s are obtained by taking a \"majority vote\" over the predictions of the original classifier h on random inputs drawn from a probability distribution µ(x), called the smoothing measure (here Y denotes the set of classes in the problem).",
"Lecuyer et al. (2019) showed that verifying the robustness of this smoothed classifier is significantly simpler than verifying the original classifier h and only requires estimating the distribution of outputs of the classifier under random perturbations of the input, but does not require access to the internals of the classifier h.",
"We refer to this as black-box verification.",
"In this work, we develop a general framework for black-box verification that recovers prior work as special cases, and improves upon previous results in various ways.",
"Contributions Our contributions are summarized as follows:",
"1. We formulate the general problem of black-box verification via a generalized randomized smoothing procedure, which extends existing approaches to allow for arbitrary smoothing measures.",
"Specifically, we show that robustness certificates for smoothed classifiers can be obtained by solving a small convex optimization problem when allowed adversarial perturbations can be characterized via divergence-based bounds on the smoothing measure.",
"2. We prove that our certificates generalize previous results obtained in related work (Lecuyer et al., 2019; Cohen et al., 2019; Li et al., 2019) , and vastly extend the class of perturbations and smoothing measures that can be used while still allowing certifiable guarantees.",
"3. We introduce the notion of full-information and information-limited settings, and show that the information-limited setting that has been the main focus of prior work leads to weaker certificates for smoothed probabilistic classifiers, and can be improved by using additional information (the distribution of label scores under randomized smoothing).",
"4. We evaluate our framework experimentally on image and classification tasks, obtaining robustness certificates that improve upon other black-box methods either in terms of certificate tightness or computation time on robustness to 0 , 1 or 2 perturbations on MNIST, CIFAR-10 and ImageNet.",
"2 perturbations result from worst-case realizations of white noise that is common in many image, speech and video processing.",
"0 perturbations can model missing data (missing pixels in an image, or samples in a time-domain audio signal) while 1 perturbations can be used to model convex combinations of discrete perturbations in text classification (Jia et al., 2019) .",
"We also obtain the first, to the best of our knowledge, certifiably robust model for an audio classification task, Librispeech (Panayotov et al., 2015) , with variable-length inputs.",
"We have introduced a general framework for black-box verification using f -divergence constraints.",
"The framework improves upon state-of-the-art results on both image classification and audio tasks by a significant margin in terms of robustness certificates or computation time.",
"We believe that our framework can potentially enable scalable computation of robustness verification for more complex predictors and structured perturbations that can be modeled using f-divergence constraints."
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] | SJlKrkSFPH | true | [
"Develop a general framework to establish certified robustness of ML models against various classes of adversarial perturbations"
] |
[
"The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition.",
"Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes.",
"In this paper, we propose a generative latent variable model, called SPACE, that provides a unified probabilistic modeling framework that combines the best of spatial-attention and scene-mixture approaches.",
"SPACE can explicitly provide factorized object representations for foreground objects while also decomposing background segments of complex morphology.",
"Previous models are good at either of these, but not both.",
"SPACE also resolves the scalability problems of previous methods by incorporating parallel spatial-attention and thus is applicable to scenes with a large number of objects without performance degradations.",
"We show through experiments on Atari and 3D-Rooms that SPACE achieves the above properties consistently in comparison to SPAIR, IODINE, and GENESIS.",
"Results of our experiments can be found on our project website: https://sites.google.com/view/space-project-page",
"One of the unsolved key challenges in machine learning is unsupervised learning of structured representation for a visual scene containing many objects with occlusion, partial observability, and complex background.",
"When properly decomposed into meaningful abstract entities such as objects and spaces, this structured representation brings many advantages of abstract (symbolic) representation to areas where contemporary deep learning approaches with a global continuous vector representation of a scene have not been successful.",
"For example, a structured representation may improve sample efficiency for downstream tasks such as a deep reinforcement learning agent (Mnih et al., 2013) .",
"It may also enable visual variable binding (Sun, 1992) for reasoning and causal inference over the relationships between the objects and agents in a scene.",
"Structured representations also provide composability and transferability for better generalization.",
"Recent approaches to this problem of unsupervised object-oriented scene representation can be categorized into two types of models: scene-mixture models and spatial-attention models.",
"In scenemixture models (Greff et al., 2017; Burgess et al., 2019; Engelcke et al., 2019) , a visual scene is explained by a mixture of a finite number of component images.",
"This type of representation provides flexible segmentation maps that can handle objects and background segments of complex morphology.",
"However, since each component corresponds to a full-scale image, important physical features of objects like position and scale are only implicitly encoded in the scale of a full image and further disentanglement is required to extract these useful features.",
"Also, since it does not explicitly reflect useful inductive biases like the locality of an object in the Gestalt principles (Koffka, 2013) , the resulting component representation is not necessarily a representation of a local area.",
"Moreover, to obtain a complete scene, a component needs to refer to other components, and thus inference is inherently performed sequentially, resulting in limitations in scaling to scenes with many objects.",
"In contrast, spatial-attention models (Eslami et al., 2016; Crawford & Pineau, 2019) can explicitly obtain the fully disentangled geometric representation of objects such as position and scale.",
"Such features are grounded on the semantics of physics and should be useful in many ways (e.g., sample efficiency, interpretability, geometric reasoning and inference, transferability).",
"However, these models cannot represent complex objects and background segments that have too flexible morphology to be captured by spatial attention (i.e. based on rectangular bounding boxes).",
"Similar to scene-mixture models, previous models in this class show scalability issues as objects are processed sequentially.",
"In this paper, we propose a method, called Spatially Parallel Attention and Component Extraction (SPACE), that combines the best of both approaches.",
"SPACE learns to process foreground objects, which can be captured efficiently by bounding boxes, by using parallel spatial-attention while decomposing the remaining area that includes both morphologically complex objects and background segments by using component mixtures.",
"Thus, SPACE provides an object-wise disentangled representation of foreground objects along with explicit properties like position and scale per object while also providing decomposed representations of complex background components.",
"Furthermore, by fully parallelizing the foreground object processing, we resolve the scalability issue of existing spatial attention methods.",
"In experiments on 3D-room scenes and Atari game scenes, we quantitatively and qualitatively compare the representation of SPACE to other models and show that SPACE combines the benefits of both approaches in addition to significant speed-ups due to the parallel foreground processing.",
"The contributions of the paper are as follows.",
"First, we introduce a model that unifies the benefits of spatial-attention and scene-mixture approaches in a principled framework of probabilistic latent variable modeling.",
"Second, we introduce a spatially parallel multi-object processing module and demonstrate that it can significantly mitigate the scalability problems of previous methods.",
"Lastly, we provide an extensive comparison with previous models where we illustrate the capabilities and limitations of each method.",
"We propose SPACE, a unified probabilistic model that combines the benefits of the object representation models based on spatial attention and the scene decomposition models based on component mixture.",
"SPACE can explicitly provide factorized object representation per foreground object while also decomposing complex background segments.",
"SPACE also achieves a significant speed-up and thus makes the model applicable to scenes with a much larger number of objects without performance degradation.",
"Besides, the detected objects in SPACE are also more intuitive than other methods.",
"We show the above properties of SPACE on Atari and 3D-Rooms.",
"Interesting future directions are to replace the sequential processing of background by a parallel one and to improve the model for natural images.",
"Our next plan is to apply SPACE for object-oriented model-based reinforcement learning.",
"Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi.",
"You only look once: Unified, real-time object detection.",
": Object detection and background segmentation using SPACE on 3D-Room data set with large number of objects."
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] | rkl03ySYDH | true | [
"We propose a generative latent variable model for unsupervised scene decomposition that provides factorized object representation per foreground object while also decomposing background segments of complex morphology."
] |
[
"We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in \"one shot\".",
"The features may be both real-valued and categorical.",
"Training of the model is performed by stochastic variational Bayes.",
"The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.",
"In past years, a number of generative probabilistic models based on neural networks have been proposed.",
"The most popular approaches include variational autoencoder (Kingma & Welling, 2013 ) (VAE) and generative adversarial net (Goodfellow et al., 2014 ) (GANs).",
"They learn a distribution over objects p(x) and allow sampling from this distribution.In many cases, we are interested in learning a conditional distribution p(x|y).",
"For instance, if x is an image of a face, y could be the characteristics describing the face (are glasses present or not; length of hair, etc.) Conditional variational autoencoder (Sohn et al., 2015) and conditional generative adversarial nets (Mirza & Osindero, 2014) are popular methods for this problem.In this paper, we consider the problem of learning all conditional distributions of the form p(x I |x U \\I ), where U is the set of all features and I is its arbitrary subset.",
"This problem generalizes both learning the joint distribution p(x) and learning the conditional distribution p(x|y).",
"To tackle this problem, we propose a Variational Autoencoder with Arbitrary Conditioning (VAEAC) model.",
"It is a latent variable model similar to VAE, but allows conditioning on an arbitrary subset of the features.",
"The conditioning features affect the prior on the latent Gaussian variables which are used to generate unobserved features.",
"The model is trained using stochastic gradient variational Bayes (Kingma & Welling, 2013) .We",
"consider two most natural applications of the proposed model. The",
"first one is feature imputation where the goal is to restore the missing features given the observed ones. The",
"imputed values may be valuable by themselves or may improve the performance of other machine learning algorithms which process the dataset. Another",
"application is image inpainting in which the goal is to fill in an unobserved part of an image with an artificial content in a realistic way. This can",
"be used for removing unnecessary objects from the images or, vice versa, for complementing the partially closed or corrupted object.The experimental evaluation shows that the proposed model successfully samples from the conditional distributions. The distribution",
"over samples is close to the true conditional distribution. This property is",
"very important when the true distribution has several modes. The model is shown",
"to be effective in feature imputation problem which helps to increase the quality of subsequent discriminative models on different problems from UCI datasets collection (Lichman, 2013) . We demonstrate that",
"model can generate diverse and realistic image inpaintings on MNIST (LeCun et al., 1998) , Omniglot (Lake et al., 2015) and CelebA (Liu et al., 2015) datasets, and works even better than the current state of the art inpainting techniques in terms of peak signal to noise ratio (PSNR).The paper is organized",
"as follows. In section 2 we review",
"the related works. In section 3 we briefly",
"describe variational autoencoders and conditional variational autoencoders. In section 4 we define",
"the problem, describe the VAEAC model and its training procedure. In section 5 we evaluate",
"VAEAC. Section 6 concludes the",
"paper. Appendix contains additional",
"explanations, theoretical analysis, and experiments for VAEAC."
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"We propose an extension of conditional variational autoencoder that allows conditioning on an arbitrary subset of the features and sampling the remaining ones."
] |
[
"We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance.",
"Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies.",
"Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches.",
"We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.",
"Discovering high-performance neural network architectures required years of extensive research by human experts through trial and error.",
"As far as the image classification task is concerned, state-ofthe-art convolutional neural networks are going beyond deep, chain-structured layout BID19 BID6 towards increasingly more complex, graph-structured topologies BID12 BID27 BID9 .",
"The combinatorial explosion in the design space makes handcrafted architectures not only expensive to obtain, but also likely to be suboptimal in performance.Recently, there has been a surge of interest in using algorithms to automate the manual process of architecture design.",
"Their goal can be described as finding the optimal architecture in a given search space such that the validation accuracy is maximized on the given task.",
"Representative architecture search algorithms can be categorized as random with weights prediction BID1 , Monte Carlo Tree Search BID15 , evolution BID21 BID26 BID13 BID16 , and reinforcement learning BID0 BID30 BID31 BID29 , among which reinforcement learning approaches have demonstrated the strongest empirical performance so far.Architecture search can be computationally very intensive as each evaluation typically requires training a neural network.",
"Therefore, it is common to restrict the search space to reduce complexity and increase efficiency of architecture search.",
"Various constraints that have been used include: growing a convolutional \"backbone\" with skip connections BID16 , a linear sequence of filter banks BID1 , or a directed graph where every node has exactly two predecessors BID31 .",
"In this work we constrain the search space by imposing a hierarchical network structure, while allowing flexible network topologies (directed acyclic graphs) at each level of the hierarchy.",
"Starting from a small set of primitives such as convolutional and pooling operations at the bottom level of the hierarchy, higher-level computation graphs, or motifs, are formed by using lower-level motifs as their building blocks.",
"The motifs at the top of the hierarchy are stacked multiple times to form the final neural network.",
"This approach enables search algorithms to implement powerful hierarchical modules where any change in the motifs is propagated across the whole network immediately.",
"This is analogous to the modularized design patterns used in many handcrafted architectures, e.g. VGGNet BID19 , ResNet BID6 , and Inception BID24 are all comprised of building blocks.",
"In our case, a hierarchical architecture is discovered through evolutionary or random search.The evolution of neural architectures was studied as a sub-task of neuroevolution BID8 BID14 BID28 BID21 BID3 , where the topology of a neural network is simultaneously evolved along with its weights and hyperparameters.",
"The benefits of indirect encoding schemes, such as multi-scale representations, have historically been discussed in BID5 ; BID11 BID20 BID22 .",
"Despite these pioneer studies, evolutionary or random architecture search has not been investigated at larger scale on image classification benchmarks until recently BID16 BID13 BID26 BID1 BID15 .",
"Our work shows that the power of simple search methods can be substantially enhanced using well-designed search spaces.Our experimental setup resembles BID31 , where an architecture found using reinforcement learning obtained the state-of-the-art performance on ImageNet.",
"Our work reveals that random or evolutionary methods, which so far have been seen as less efficient, can scale and achieve competitive performance on this task if combined with a powerful architecture representation, whilst utilizing significantly less computational resources.To summarize, our main contributions are:1.",
"We introduce hierarchical representations for describing neural network architectures.",
"2. We show that competitive architectures for image classification can be obtained even with simplistic random search, which demonstrates the importance of search space construction.",
"3. We present a scalable variant of evolutionary search which further improves the results and achieves the best published results 1 among evolutionary architecture search techniques.",
"We have presented an efficient evolutionary method that identifies high-performing neural architectures based on a novel hierarchical representation scheme, where smaller operations are used as the building blocks to form the larger ones.",
"Notably, we show that strong results can be obtained even using simplistic search algorithms, such as evolution or random search, when coupled with a well-designed architecture representation.",
"Our best architecture yields the state-of-the-art result on A ARCHITECTURE VISUALIZATION Visualization of the learned cell and motifs of our best-performing hierarchical architecture.",
"Note that only motifs 1,3,4,5 are used to construct the cell, among which motifs 3 and 5 are dominating."
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"In this paper we propose a hierarchical architecture representation in which doing random or evolutionary architecture search yields highly competitive results using fewer computational resources than the prior art."
] |
[
"In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e.g., images obtained from self-supervised robot interaction.",
"VP algorithms essentially combine data-driven perception and planning, and are important for robotic manipulation and navigation domains, among others.",
"A recent and promising approach to VP is the semi-parametric topological memory (SPTM) method, where image samples are treated as nodes in a graph, and the connectivity in the graph is learned using deep image classification.",
"Thus, the learned graph represents the topological connectivity of the data, and planning can be performed using conventional graph search methods.",
"However, training SPTM necessitates a suitable loss function for the connectivity classifier, which requires non-trivial manual tuning.",
"More importantly, SPTM is constricted in its ability to generalize to changes in the domain, as its graph is constructed from direct observations and thus requires collecting new samples for planning.",
"In this paper, we propose Hallucinative Topological Memory (HTM), which overcomes these shortcomings.",
"In HTM, instead of training a discriminative classifier we train an energy function using contrastive predictive coding.",
"In addition, we learn a conditional VAE model that generates samples given a context image of the domain, and use these hallucinated samples for building the connectivity graph, allowing for zero-shot generalization to domain changes.",
"In simulated domains, HTM outperforms conventional SPTM and visual foresight methods in terms of both plan quality and success in long-horizon planning.",
"For robots to operate in unstructured environments such as homes and hospitals, they need to manipulate objects and solve complex tasks as they perceive the physical world.",
"While task planning and object manipulation have been studied in the classical AI paradigm [20, 9, 30, 10] , most successes have relied on a human-designed state representation and perception, which can be challenging to obtain in unstructured domains.",
"While high-dimensional sensory input such as images can be easy to acquire, planning using raw percepts is challenging.",
"This has motivated the investigation of datadriven approaches for robotic manipulation.",
"For example, deep reinforcement learning (RL) has made impressive progress in handling high-dimensional sensory inputs and solving complex tasks in recent years [7, 4, 15, 23] .",
"One of the main challenges in deploying deep RL methods in human-centric environment is interpretability.",
"For example, before executing a potentially dangerous task, it would be desirable to visualize what the robot is planning to do step by step, and intervene if necessary.",
"Addressing both data-driven modeling and interpretability, the visual planning (VP) paradigm seeks to learn a model of the environment from raw perception and then produce a visual plan of solving a task before actually executing a robot action.",
"Recently, several studies in manipulation and navigation [13, 29, 5, 22] have investigated VP approaches that first learn what is possible to do in a particular environment by self-supervised interaction, and then use the learned model to generate a visual plan from the current state to the goal, and finally apply visual servoing to follow the plan.",
"One particularly promising approach to VP is the semi-parametric topological memory (SPTM) method proposed by Savinov et al. [22] .",
"In SPTM, images collected offline are treated as nodes in a graph and represent the possible states of the system.",
"To connect nodes in this graph, an image classifier is trained to predict whether pairs of images were 'close' in the data or not, effectively learning which image transitions are feasible in a small number of steps.",
"The SPTM graph can then be used to generate a visual plan -a sequence of images between a pair of start and goal images -by directly searching the graph.",
"SPTM has several advantages, such as producing highly interpretable visual plans and the ability to plan long-horizon behavior.",
"However, since SPTM builds the visual plan directly from images in the data, when the environment changes -for example, the lighting varies, the camera is slightly moved, or other objects are displaced -SPTM requires recollecting images in the new environment; in this sense, SPTM does not generalize in a zero-shot sense.",
"Additionally, similar to [5] , we find that training the graph connectivity classifier as originally proposed by [22] requires extensive manual tuning.",
"Figure 1 : HTM illustration.",
"Top left: data collection.",
"In this illustration, the task is to move a green object between gray obstacles.",
"Data consists of multiple obstacle configurations (contexts), and images of random movement of the object in each configuration.",
"Bottom left: the elements of HTM.",
"A CVAE is trained to hallucinate images of the object and obstacles conditioned on the obstacle image context.",
"A connectivity energy model is trained to score pairs of images based on the feasibility of their transition.",
"Right: HTM visual planning.",
"Given a new context image and a pair of start and goal images, we first use the CVAE to hallucinate possible images of the object and obstacles.",
"Then, a connectivity graph (blue dotted lines) is computed based on the connectivity energy, and we plan for the shortest path from start to goal on this graph (orange solid line).",
"For executing the plan, a visual servoing controller is later used to track the image sequence.",
"In this work, we propose to improve both the robustness and zero-shot generalization of SPTM.",
"To tackle the issue of generalization, we assume that the environment is described using some context vector, which can be an image of the domain or any other observation data that contains enough information to extract a plan (see Figure 1 top left) .",
"We then train a conditional generative model that hallucinates possible states of the domain conditioned on the context vector.",
"Thus, given an unseen context, the generative model hallucinates exploration data without requiring actual exploration.",
"When building the connectivity graph with these hallucinated images, we replace the vanilla classifier used in SPTM with an energy-based model that employs a contrastive loss.",
"We show that this alteration drastically improves planning robustness and quality.",
"Finally, for planning, instead of connecting nodes in the graph according to an arbitrary threshold of the connectivity classifier, as in SPTM, we cast the planning as an inference problem, and efficiently search for the shortest path in a graph with weights proportional to the inverse of a proximity score from our energy model.",
"Empirically, we demonstrate that this provides much smoother plans and barely requires any hyperparameter tuning.",
"We term our approach Hallucinative Topological Memory (HTM).",
"A visual overview of our algorithm is presented in Figure 1 .",
"We evaluate our method on a set of simulated VP problems of moving an object between obstacles, which require long-horizon planning.",
"In contrast with prior work, which only focused on the success of the method in executing a task, here we also measure the interpretability of visual planning, through mean opinion scores of features such as image fidelity and feasibility of the image sequence.",
"In both measures, HTM outperforms state-of-the-art data-driven approaches such as visual foresight [4] and the original SPTM.",
"We propose a method that is visually interpretable and modular -we first hallucinate possible configurations, then compute a connectivity between them, and then plan.",
"Our HTM can generalize to unseen environments and improve visual plan quality and execution success rate over state-of-the-art VP methods.",
"Our results suggest that combining classical planning methods with data-driven perception can be helpful for long-horizon visual planning problems, and takes another step in bridging the gap between learning and planning.",
"In future work, we plan to combine HTM with Visual MPC for handling more complex objects, and use object-oriented planning for handling multiple objects.",
"Another interesting aspect is to improve planning by hallucinating samples conditioned on the start and goal configurations, which can help reduce the search space during planning."
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] | BkgF4kSFPB | true | [
"We propose Hallucinative Topological Memory (HTM), a visual planning algorithm that can perform zero-shot long horizon planning in new environments. "
] |
[
"Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role.",
"However, it has been observed that BN is costly due to the reduction operations.",
"In this paper, we propose alleviating the BN’s cost by using only a small fraction of data for mean & variance estimation at each iteration.",
"The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency.",
"We identify that the effectiveness expects less data correlation while the efficiency expects regular execution pattern.",
"To this end, we propose two categories of approach: sampling or creating few uncorrelated data for statistics’ estimation with certain strategy constraints.",
"The former includes “Batch Sampling (BS)” that randomly selects few samples from each batch and “Feature Sampling (FS)” that randomly selects a small patch from each feature map of all samples, and the latter is “Virtual Dataset Normalization (VDN)” that generates few synthetic random samples.",
"Accordingly, multi-way strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime.",
"All the proposed methods are comprehensively evaluated on various DNN models, where an overall training speedup by up to 21.7% on modern GPUs can be practically achieved without the support of any specialized libraries, and the loss of model accuracy and convergence rate are negligible.",
"Furthermore, our methods demonstrate powerful performance when solving the well-known “micro-batch normalization” problem in the case of tiny batch size.",
"Recent years, Deep Neural Networks (DNNs) have achieved remarkable success in a wide spectrum of domains such as computer vision BID16 and language modeling BID4 .",
"The success of DNNs largely relies on the capability of presentation benefit from the deep structure BID5 .",
"However, training a deep network is so difficult to converge that batch normalization (BN) has been proposed to solve it BID14 .",
"BN leverages the statistics (mean & variance) of mini-batches to standardize the activations.",
"It allows the network to go deeper without significant gradient explosion or vanishing BID23 BID14 .",
"Moreover, previous work has demonstrated that BN enables the use of higher learning rate and less awareness on the initialization BID14 , as well as produces mutual information across samples BID21 or introduces estimation noises BID2 for better generalization.",
"Despite BN's effectiveness, it is observed that BN introduces considerable training overhead due to the costly reduction operations.",
"The use of BN can lower the overall training speed (mini second per image) by >45% , especially in deep models.",
"To alleviate this problem, several methods were reported.",
"Range Batch Normalization (RBN) BID1 accelerated the forward pass by estimating the variance according to the data range of activations within each batch.",
"A similar approach, L 1 -norm BN (L1BN) , simplified both the forward and backward passes by replacing the L 2 -norm variance with its L 1 -norm version and re-derived the gradients for backpropagation (BP) training.",
"Different from the above two methods, Self-normalization BID15 provided another solution which totally eliminates the need of BN operation with an elaborate activation function called \"scaled exponential linear unit\" (SELU).",
"SELU can automatically force the activation towards zero mean and unit variance for better convergence.",
"Nevertheless, all of these methods are not sufficiently effective.",
"The strengths of L1BN & RBN are very limited since GPU has sufficient resources to optimize the execution speed of complex arithmetic operations such as root for the vanilla calculation of L 2 -norm variance.",
"Since the derivation of SELU is based on the plain convolutional network, currently it cannot handle other modern structures with skip paths like ResNet and DenseNet.In this paper, we propose mitigating BN's computational cost by just using few data to estimate the mean and variance at each iteration.",
"Whereas, the key challenge of this way lies at how to preserve the normalization effectiveness of the vanilla BN and improve the execution efficiency in the meantime, i.e. balance the effectiveness-efficiency trade-off.",
"We identify that the effectiveness preservation expects less data correlation and the efficiency improvement expects regular execution pattern.",
"This observation motivates us to propose two categories of approach to achieve the goal of effective and efficient BN: sampling or creating few uncorrelated data for statistics' estimation with certain strategy constraints.Sampling data includes \"Batch Sampling (BS)\" that randomly selects few samples from each batch and \"Feature Sampling (FS)\" that randomly selects a small patch from each feature map (FM) of all samples; creating data means \"Virtual Dataset Normalization (VDN)\" that generates few synthetic random samples, inspired by BID22 .",
"Consequently, multi-way strategies including intra-layer regularity, inter-layer randomness, and static execution graph during each epoch, are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime.",
"All the proposed approaches with single-use or joint-use are comprehensively evaluated on various DNN models, where the loss of model accuracy and convergence rate is negligible.",
"We practically achieve an overall training speedup by up to 21.7% on modern GPUs.",
"Note that any support of specialized libraries is not needed in our work, which is not like the network pruning BID32 or quantization BID12 requiring extra library for sparse or low-precision computation, respectively.",
"Most previous acceleration works targeted inference which remained the training inefficient BID26 BID20 BID19 BID31 BID9 , and the rest works for training acceleration were orthogonal to our approach BID7 BID29 .",
"Additionally, our methods further shows powerful performance when solving the well-known \"micro-batch normalization\" problem in the case of tiny batch sizes.In summary, the major contributions of this work are summarized as follows.•",
"We propose a new way to alleviate BN's computational cost by using few data to estimate the mean and variance, in which we identify that the key challenge is to balance the normalization effectiveness via less data correlation and execution efficiency via regular execution pattern.•",
"We propose two categories of approach to achieve the above goal: sampling (BS/FS) or creating (VDN) few uncorrelated data for statistics' estimation, in which multi-way strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. The",
"approaches can be used alone or jointly.• Various",
"benchmarks are evaluated, on which up to 21.7% practical acceleration is achieved for overall training on modern GPUs with negligible accuracy loss and without specialized library support.• Our methods",
"are also extended to the micro-BN problem and achieve advanced performance 1 .In order to make",
"this paper easier for understanding, we present the organization of the whole paper in FIG0 The activations in one layer for normalization can be described by a d-dimensional activation feature DISPLAYFORM0 , where for each feature we have DISPLAYFORM1 Note that in convolutional (Conv) layer, d is the number of FMs and m equals to the number of points in each FM across all the samples in one batch; while in fully-connected (FC) layer, d and m are the neuron number and batch size, respectively. BN uses the statistics",
"(mean E[ DISPLAYFORM2 of the intra-batch data for each feature to normalize activation by DISPLAYFORM3 where DISPLAYFORM4 are trainable parameters introduced to recover the representation capability, is a small constant to avoid numerical error, and DISPLAYFORM5 The detailed operations of a BN layer in the backward pass can be found in Appendix C. DISPLAYFORM6 Iter. per second. TAB3 ; (b)",
"usual optimization",
"of the",
"reduction operation using adder tree; (c) the computational graph of",
"BN in the forward pass (upper) and backward pass (lower); (d) the computation graph of BN",
"using few data for statistics' estimation in forward pass (upper) and backward pass (lower). x is neuronal activations, µ and",
"σ denote the mean and standard deviation of x within one batch, respectively, and is the summation operation.From FIG0 , we can see that adding BN will significantly slow down the training speed (iterations per second) by 32%-43% on ImageNet. The reason why BN is costly is that",
"it contains several \"reduction operations\", i.e. m j=1 . We offer more thorough data analysis",
"in Appendix E. If the reduction operations are not optimized, it's computational complexity should be O(m). With the optimized parallel algorithm",
"proposed in BID3 , the reduction operation is transformed to cascaded adders of depth of log(m) as shown in FIG0 . However, the computational cost is still",
"high since we usually have m larger than one million. As shown in FIG0 , the red \" \"s represent",
"operations that contain summations, which cause the BN inefficiency.",
"Motivated by the importance but high cost of BN layer, we propose using few data to estimate the mean and variance for training acceleration.",
"The key challenge towards this goal is how to balance the normalization effectiveness with much less data for statistics' estimation and the execution efficiency with irregular memory access.",
"To this end, we propose two categories of approach: sampling (BS/FS) or creating (VDN) few uncorrelated data, which can be used alone or jointly.",
"Specifically, BS randomly selects few samples from each batch, FS randomly selects a small patch from each FM of all samples, and VDN generates few synthetic random samples.",
"Then, multi-way strategies including intra-layer regularity, inter-layer randomness, and static execution graph are designed to reduce the data correlation and optimize the execution pattern in the meantime.",
"Comprehensive experiments evidence that the proposed approaches can achieve up to 21.7% overall training acceleration with negligible accuracy loss.",
"In addition, VDN can also be applied to the micro-BN scenario with advanced performance.",
"This paper preliminary proves the effectiveness and efficiency of BN using few data for statistics' estimation.",
"We emphasize that the training speedup is practically achieved on modern GPUs, and we do not need any support of specialized libraries making it easy-to-use.",
"Developing specialized kernel optimization deserves further investigation for more aggressive execution benefits."
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] | HkGsHj05tQ | true | [
"We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively."
] |
[
"Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud.",
"We propose Federated matched averaging (FedMA) algorithm designed for federated learning of modern neural network architectures e.g. convolutional neural networks (CNNs) and LSTMs.",
"FedMA constructs the shared global model in a layer-wise manner by matching and averaging hidden elements (i.e. channels for convolution layers; hidden states for LSTM; neurons for fully connected layers) with similar feature extraction signatures.",
"Our experiments indicate that FedMA outperforms popular state-of-the-art federated learning algorithms on deep CNN and LSTM architectures trained on real world datasets, while improving the communication efficiency.",
"Edge devices such as mobile phones, sensor networks or vehicles have access to a wealth of data.",
"However, due to concerns raised by data privacy, network bandwidth limitation, and device availability, it's unpractical to gather all local data to the data center and conduct centralized training.",
"To address these concerns, federated learning is emerging (McMahan et al., 2017; Li et al., 2019; Smith et al., 2017; Caldas et al., 2018; Bonawitz et al., 2019) to allow local clients to collaboratively train a shared global model.",
"The typical federated learning paradigm involves two stages:",
"(i) clients train models over their datasets independently",
"(ii) the data center uploads their locally trained models.",
"The data center then aggregates the received models into a shared global model.",
"One of the standard aggregation methods is FedAvg (McMahan et al., 2017) where parameters of local models are averaged element-wise with weights proportional to sizes of client datasets.",
"FedProx (Sahu et al., 2018 ) adds a proximal term for client local cost functions, which limits the impact of local updates by restricting them to be close to the global model.",
"Agnostic Federated Learning (AFL) (Mohri et al., 2019) , as another variant of FedAvg, optimizes a centralized distribution that is formed by a mixture of the client distributions.",
"One shortcoming of the FedAvg algorithm is that coordinate-wise averaging of weights may have drastic detrimental effect on the performance and hence hinders the communication efficiency.",
"This issue arises due to the permutation invariant nature of the neural network (NN) parameters, i.e. for any given NN there are many variations of it that only differ in the ordering of parameters and constitute local optima which are practically equivalent.",
"Probabilistic Federated Neural Matching (PFNM) (Yurochkin et al., 2019) addresses this problem by finding permutation of the parameters of the NNs before averaging them.",
"PFNM further utilizes Bayesian nonparametric machinery to adapt global model size to heterogeneity of the data.",
"As a result, PFNM has better performance and communication efficiency, however it was only developed for fully connected NNs and tested on simple architectures.",
"Our contribution In this work",
"(i) we demonstrate how PFNM can be applied to CNNs and LSTMs, however we find that it gives very minor improvement over weight averaging when applied to modern deep neural network architectures;",
"(ii) we propose Federated Matched Averaging (FedMA), a new layers-wise federated learning algorithm for modern CNNs and LSTMs utilizing matching and model size adaptation underpinnings of PFNM;",
"(iii) We empirically study FedMA with real datasets under the federated learning constraints.",
"In this paper, we presented FedMA, a new layer-wise federated learning algorithm designed for modern CNNs and LSTMs architectures utilizing probabilistic matching and model size adaptation.",
"We demonstrate the convergence rate and communication efficiency of FedMA empirically.",
"In the future, we would like to extend FedMA towards finding the optimal averaging strategy.",
"Making FedMa support more building blocks e.g. residual structures in CNNs and batch normalization layers is also of interest.",
"Table 4 : Detailed information of the VGG-9 architecture used in our experiments, all non-linear activation function in this architecture is ReLU; the shapes for convolution layers follows (Cin, Cout, c, c) In preprocessing the images in CIFAR-10 dataset, we follow the standard data augmentation and normalization process.",
"For data augmentation, random cropping and horizontal random flipping are used.",
"Each color channels are normalized with mean and standard deviation by µ r = 0.491372549, µ g = 0.482352941, µ b = 0.446666667, σ r = 0.247058824, σ g = 0.243529412, σ b = 0.261568627.",
"Each channel pixel is normalized by subtracting the mean value in this color channel and then divided by the standard deviation of this color channel."
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"Communication efficient federated learning with layer-wise matching"
] |
[
"We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. ",
"SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem. ",
"To capture this intuition, source samples are each given weights; these weights are solved for jointly with the source and target classification problems via a bilevel optimization scheme. ",
"The target therefore gets to choose the source samples which are most informative for its own classification task. ",
"Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting. ",
"SOSELETO may be applied to both classic transfer learning, as well as the problem of training on datasets with noisy labels; we show state of the art results on both of these problems.",
"Deep learning has demonstrated remarkable successes in tasks where large training sets are available.",
"Yet, its usefulness is still limited in many important problems that lack such data.",
"A natural question is then how one may apply the techniques of deep learning within these relatively data-poor regimes.",
"A standard approach that seems to work relatively well is transfer learning.",
"Despite its success, we claim that this approach misses an essential insight: some source examples are more informative than others for the target classification problem.",
"Unfortunately, we don't know a priori which source examples will be important.",
"Thus, we propose to learn this source filtering as part of an end-to-end training process.The resulting algorithm is SOSELETO: SOurce SELEction for Target Optimization.",
"Each training sample in the source dataset is given a weight, representing its importance.",
"A shared source/target representation is then optimized by means of a bilevel optimization.",
"In the interior level, the source minimizes its classification loss with respect to the representation and classification layer parameters, for fixed values of the sample weights.",
"In the exterior level, the target minimizes its classification loss with respect to both the source sample weights and its own classification layer.",
"The sample weights implicitly control the representation through the interior level.",
"The target therefore gets to choose the source samples which are most informative for its own classification task.",
"Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting, as the target does not directly control the representation parameters.",
"The entire processtraining of the shared representation, source and target classifiers, and source weights -happens simultaneously.Related Work The most common techniques for transfer learning are feature extraction e.g. and fine-tuning, e.g. BID8 .",
"An older survey of transfer learning techniques may be found in BID20 .",
"Domain adaptation BID23 involves knowledge transfer when the source and target classes are the same.",
"Earlier techniques aligned the source and target via matching of feature space statistics BID3 BID15 ; subsequent work used adversarial methods to improve the domain adaptation performance BID6 Tzeng et al., 2015; .",
"In this paper, we are more interested in transfer learning where the source and target classes are different.",
"BID16 ; BID21 BID1 b) address domain adaptation that is closer to our setting.",
"BID2 examines \"partial transfer learning\" in which there is partial overlap between source and target classes (often the target classes are a subset of the source).",
"This setting is also dealt with in BID0 .",
"Like SOSELETO, BID7 propose selecting a portion of the source dataset, however, the selection is done prior to training and is not end-to-end.",
"In , an adversarial loss aligns the source and target representations in a few-shot setting.Instance reweighting is a well studied technique for domain adaptation, demonstrated e.g. in Covariate Shift methods BID24 BID25 BID26 .",
"While in these works, the source and target label spaces are the same, we allow them to be different -even entirely non-overlapping.",
"Crucially, we do not make assumptions on the similarity of the distributions nor do we explicitly optimize for it.",
"The same distinction applies for the recent work of BID9 , and for the partial overlap assumption of Zhang et al. (2018) .",
"In addition, these two works propose an unsupervised approach, whereas our proposed method is completely supervised.Classification with noisy labels is a longstanding problem in the machine learning literature, see the review paper BID5 .",
"Within the realm of deep learning, it has been observed that with sufficiently large data, learning with label noise -without modification to the learning algorithms -actually leads to reasonably high accuracy BID10 BID28 BID22 BID4 .",
"We consider the setting where the large noisy dataset is accompanied by a small clean dataset.",
"BID27 introduced a noise layer into the CNN that adapts the output to align with the noisy label distribution.",
"Xiao et al. (2015) proposed to predict simultaneously the clean label and the type of noise; consider the same setting, but with additional information in the form of a knowledge graph on labels.",
"BID18 conditioned the gradient propagation on the agreement of two separate networks.",
"BID14 BID7 combine ideas of learning with label noise with instance reweighting.",
"We have presented SOSELETO, a technique for exploiting a source dataset to learn a target classification task.",
"This exploitation takes the form of joint training through bilevel optimization, in which the source loss is weighted by sample, and is optimized with respect to the network parameters; while the target loss is optimized with respect to these weights and its own classifier.",
"We have empirically shown the effectiveness of the algorithm on both learning with label noise, as well as transfer learning problems.",
"An interesting direction for future research involves incorporating an additional domain alignment term.",
"We note that SOSELETO is architecture-agnostic, and may be extended beyond classification tasks.",
"DISPLAYFORM0 end while SOSELETO consists of alternating the interior and exterior descent operations, along with the descent equations for the source and target classifiers φ s and φ t .",
"As usual, the whole operation is done on a mini-batch basis, rather than using the entire set; note that if processing is done in parallel, then source mini-batches are taken to be non-overlapping, so as to avoid conflicts in the weight updates.",
"A summary of SOSELETO algorithm appears in 1.",
"Note that the target derivatives ∂L t /∂θ and ∂L t /∂φ t are evaluated over a target mini-batch; we suppress this for clarity.In terms of time-complexity, we note that each iteration requires both a source batch and a target batch; assuming identical batch sizes, this means that SOSELETO requires about twice the time as the ordinary source classification problem.",
"Regarding space-complexity, in addition to the ordinary network parameters we need to store the source weights α.",
"Thus, the additional relative spacecomplexity required is the ratio of the source dataset size to the number of network parameters.",
"This is obviously problem and architecture dependent; a typical number might be given by taking the source dataset to be Imagenet ILSVRC-2012 (size 1.2M) and the architecture to be ResNeXt-101 Xie et al. (2017) (size 44.3M parameters), yielding a relative space increase of about 3%."
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] | r1lqA-OSvV | true | [
"Learning with limited training data by exploiting \"helpful\" instances from a rich data source. "
] |
[
"Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known. ",
"Inspired by the recent work in continuous-time minimizers, our work models the common trust region methods with the exploration-exploitation using a dynamical system coupling a pair of dynamical processes.",
"While the first exploration process searches the minimum of the blackbox function through minimizing a time-evolving surrogation function, another exploitation process updates the surrogation function time-to-time using the points traversed by the exploration process.",
"The efficiency of derivative-free optimization thus depends on ways the two processes couple.",
"In this paper, we propose a novel dynamical system, namely \\ThePrev---\\underline{S}tochastic \\underline{H}amiltonian \\underline{E}xploration and \\underline{E}xploitation, that surrogates the subregions of blackbox function using a time-evolving quadratic function, then explores and tracks the minimum of the quadratic functions using a fast-converging Hamiltonian system.",
"The \\ThePrev\\ algorithm is later provided as a discrete-time numerical approximation to the system.",
"To further accelerate optimization, we present \\TheName\\ that parallelizes multiple \\ThePrev\\ threads for concurrent exploration and exploitation.",
"Experiment results based on a wide range of machine learning applications show that \\TheName\\ outperform a boarder range of derivative-free optimization algorithms with faster convergence speed under the same settings.",
"Derivative-free optimization (DFO) techniques BID31 , such as Bayesian optimization algorithms BID43 BID24 , non-differentiable coordinate descent BID4 , natural gradient method BID12 BID14 , and natural evolution strategies BID41 , have been widely used for black-box function optimization.",
"DFO techniques have been viewed as one of promising solutions, when the first-order/higher-order derivatives of the objective functions are not available.",
"For example, to train large-scale machine learning models, parameter tuning is sometimes required.",
"The problem to find the best parameters from the high-dimensional parameter space is frequently formalized as a black-box optimization problem, as the function that maps the specific parameter settings to the performance of models is not known BID11 BID9 BID48 BID21 .",
"The evaluation of the black-box function is often computationally expensive, and there thus needs DFO algorithms to converge fast with global/local minimum guarantee.Backgrounds.",
"To ensure the performance of DFO algorithms, a series of pioneering work has been done BID5 BID36 BID16 BID2 BID11 .",
"Especially, Powell et al. (Powell, 1964; BID33 proposed Trust-Region methods that intends to \"surrogate\" the DFO solutions through exploring the minimum in the trust regions of the blackbox objective functions, where the trust regions are tightly approximated using model functions (e.g., quadratic functions or Gaussian process) via interpolation.",
"Such two processes for exploration and exploitation are usually alternatively iterated, so as to pursue the global/local minimum BID2 .",
"With exploration and exploitation BID7 , a wide range of algorithms have been proposed using trust region for DFO surrogation BID34 BID38 BID43 BID45 BID42 BID46 BID39 BID0 BID30 BID23 BID1 .Technical",
"Challenges. Though trust",
"region methods have been successfully used for derivative-free optimization for decades, the drawbacks of these methods are still significant:• The computational and storage complexity for (convex) surrogates is extremely high. To approximate",
"the trust regions of blackbox functions, quadratic functions BID34 BID39 and Gaussian process BID43 BID46 BID23 are frequently used as (convex) surrogates. However, fitting",
"the quadratic functions and Gaussian process through interpolation is quite time-consuming with high sample complexity. For example, using",
"quadratic functions as surrogates (i.e., approximation to the second-order Taylor's expansion) needs to estimate the gradient and inverse Hessian matrix BID34 BID39 , where a large number of samples are required to avoid ill-conditioned inverse Hessian approximation; while the surrogate function in GP is nonconvex, which is even more sophisticated to optimize.• The convergence of",
"trust region methods cannot be guaranteed for high-dimensional nonconvex DFO. Compared to the derivative-based",
"algorithms such as stochastic gradient descent and accelerated gradient methods BID3 BID44 , the convergence of DFO algorithms usually are not theoretically guaranteed. Jamieson et al. BID16 provided the",
"lower bound for algorithms based on boolean-based comparison of function evaluation. It shows that DFO algorithms can converge",
"at Ω(1/ √ T ) rate in the best case (T refers to the total number of iterations), without assumptions on convexity and smoothness, even when the evaluation of black-box function is noisy.Our Intuitions. To tackle the technical challenges, we are",
"motivated to study novel trust region methods with following properties 1. Low-complexity Quadratic Surrogates with",
"Limited Memory. To lower the computational complexity, we propose",
"to use quadratic functions with identity Hessian matrices as surrogates. Rather than incorporating all evaluated samples in",
"quadratic form approximation, our algorithm only works with the most-recently evaluated sample points. In this way, the memory consumption required can be",
"further reduced. However, the use of identity Hessian matrices for quadratic",
"form loses the information about the distribution (e.g., Fisher information or covariance BID13 ) of evaluated sample points. 2. Fast Quadratic Exploration with Stochastic Hamiltonian Dynamical",
"Systems. Though it is difficult to improve the convergence rate of the DFO algorithms",
"in general nonconvex settings with less oracle calls (i.e., times of function evaluation), one can make the exploration over the quadratic trust region even faster. Note that exploration requires to cover a trust region rather than running on",
"the fastest path (e.g., the gradient flow BID15 ) towards the minimum of trust region. In this case, there needs an exploration mechanism traversing the whole quadratic",
"trust region in a fast manner and (asymptotically) approaching to the minimum. FIG0 illustrates the examples of exploration processes over the quadratic region",
"via its gradient flows (i.e., gradient descent) or using Hamiltonian dynamics with gradients BID25 as well as their stochastic variants with explicit perturbation, all in the same length of time. It shows that the stochastic Hamiltonian dynamics (shown in FIG0 (d)) can well balance",
"the needs of fast-approaching the minimum while sampling the quadratic region with its trajectories. Compared to the (stochastic) gradient flow, which leads to the convergence to the minimum",
"in the fast manner, the stochastic Hamiltonian system are expected to well explore the quadratic trust region with the convergence kept. Inspired by theoretical convergence consequences of Hamiltonian dynamics with Quadratic form",
"BID44 BID25 , we propose to use stochastic Hamiltonian dynamical system for exploring the quadratic surrogates. 3. Multiple Quadratic Trust Regions with Parallel Exploration-Exploitation. Instead of using",
"one quadratic cone as the surrogate, our method constructs the trust regions",
"using multiple quadratic surrogates, where every surrogate is centered by one sample point. In this way, the information of multiple sample points can be still preserved. Further, to enjoy",
"the speedup of parallel computation, the proposed method can be accelerated through",
"exploring the minimum from multiple trust regions (using multiple Hamiltonian dynamical sys- Our work is inspired by the recent progress in the continuous-time convex minimizers BID44 BID15 BID47 on convex functions, where the optimization algorithms are considered as the discrete-time numerical approximation to some (stochastic) ordinary differential equations (ODEs) or dynamics, such as Itô processes for SGD algorithms BID15 or Hamiltonian systems for Nesterov's accelerated SGD BID44 . We intend to first study the new ODE and dynamical system as a continuous-time DFO minimizer that addresses",
"above three research issues. With the new ODE, we aim at proposing the discrete-time approximation as the algorithms for black-box optimization",
".Our Contributions. Specifically, we make following contributions. (1) To address the three technical challenges, a continuous-time minimizer",
"for derivative-free optimization based on a Hamiltonian",
"system coupling two processes for exploration and exploitation respectively. (2) Based on the proposed dynamical system, an algorithm, namely SHE 2 -Stochastic Hamiltonian Exploration and Exploitation, as a",
"discrete-time version of the proposed dynamical system, as well as P-SHE 2 that parallelizes SHE 2 for acceleration. (3) With the proposed algorithms, a series of experiments to evaluate SHE 2 and P-SHE 2 using real-world applications. The two algorithms",
"outperform a wide range of DFO algorithms with better convergence. To the best of our knowledge, this work is the first",
"to use a Hamiltonian system with coupled process for DFO algorithm design and analysis",
".",
"In this paper, we present SHE 2 and P-SHE 2 -two derivative-free optimization algorithms that leverage a Hamiltonian exploration and exploitation dynamical systems for black-box function optimization.",
"Under mild condition SHE 2 algorithm behaves as a discrete-time approximation to a Nestereov's scheme ODE BID44 over the quadratic trust region of the blackbox function.",
"Moreover, we propose P-SHE 2 to further accelerate the minimum search through parallelizing multiple SHE 2 -alike search threads with simple synchronization.Compared to the existing trust region methods, P-SHE 2 uses multiple quadratic trust regions with multiple (coupled) stochastic Hamiltonian dynamics to accelerate the exploration-exploitation processes, while avoiding the needs of Hessian matrix estimation for quadratic function approximation.",
"Instead of interpolating sampled points in one quadratic function, P-SHE 2 defacto constructs one quadratic surrogate (with identity Hessian) for each sampled point and leverages parallel search threads with parallel black-box function evaluation to boost the performance.",
"Experiment results show that P-SHE 2 can compete a wide range of DFO algorithms to minimize nonconvex benchmark functions, train supervised learning models via parameter optimization, and fine-tune deep neural networks via hyperparameter optimization.",
"Here we provide a short discussion on the convergence rate of the algorithm SHE2.",
"In the previous appendix we have demonstrated that the system (4) converges via two steps.Step 1 in Lemma 1 shows that the differential equation modeling Nesterov's accelerated gradient descent (see BID44 ) helps the process X(t) to \"catch up\" with the minimum point Y (t) on its path.Step 2 in Lemma 2 shows that when t → ∞ the noise term ζ(t) helps the process X(t) to reach local"
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] | rklEUjR5tm | true | [
"a new derivative-free optimization algorithms derived from Nesterov's accelerated gradient methods and Hamiltonian dynamics"
] |
[
" Binarized Neural networks (BNNs) have been shown to be effective in improving network efficiency during the inference phase, after the network has been trained.",
"However, BNNs only binarize the model parameters and activations during propagations.",
"Therefore, BNNs do not offer significant efficiency improvements during training, since the gradients are still propagated and used with high precision. \n \n ",
"We show there is no inherent difficulty in training BNNs using \"Binarized BackPropagation\" (BBP), in which we also binarize the gradients.",
"To avoid significant degradation in test accuracy, we simply increase the number of filter maps in a each convolution layer.",
"Using BBP on dedicated hardware can potentially significantly improve the execution efficiency (\\emph{e.g.}, reduce dynamic memory footprint, memory bandwidth and computational energy) and speed up the training process with an appropriate hardware support, even after such an increase in network size.",
"Moreover, our method is ideal for distributed learning as it reduces the communication costs significantly (e.g., by ~32).",
"Using this method, we demonstrate a minimal loss in classification accuracy on several datasets and topologies."
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] | ryKRRsm0Z | false | [
"Binarized Back-Propagation all you need for completely binarized training is to is to inflate the size of the network"
] |
[
"The weight initialization and the activation function of deep neural networks have a crucial impact on the performance of the training procedure.",
"An inappropriate selection can lead to the loss of information of the input during forward propagation and the exponential vanishing/exploding of gradients during back-propagation.",
"Understanding the theoretical properties of untrained random networks is key to identifying which deep networks may be trained successfully as recently demonstrated by Schoenholz et al. (2017) who showed that for deep feedforward neural networks only a specific choice of hyperparameters known as the `edge of chaos' can lead to good performance.\n",
"We complete this analysis by providing quantitative results showing that, for a class of ReLU-like activation functions, the information propagates indeed deeper for an initialization at the edge of chaos.",
"By further extending this analysis, we identify a class of activation functions that improve the information propagation over ReLU-like functions.",
"This class includes the Swish activation, $\\phi_{swish}(x) = x \\cdot \\text{sigmoid}(x)$, used in Hendrycks & Gimpel (2016),\n",
"Elfwing et al. (2017) and Ramachandran et al. (2017).",
"This provides a theoretical grounding for the excellent empirical performance of $\\phi_{swish}$ observed in these contributions.",
"We complement those previous results by illustrating the benefit of using a random initialization on the edge of chaos in this context.",
"Deep neural networks have become extremely popular as they achieve state-of-the-art performance on a variety of important applications including language processing and computer vision; see, e.g., BID8 .",
"The success of these models has motivated the use of increasingly deep networks and stimulated a large body of work to understand their theoretical properties.",
"It is impossible to provide here a comprehensive summary of the large number of contributions within this field.",
"To cite a few results relevant to our contributions, BID11 have shown that neural networks have exponential expressive power with respect to the depth while BID14 obtained similar results using a topological measure of expressiveness.We follow here the approach of BID14 and BID16 by investigating the behaviour of random networks in the infinite-width and finite-variance i.i.d. weights context where they can be approximated by a Gaussian process as established by BID10 and BID9 .In",
"this paper, our contribution is two-fold. Firstly",
", we provide an analysis complementing the results of BID14 and BID16 and show that initializing a network with a specific choice of hyperparameters known as the 'edge of chaos' is linked to a deeper propagation of the information through the network. In particular",
", we establish that for a class of ReLU-like activation functions, the exponential depth scale introduced in BID16 is replaced by a polynomial depth scale. This implies",
"that the information can propagate deeper when the network is initialized on the edge of chaos. Secondly, we",
"outline the limitations of ReLU-like activation functions by showing that, even on the edge of chaos, the limiting Gaussian Process admits a degenerate kernel as the number of layers goes to infinity. Our main result",
"(4) gives sufficient conditions for activation functions to allow a good 'information flow' through the network (Proposition 4) (in addition to being non-polynomial and not suffering from the exploding/vanishing gradient problem). These conditions",
"are satisfied by the Swish activation φ swish (x) = x · sigmoid(x) used in BID4 , BID2 and BID15 . In recent work,",
"BID15 used automated search techniques to identify new activation functions and found experimentally that functions of the form φ(x) = x · sigmoid(βx) appear to perform indeed better than many alternative functions, including ReLU. Our paper provides",
"a theoretical grounding for these results. We also complement",
"previous empirical results by illustrating the benefits of an initialization on the edge of chaos in this context. All proofs are given",
"in the Supplementary Material.",
"We have complemented here the analysis of BID16 which shows that initializing networks on the EOC provides a better propagation of information across layers.",
"In the ReLU case, such an initialization corresponds to the popular approach proposed in BID3 .",
"However, even on the EOC, the correlations still converge to 1 at a polynomial rate for ReLU networks.",
"We have obtained a set of sufficient conditions for activation functions which further improve information propagation when the parameters (σ b , σ w ) are on the EOC.",
"The Tanh activation satisfied those conditions but, more interestingly, other functions which do not suffer from the vanishing/exploding gradient problems also verify them.",
"This includes the Swish function used in BID4 , BID2 and promoted in BID15 but also ELU Clevert et al. (2016) .Our",
"results have also interesting implications for Bayesian neural networks which have received renewed attention lately; see, e.g., Hernandez-Lobato & Adams FORMULA4 and BID9 . They",
"show that if one assigns i.i.d. Gaussian prior distributions to the weights and biases, the resulting prior distribution will be concentrated on close to constant functions even on the EOC for ReLU-like activation functions. To obtain",
"much richer priors, our results indicate that we need to select not only parameters (σ b , σ w ) on the EOC but also an activation function satisfying Proposition 4."
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"How to effectively choose Initialization and Activation function for deep neural networks"
] |
[
"The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships.",
"Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes.",
"To this end, we introduce contextual decomposition (CD), an interpretation algorithm for analysing individual predictions made by standard LSTMs, without any changes to the underlying model.",
"By decomposing the output of a LSTM, CD captures the contributions of combinations of words or variables to the final prediction of an LSTM.",
"On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction.",
"Using the phrase-level labels in SST, we also demonstrate that CD is able to successfully extract positive and negative negations from an LSTM, something which has not previously been done.",
"In comparison with simpler linear models, techniques from deep learning have achieved impressive accuracy by effectively learning non-linear interactions between features.",
"However, due to our inability to describe the learned interactions, this improvement in accuracy has come at the cost of state of the art predictive algorithms being commonly regarded as black-boxes.",
"In the domain of natural language processing (NLP), Long Short Term Memory networks (LSTMs) BID2 have become a basic building block, yielding excellent performance across a wide variety of tasks (Sutskever et al., 2014) BID10 BID7 , while remaining largely inscrutable.In this work, we introduce contextual decomposition (CD), a novel interpretation method for explaining individual predictions made by an LSTM without any modifications to the underlying model.",
"CD extracts information about not only which words contributed to a LSTM's prediction, but also how they were combined in order to yield the final prediction.",
"By mathematically decomposing the LSTM's output, we are able to disambiguate the contributions made at each step by different parts of the sentence.To validate the CD interpretations extracted from an LSTM, we evaluate on the problem of sentiment analysis.",
"In particular, we demonstrate that CD is capable of identifying words and phrases of differing sentiment within a given review.",
"CD is also used to successfully extract positive and negative negations from an LSTM, something that has not previously been done.",
"As a consequence of this analysis, we also show that prior interpretation methods produce scores which have document-level information built into them in complex, unspecified ways.",
"For instance, prior work often identifies strongly negative phrases contained within positive reviews as neutral, or even positive.",
"In this paper, we have proposed contextual decomposition (CD), an algorithm for interpreting individual predictions made by LSTMs without modifying the underlying model.",
"In both NLP and general applications of LSTMs, CD produces importance scores for words (single variables in general), phrases (several variables together) and word interactions (variable interactions).",
"Using two sentiment analysis datasets for empirical validation, we first show that for information also produced by prior methods, such as word-level scores, our method compares favorably.",
"More importantly, we then show that CD is capable of identifying phrases of varying sentiment, and extracting meaningful word (or variable) interactions.",
"This movement beyond word-level importance is critical for understanding a model as complex and highly non-linear as LSTMs.",
"6 APPENDIX Figure 4: Logistic regression coefficients versus coefficients extracted from an LSTM on SST.",
"We include a least squares regression line.",
"Stronger linear relationships in the plots correspond to better interpretation techniques."
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] | rkRwGg-0Z | true | [
"We introduce contextual decompositions, an interpretation algorithm for LSTMs capable of extracting word, phrase and interaction-level importance score"
] |
[
"Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.",
"This is due to the difficulty of estimating the phase of clean speech.",
"To improve speech enhancement performance, we tackle the phase estimation problem in three ways.",
"First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms.",
"Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks.",
"Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure.",
"Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement.",
"Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid.",
"Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin.",
"Speech enhancement is one of the most important and challenging tasks in speech applications where the goal is to separate clean speech from noise when noisy speech is given as an input.",
"As a fundamental component for speech-related systems, the applications of speech enhancement vary from speech recognition front-end modules to hearing aid systems for the hearing-impaired BID36 BID32 .Due",
"to recent advances in deep learning, the speech enhancement task has been able to reach high levels in performance through significant improvements. When",
"using audio signals with deep learning models, it has been a common practice to transform a time-domain waveform to a time-frequency (TF) representation (i.e. spectrograms) via short-time-Fourier-transform (STFT). Spectrograms",
"are represented as complex matrices, which are normally decomposed into magnitude and phase components to be used in real-valued networks. In tasks involving",
"audio signal reconstruction, such as speech enhancement, it is ideal to perform correct estimation of both components. Unfortunately, complex-valued",
"phase has been often neglected due to the difficulty of its estimation. This has led to the situation",
"where most approaches focus only on the estimation of a magnitude spectrogram while reusing noisy phase information BID9 BID39 BID7 BID15 BID26 . However, reusing phase from noisy",
"speech has clear limitations, particularly under extremely noisy conditions, in other words, when signal-to-noise ratio (SNR) is low. This can be easily verified by simply",
"using the magnitude spectrogram of clean speech with the phase spectrogram of noisy speech to reconstruct clean speech, as illustrated in Fig A popular approach to speech enhancement is to optimize a mask which produces a spectrogram of clean speech when applied to noisy input audio. One of the first mask-based attempts",
"to perform the task by incorporating phase information was the proposal of the phase-sensitive mask (PSM) . Since the performance of PSM was limited",
"because of reusing noisy phase, later studies proposed using complex-valued ratio mask (cRM) to directly optimize on complex values BID37 BID3 . We found this direction promising for phase",
"estimation because it has been shown that a complex ideal ratio mask (cIRM) is guaranteed to give the best oracle performance out of other ideal masks such as ideal binary masks, ideal ratio masks, or PSMs . Moreover, this approach jointly estimates magnitude",
"and phase, removing the need of separate models. To estimate a complex-valued mask, a natural desire",
"would be to use an architecture which can handle complex-domain operations. Recent work gives a solution to this by providing deep",
"learning building blocks adapted to complex arithmetic BID28 .In this paper, we build upon previous studies to design",
"a new complex-valued masking framework, based on a proposed variant of U-Net BID19 , named Deep Complex U-Net (DCUnet). In our proposed framework, DCUnet is trained to estimate",
"a complex ratio mask represented in polar coordinates with prior knowledge observable from ideal complex-valued masks. With the complex-valued estimation of clean speech, we can",
"use inverse short-time-Fourier-transform (ISTFT) to convert a spectrogram into a time-domain waveform. Taking this as an advantage, we introduce a novel loss function",
"which directly optimizes source-to-distortion ratio (SDR) BID31 , a quantitative evaluation measure widely used in many source separation tasks.Our contributions can be summarized as follows:1. We propose a new neural architecture, Deep Complex U-Net, which",
"combines the advantages of both deep complex networks and U-Net, yielding state-of-the-art performance.2. While pointing out limitations of current masking strategies, we",
"design a new complexvalued masking method based on polar coordinates.3. We propose a new loss function weighted-SDR loss, which directly",
"optimizes a well known quantitative evaluation measure.",
"In this paper, we proposed Deep Complex U-Net which combines two models to deal with complexvalued spectrograms for speech enhancement.",
"In doing so, we designed a new complex-valued masking method optimized with a novel loss function, weighted-SDR loss.",
"Through ablation studies, we showed that the proposed approaches are effective for more precise phase estimation, resulting in state-of-the-art performance for speech enhancement.",
"Furthermore, we conducted both quantitative and qualitative studies and demonstrated that the proposed method is consistently superior to the previously proposed algorithms.",
"n the near future, we plan to apply our system to various separation tasks such as speaker separation or music source separation.",
"Another important direction is to extend the proposed model to deal with multichannel audio since accurate estimation of phase is even more critical in multichannel environments BID34 .",
"Apart from separation, our approach can be generalized to various audio-related tasks such as dereverberation, bandwidth extension or phase estimation networks for text-to-speech systems.",
"Taking advantage of sequence modeling, it may also be interesting to find further extensions with complex-valued LSTMs BID1 BID38 ."
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"This paper proposes a novel complex masking method for speech enhancement along with a loss function for efficient phase estimation."
] |
[
"All living organisms struggle against the forces of nature to carve out niches where\n",
"they can maintain relative stasis.",
"We propose that such a search for order amidst\n",
"chaos might offer a unifying principle for the emergence of useful behaviors in\n",
"artificial agents.",
"We formalize this idea into an unsupervised reinforcement learning\n",
"method called surprise minimizing RL (SMiRL).",
"SMiRL trains an agent with the\n",
"objective of maximizing the probability of observed states under a model trained on\n",
"all previously seen states.",
"The resulting agents acquire several proactive behaviors\n",
"to seek and maintain stable states such as balancing and damage avoidance, that\n",
"are closely tied to the affordances of the environment and its prevailing sources\n",
"of entropy, such as winds, earthquakes, and other agents.",
" We demonstrate that\n",
"our surprise minimizing agents can successfully play Tetris, Doom, and control\n",
"a humanoid to avoid falls, without any task-specific reward supervision.",
" ",
"We\nfurther show that SMiRL can be used as an unsupervised pre-training objective\n",
"that substantially accelerates subsequent reward-driven learning",
"The general struggle for existence of animate beings is not a struggle for raw materials, nor for energy, but a struggle for negative entropy.",
"(Ludwig Boltzmann, 1886)",
"All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them (Boltzmann, 1886; Schrödinger, 1944; Schneider & Kay, 1994; Friston, 2009) .",
"Humans, for example, go to great lengths to shield themselves from surprise -we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm.",
"The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats.",
"Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents?",
"Our method therefore addresses the unsupervised reinforcement learning problem: how might an agent in an environment acquire complex behaviors and skills with no external supervision?",
"This central problem in artificial intelligence has evoked several candidate solutions, largely focusing on novelty-seeking behaviors (Schmidhuber, 1991; Lehman & Stanley, 2011; Still & Precup, 2012; Bellemare et al., 2016; Houthooft et al., 2016; Pathak et al., 2017) .",
"In simulated worlds, such as video games, novelty-seeking intrinsic motivation can lead to interesting and meaningful behavior.",
"However, we argue that these sterile environments are fundamentally lacking compared to the real world.",
"In the real world, natural forces and other agents offer bountiful novelty.",
"The second law of thermodynamics stipulates ever-increasing entropy, and therefore perpetual novelty, without even requiring any agent intervention.",
"Instead, the challenge in natural environments is homeostasis: discovering behaviors that enable agents to maintain an equilibrium, for example to preserve their bodies, their homes, and avoid predators and hunger.",
"Even novelty seeking behaviors may emerge naturally as a means to maintain homeostasis: an agent that is curious and forages for food in unlikely places might better satisfy its hunger.",
"In natural environments (left), an inactive agent will experience a wide variety of states.",
"By reasoning about future surprise, a SMiRL agent can take actions that temporarily increase surprise but reduce it in the long term.",
"For example, building a house initially results in novel states, but once it is built, the house allows the agent to experience a more stable and surprise-free environment.",
"On the right we show an interpretation of the agent interaction loop using SMiRL.",
"When the agent observes a state, it updates it belief p(s) over states.",
"Then, the action policy π(a|s, θ) is conditioned on this belief and maximizes the expected likelihood of the next state under its belief.",
"We formalize allostasis as an objective for reinforcement learning based on surprise minimization (SMiRL).",
"In highly entropic and dynamic environments with undesirable forms of novelty, minimizing surprise (i.e., minimizing novelty) causes agents to naturally seek a stable equilibrium.",
"Natural environments with winds, earthquakes, adversaries, and other disruptions already offer a steady stream of novel stimuli, and an agent that minimizes surprise in these environments will act and explore in order to find the means to maintain a stable equilibrium in the face of these disturbances.",
"SMiRL is simple to describe and implement: it works by maintaining a density p(s) of visited states and training a policy to act such that future states have high likelihood under p(s).",
"This interaction scheme is shown in Figure 1 (right) Across many different environments, with varied disruptive forces, and in agents with diverse embodiments and action spaces, we show that this simple approach induces useful equilibrium-seeking behaviors.",
"We show that SMiRL agents can solve Tetris, avoid fireballs in Doom, and enable a simulated humanoid to balance and locomote, without any explicit task reward.",
"More pragmatically, we show that SMiRL can be used together with a task reward to accelerate standard reinforcement learning in dynamic environments, and can provide a simple mechanism for imitation learning.",
"SMiRL holds promise for a new kind of unsupervised RL method that produces behaviors that are closely tied to the prevailing disruptive forces, adversaries, and other sources of entropy in the environment.",
"Videos of our results are available at https://sites.google.com/view/surpriseminimization",
"We presented an unsupervised reinforcement learning method based on minimization of surprise.",
"We show that surprise minimization can be used to learn a variety of behaviors that maintain \"homeostasis,\" putting the agent into stable and sustainable limit cycles in its environment.",
"Across a range of tasks, these stable limit cycles correspond to useful, semantically meaningful, and complex behaviors: clearing rows in Tetris, avoiding fireballs in VizDoom, and learning to balance and hop forward with a bipedal robot.",
"The key insight utilized by our method is that, in contrast to simple simulated domains, realistic environments exhibit dynamic phenomena that gradually increase entropy over time.",
"An agent that resists this growth in entropy must take active and coordinated actions, thus learning increasingly complex behaviors.",
"This stands in stark contrast to commonly proposed intrinsic exploration methods based on novelty, which instead seek to visit novel states and increase entropy.",
"Besides fully unsupervised reinforcement learning, where we show that our method can give rise to intelligent and complex policies, we also illustrate several more pragmatic applications of our approach.",
"We show that surprise minimization can provide a general-purpose risk aversion reward that, when combined with task rewards, can improve learning in environments where avoiding catastrophic (and surprising) outcomes is desirable.",
"We also show that SMiRL can be adapted to perform a rudimentary form of imitation.",
"Our investigation of surprise minimization suggests a number of directions for future work.",
"The particular behavior of a surprise minimizing agent is strongly influenced by the particular choice of state representation: by including or excluding particular observation modalities, the agent will be more or less surprised.",
"where s is a single state, θ i is the sample mean calculated from D t indicating the proportion of datapoints where location i has been occupied by a block, and s i is a binary variable indicating the presence of a block at location i.",
"If the blocks stack to the top, the game board resets, but the episode continues and the dataset D t continues to accumulate states.",
"SMiRL on VizDoom and Humanoid.",
"In these environments the observations placed in the buffer are downsampled 10 × 13 single-frame observations for VizDoom environments and the full state for the Humanoid environments.",
"We model p(s) as an independent Gaussian distribution for each dimension in the observation.",
"Then, the SMiRL reward can be computed as:",
"where s is a single state, µ i and σ i are calculated as the sample mean and standard deviation from D t and s i is the i th observation feature of s."
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"Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy."
] |
[
"Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently.",
"A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs.",
"Alternatively, a more recent approach to meta-learning aims to acquire deep representations that can be effectively fine-tuned, via standard gradient descent, to new tasks.",
"In this paper, we consider the meta-learning problem from the perspective of universality, formalizing the notion of learning algorithm approximation and comparing the expressive power of the aforementioned recurrent models to the more recent approaches that embed gradient descent into the meta-learner.",
"In particular, we seek to answer the following question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm?",
"We find that this is indeed true, and further find, in our experiments, that gradient-based meta-learning consistently leads to learning strategies that generalize more widely compared to those represented by recurrent models.",
"Deep neural networks that optimize for effective representations have enjoyed tremendous success over human-engineered representations.",
"Meta-learning takes this one step further by optimizing for a learning algorithm that can effectively acquire representations.",
"A common approach to metalearning is to train a recurrent or memory-augmented model such as a recurrent neural network to take a training dataset as input and then output the parameters of a learner model (Schmidhuber, 1987; Bengio et al., 1992; Li & Malik, 2017a; BID0 .",
"Alternatively, some approaches pass the dataset and test input into the model, which then outputs a corresponding prediction for the test example (Santoro et al., 2016; Duan et al., 2016; Wang et al., 2016; Mishra et al., 2018) .",
"Such recurrent models are universal learning procedure approximators, in that they have the capacity to approximately represent any mapping from dataset and test datapoint to label.",
"However, depending on the form of the model, it may lack statistical efficiency.In contrast to the aforementioned approaches, more recent work has proposed methods that include the structure of optimization problems into the meta-learner (Ravi & Larochelle, 2017; Finn et al., 2017a; Husken & Goerick, 2000) .",
"In particular, model-agnostic meta-learning (MAML) optimizes only for the initial parameters of the learner model, using standard gradient descent as the learner's update rule (Finn et al., 2017a) .",
"Then, at meta-test time, the learner is trained via gradient descent.",
"By incorporating prior knowledge about gradient-based learning, MAML improves on the statistical efficiency of black-box meta-learners and has successfully been applied to a range of meta-learning problems (Finn et al., 2017a; b; Li et al., 2017) .",
"But, does it do so at a cost?",
"A natural question that arises with purely gradient-based meta-learners such as MAML is whether it is indeed sufficient to only learn an initialization, or whether representational power is in fact lost from not learning the update rule.",
"Intuitively, we might surmise that learning an update rule is more expressive than simply learning an initialization for gradient descent.",
"In this paper, we seek to answer the following question: does simply learning the initial parameters of a deep neural network have the same representational power as arbitrarily expressive meta-learners that directly ingest the training data at meta-test time?",
"Or, more concisely, does representation combined with standard gradient descent have sufficient capacity to constitute any learning algorithm?We",
"analyze this question from the standpoint of the universal function approximation theorem. We",
"compare the theoretical representational capacity of the two meta-learning approaches: a deep network updated with one gradient step, and a meta-learner that directly ingests a training set and test input and outputs predictions for that test input (e.g. using a recurrent neural network). In",
"studying the universality of MAML, we find that, for a sufficiently deep learner model, MAML has the same theoretical representational power as recurrent meta-learners. We",
"therefore conclude that, when using deep, expressive function approximators, there is no theoretical disadvantage in terms of representational power to using MAML over a black-box meta-learner represented, for example, by a recurrent network.Since MAML has the same representational power as any other universal meta-learner, the next question we might ask is: what is the benefit of using MAML over any other approach? We",
"study this question by analyzing the effect of continuing optimization on MAML performance. Although",
"MAML optimizes a network's parameters for maximal performance after a fixed small number of gradient steps, we analyze the effect of taking substantially more gradient steps at meta-test time. We find",
"that initializations learned by MAML are extremely resilient to overfitting to tiny datasets, in stark contrast to more conventional network initialization, even when taking many more gradient steps than were used during meta-training. We also",
"find that the MAML initialization is substantially better suited for extrapolation beyond the distribution of tasks seen at meta-training time, when compared to meta-learning methods based on networks that ingest the entire training set. We analyze",
"this setting empirically and provide some intuition to explain this effect.",
"In this paper, we show that there exists a form of deep neural network such that the initial weights combined with gradient descent can approximate any learning algorithm.",
"Our findings suggest that, from the standpoint of expressivity, there is no theoretical disadvantage to embedding gradient descent into the meta-learning process.",
"In fact, in all of our experiments, we found that the learning strategies acquired with MAML are more successful when faced with out-of-domain tasks compared to recurrent learners.",
"Furthermore, we show that the representations acquired with MAML are highly resilient to overfitting.",
"These results suggest that gradient-based meta-learning has a num-ber of practical benefits, and no theoretical downsides in terms of expressivity when compared to alternative meta-learning models.",
"Independent of the type of meta-learning algorithm, we formalize what it means for a meta-learner to be able to approximate any learning algorithm in terms of its ability to represent functions of the dataset and test inputs.",
"This formalism provides a new perspective on the learning-to-learn problem, which we hope will lead to further discussion and research on the goals and methodology surrounding meta-learning.",
"While there are likely a number of ways to prove Lemma 4.1 (copied below for convenience), here we provide a simple, though inefficient, proof of Lemma 4.1.Lemma 4.1 Let us assume that e(y) can be chosen to be any linear (but not affine) function of y.",
"Then, we can choose θ ft , θ h , {A i ; i > 1}, {B i ; i < N } such that the function DISPLAYFORM0 can approximate any continuous function of (x, y, x ) on compact subsets of R dim(y) ."
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"Deep representations combined with gradient descent can approximate any learning algorithm."
] |
[
"Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing.",
"However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces.",
"To this end, we aim to create a BCI that decodes text directly from neural signals.",
"We implement a framework that initially isolates frequency bands in the input signal encapsulating differential information regarding production of various phonemic classes.",
"These bands form a feature set that feeds into an LSTM which discerns at each time point probability distributions across all phonemes uttered by a subject.",
"Finally, a particle filtering algorithm temporally smooths these probabilities incorporating prior knowledge of the English language to output text corresponding to the decoded word.",
"Further, in producing an output, we abstain from constraining the reconstructed word to be from a given bag-of-words, unlike previous studies.",
"The empirical success of our proposed approach, offers promise for the employment of such an interface by patients in unfettered, naturalistic environments.",
"Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) restrict an individual's potential to fully engage with their surroundings by hindering communication abilities.",
"BrainComputer Interfaces (BCI) have long been envisioned to assist such patients as they bypass affected pathways and directly translate neural recordings into text or speech output.",
"However, practical implementation of this technology has been hindered by limitations in speed and accuracy of existing systems [4] .",
"Many patients rely on devices that use motor imagery [10] , or on interfaces that require them to individually identify and spell out text characters such as the \"point and click\" cursor method",
"3) A bLSTM creates probability distributions over phonemes at each time point.",
"4) Probabilities are smoothed and domain knowledge is incorporated using a probabilistic automaton traversed using a particle filtering algorithm.",
"5) The highest probability word is chosen as the output.",
"[11, 12] .",
"Despite significant work in system optimization, inherent limitations in their designs render them significantly slower than spoken communication.",
"To address these shortcomings, several studies are using electrocorticography (ECoG) and local field potential (LFP) signals [2] .",
"These invasive approaches provide superior signal quality with high temporal and spatial accuracy.",
"Previous work attempted translation to continuous phoneme sequences using invasive neural data [8] ; however, despite their reported higher translation speed, their applications are limited to a reduced dictionary (10-100 words).",
"Other design choices meant to enhance phoneme classification capitalize on prior knowledge of the target words, hindering their generalization to unmodified scenarios.",
"Additionally, a recent study synthesized speech using recordings from speech cortex.",
"Though the authors demonstrate partial transferrability of their decoder amongst patients, their accuracy is again limited to selection of the reconstructed word by a listener from a pool of 25 words and worsens as the pool size increases [3] .",
"Thus, establishing the capability of these approaches to generalize to unconstrained vocabularies is not obvious and has to our knowledge not yet been studied.",
"Here, we present the performance of a two-part decoder network comprising of an LSTM and a particle filtering algorithm on data gathered from six patients.",
"We provide empirical evidence that our interface achieves an average accuracy of 32% calculated against a full corpus, i.e. one encompassing all feasible English words that can be formulated using the entire set of phonemes uttered by a patient, thus marking an important, non-incremental step in the direction of viability of such an interface.",
"Each of the subjects in this study were able to communicate with significantly higher accuracy than chance.",
"Nevertheless, the average word error rate seen in this study (67.8% on average) was higher than the 53% reported in [3] .",
"There were several important differences in these studies, however.",
"The primary difference is that their system produced an audio output that required a human listener to transcribe into a word selection.",
"Despite advances in machine learning and natural language processing, humans have superior ability to use contextual information to find meaning in a signal.",
"Furthermore, that study limited classifications to an output domain set of 50 words, which is generally not sufficient for a realistic communication system.",
"While this study makes a significant addition to existing BCI literature in terms of its avoidance of the traditional bag-of-words approach, our accuracies are lower than those reported in ERP-based BCI studies [12] .",
"Moreover, in order for a BCI system based on translating neural signals to become a practical solution, improvements need to be made either in signal acquisition, machine learning translation, or user strategy.",
"One approach could be to sacrifice some of the speed advantages by having users repeat words multiple times.",
"While this would reduce communication speeds below natural speaking rates, it would still greatly exceed ERP-based methods, while increasing the signals available for classification which could improve system accuracy.",
"However, both this study and previous literature have primarily been concerned with decoding speech/text for patients with intact motor abilities.",
"It is presently unclear how this would translate to intended speech.",
"While the electrodes used in this study are inept to answer this question, given their majority location in the speech cortical areas [9] , we suggest a plausible new experiment: teaching those who can't speak to rethink speech in terms of vocal tract movements.",
"Using electrodes in the sensorimotor cortex [3] and continuous visual feedback of ground truth vocal tract movements for each phoneme's pronounciation, a subject's attention could be entrained to only the (intended or executed) motion of their vocal tract for covert and overt speech respectively.",
"One can then test the transferability of state space models -latent variables comprising of different articulators and observed states corresponding to the time-varying neural signals -between the covert and overt behaviours to better understand and harness the physiological variability between the two to eventually translate current studies into potentially viable devices.",
"The proposed system serves as a step in the direction of a generalized BCI system that can directly translate neural signals into written text in naturalistic scenarios.",
"However, communication accuracies are currently insufficient for a practical BCI device, so future work must focus on improving these and developing an interface to present feedback to users."
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"We present an open-loop brain-machine interface whose performance is unconstrained to the traditionally used bag-of-words approach."
] |
[
"Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.",
"While previous contributions to feature extraction propose embeddings based on a single layer of the network, in this paper we propose a full-network embedding which successfully integrates convolutional and fully connected features, coming from all layers of a deep convolutional neural network.",
"To do so, the embedding normalizes features in the context of the problem, and discretizes their values to reduce noise and regularize the embedding space.",
"Significantly, this also reduces the computational cost of processing the resultant representations.",
"The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used for obtaining the initial features.",
"The performance gap in classification accuracy between thoroughly tuned solutions and the full-network embedding is also reduced, which makes of the proposed approach a competitive solution for a large set of applications.",
"Deep learning models, and particularly convolutional neural networks (CNN), have become the standard approach for tackling image processing tasks.",
"The key to the success of these methods lies in the rich representations deep models build, which are generated after an exhaustive and computationally expensive learning process BID16 .",
"To generate deep representations, deep learning models have strong training requirements in terms of dataset size, computational power and optimal hyper-parametrization.",
"For any domain or application in which either of those factors is an issue, training a deep model from scratch becomes unfeasible.Within deep learning, the field of transfer learning studies how to extract and reuse pre-trained deep representations.",
"This approach has three main applications: improving the performance of a network by initializing its training from a non-random state BID31 BID2 BID17 , enabling the training of deep networks for tasks of limited dataset size BID9 BID27 , and exploiting deep representations through alternative machine learning methods BID0 BID25 BID10 .",
"The first two cases, where training a deep network remains the end purpose of the transfer learning process, are commonly known as transfer learning for fine-tuning, while the third case, where the end purpose of the transfer learning does not necessarily include training a deep net, is typically referred as transfer learning for feature extraction.Of the three limiting factors of training deep networks (i.e., dataset size, computational cost, and optimal hyper-parametrization), transfer learning for fine-tuning partly solves the first.",
"Indeed, one can successfully train a CNN on a dataset composed by roughly a few thousand instances using a pre-trained model as starting point, and achieve state-of-the-art-results.",
"Unfortunately, fine-tuning a model still requires a minimum dataset size, a significant amount of computational resources, and lots of time to optimize the multiple hyper-parameters involved in the process.Transfer learning for feature extraction on the other hand is based on processing a set of data instances through a pre-trained neural network, extracting the activation values so these can be used by another learning mechanism.",
"This is applicable to datasets of any size, as each data instance is processed independently.",
"It has a relatively small computational cost, since there is no deep net training.",
"And finally, it requires no hyper-parameter optimization, since the pre-trained model can be used out-of-the-box.",
"Significantly, the applications of transfer learning for feature extraction are limited only by the capabilities of the methods that one can execute on top of the generated deep representations.As previously mentioned, designing and training a deep model to maximize classification performance is a time consuming task.",
"In this paper we explore the opposite approach, minimizing the design and tuning effort using a feature extraction process.",
"Our goal is to build an out-of-the-box classification tool (which could be used by anyone regardless of technical background) capable of defining a full-network embedding (integrating the representations built by all layers of a source CNN model).",
"When compared to single-layer embeddings, this approach generates richer and more powerful embeddings, while also being more robust to the use of inappropriate pre-trained models.",
"We asses the performance of such solution when compared with thoroughly designed and tuned models.",
"In this paper we describe a feature extraction process which leverages the information encoded in all the features of a deep CNN.",
"The full-network embedding introduces the use of feature standardization and of a novel feature discretization methodology.",
"The former provides context-dependent Table 5 : Classification results in % average per-class accuracy of the baseline and the full-network embedding when using a network pre-trained on ImageNet 2012 for mit67 and on Places2 for the rest.",
"embeddings, which adapt the representations to the problem at hand.",
"The later reduces noise and regularizes the embedding space while keeping the size of the original representation language (i.e., the pre-trained model used as source).",
"Significantly, the feature discretization restricts the computational overhead resultant of processing much larger embeddings when training an SVM.",
"Our experiments also show that the full-network is more robust than single-layer embeddings when an appropriate source model is not available.The resultant full-network embedding is shown to outperform single-layer embeddings in several classification tasks, and to provide the best reported results on one of those tasks (wood).",
"Within the state-of-the-art, the full-network embedding represents the best available solution when one of the following conditions apply: When the accessible data is scarce, or an appropriate pre-trained model is not available (e.g., specialized industrial applications), when computational resources are limited (e.g., no GPUs availability), or when development time or technical expertise is restricted or non cost-effective.Beyond classification, the full-network embedding may be of relevance for any task exploiting visual embeddings.",
"For example, in image retrieval and image annotation tasks, the full-network embedding has been shown to provide a boost in performance when compared to one layer embeddings ."
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] | SkAK2jg0b | true | [
"We present a full-network embedding of CNN which outperforms single layer embeddings for transfer learning tasks."
] |
[
"We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds.",
"These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation.",
"We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same training epochs as dense models.",
"Dynamic Sparse Training achieves prior art performance compared with other sparse training algorithms on various network architectures.",
"Additionally, we have several surprising observations that provide strong evidence to the effectiveness and efficiency of our algorithm.",
"These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures.",
"Despite the impressive success that deep neural networks have achieved in a wide range of challenging tasks, the inference in deep neural network is highly memory-intensive and computationintensive due to the over-parameterization of deep neural networks.",
"Network pruning (LeCun et al. (1990) ; Han et al. (2015) ; Molchanov et al. (2017) ) has been recognized as an effective approach to improving the inference efficiency in resource-limited scenarios.",
"Traditional pruning methods consist of dense network training followed with pruning and fine-tuning iterations.",
"To avoid the expensive pruning and fine-tuning iterations, many sparse training methods (Mocanu et al., 2018; Bellec et al., 2017; Mostafa & Wang, 2019; Dettmers & Zettlemoyer, 2019) have been proposed, where the network pruning is conducted during the training process.",
"However, all these methods suffers from following three problems:",
"Coarse-grained predefined pruning schedule.",
"Most of the existing pruning methods use predefined pruning schedule with many additional hyperparameters like pruning a% parameter each time and then fine-tuning for b epochs with totally c pruning steps.",
"It is non-trivial to determine these hyperparameters for network architectures with various degree of complexity.",
"Therefore, usually a fixed pruning schedule is adopted for all the network architectures, which means that a very simple network architecture like LeNet-300-100 will have the same pruning schedule as a far more complex network like ResNet-152.",
"Besides, almost all the existing pruning methods conduct epoch-wise pruning, which means that the pruning is conducted between two epochs and no pruning operation happens inside each epoch.",
"Failure to properly recover the pruned weights.",
"Almost all the existing pruning methods conduct \"hard\" pruning that prunes weights by directly setting their values to 0.",
"Many works (Guo et al., 2016; Mocanu et al., 2018; He et al., 2018) have argued that the importance of network weights are not fixed and will change dynamically during the pruning and training process.",
"Previously unimportant weights may tend to be important.",
"So the ability to recover the pruned weights is of high significance.",
"However, directly setting the pruned weights to 0 results in the loss of historical parameter importance, which makes it difficult to determine:",
"1) whether and when each pruned weight should be recovered,",
"2) what values should be assigned to the recovered weights.",
"Therefore, existing methods that claim to be able to recover the pruned weights simply choose a predefined portion of pruned weights to recover and these recover weights are randomly initialized or initialized to the same value.",
"Failure to properly determine layer-wise pruning rates.",
"Modern neural network architectures usually contain dozens of layers with various number of parameters.",
"Therefore, the degrees of parameter redundancy are very different among the layers.",
"For simplicity, some methods prune the same percentage of parameter at each layer, which is not optimal.",
"To obtain dynamic layer-wise pruning rates, a single global pruning threshold or layer-wise greedy algorithms are applied.",
"Using a single global pruning threshold is exceedingly difficult to assess the local parameter importance of the individual layer, since each layer has a significantly different amount of parameter and contribution to the model performance.",
"This makes pruning algorithms based on a single global threshold inconsistent and non-robust.",
"The problem of layer-by-layer greedy pruning methods is that the unimportant neurons in an early layer may have a significant influence on the responses in later layers, which may result in propagation and amplification of the reconstruction error (Yu et al., 2018) .",
"We propose a novel end-to-end sparse training algorithm that properly solves the above problems.",
"With only one additional hyperparameter used to set the final model sparsity, our method can achieve dynamic fine-grained pruning and recovery during the whole training process.",
"Meanwhile, the layerwise pruning rates will be adjusted automatically with respect to the change of parameter importance during the training and pruning process.",
"Our method achieves state-of-the-art performance compared with other sparse training algorithms.",
"The proposed algorithm has following promising properties:",
"• Step-wise pruning and recovery.",
"A training epoch usually will have tens of thousands of training steps, which is the feed-forward and back-propagation pass for a single mini-batch.",
"Instead of pruning between two training epochs with predefined pruning schedule, our method prunes and recovers the network parameter at each training step, which is far more fine-grained than existing methods.",
"• Neuron-wise or filter-wise trainable thresholds.",
"All the existing methods adopt a single pruning threshold for each layer or the whole architecture.",
"Our method defines a threshold vector for each layers.",
"Therefore, our method adopts neuron-wise pruning thresholds for fully connected and recurrent layer and filter-wise pruning thresholds for convolutional layer.",
"Additionally, all these pruning thresholds are trainable and will be updated automatically via back-propagation.",
"• Dynamic pruning schedule.",
"The training process of deep neural network consists of many hyperparameters.",
"The learning rate is perhaps the most important hyperparameter.",
"Usually, the learning rate will decay during the training process.",
"Our method can automatic adjust the layer-wise pruning rates under different learning rate to get the optimal sparse network structure.",
"• Consistent sparse pattern.",
"Our algorithm can get consistent layer-wise sparse pattern under different model sparsities, which indicates that our method can automatic determine the optimal layer-wise pruning rates given the target model sparsity."
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"We present a novel network pruning method that can find the optimal sparse structure during the training process with trainable pruning threshold"
] |
[
"To which extent can successful machine learning inform our understanding of biological learning?",
"One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation.",
"Here we focus on learning in recurrent networks and investigate a range of learning algorithms.",
"Our approach decomposes them into their computational building blocks and discusses their abstract potential as biological operations.",
"This alternative strategy provides a “lazy” but principled way of evaluating ML ideas in terms of their biological plausibility",
"It is still unclear how neural circuits achieve sophisticated learning, in particular solving temporal credit assignment.",
"Here we approached the problem by looking for biologically sensible approximations to RTRL and BPTT.",
"Although we have empirical results to prove that our solutions can solve temporal credit assignment for simple tasks, the substance of our contribution is conceptual, in articulating what computations are abstractly feasible and which are not.",
"In particular, we have shown that accessing the Jacobian for learning is possible by using a set of synapses trained to linearly approximate the network's own dynamics.",
"Along the way, we have identified some key lessons.",
"The main one is that neural circuits need additional infrastructure specifically to support learning.",
"This could be extra neurons, extra compartments within neurons, separate coordinated phases of computation, input gating by inhibition, etc.",
"While we all know that biology is a lot more complicated than traditional models of circuit learning would suggest, it has proved difficult to identify the functional role of these details in a bottom-up way.",
"On the other hand, drawing a link between ML algorithms and biology can hint at precise computational roles for not well understood circuit features.",
"Another lesson is that implementing even fairly simple learning equations in parallel to the forward pass is nontrivial, since it already uses up so much neural hardware.",
"Even a simple matrix-vector product requires an entirely separate phase of network dynamics in order to not interfere with the forward pass of computation.",
"While it may be tempting to outsource some of these update equations to separate neurons, the results would not be locally available to drive synaptic plasticity.",
"Of course, we acknowledge that any particular solution, whether RFLO or DNI, is a highly contrived, specific, and likely incorrect guess at how neural circuits learn, but we believe the exercise has big-picture implications for how to think about biological learning.",
"Beyond the particular topic of online learning in recurrent networks, our work provides a general blueprint for abstractly evaluating computational models as mechanistic explanations for biological neural networks.",
"Knowing what computational building blocks are at our disposal and what biological details are needed to implement them is an important foundation for studying ML algorithms in a biological context."
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"We evaluate new ML learning algorithms' biological plausibility in the abstract based on mathematical operations needed"
] |
[
"In recent years several adversarial attacks and defenses have been proposed.",
"Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used.",
"One way out of this dilemma are provable robustness guarantees.",
"While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations.",
"We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- \\textit{and} $l_\\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\\geq 1$.",
"The vulnerability of neural networks against adversarial manipulations (Szegedy et al., 2014; Goodfellow et al., 2015) is a problem for their deployment in safety critical systems such as autonomous driving and medical applications.",
"In fact, small perturbations of the input which appear irrelevant or are even imperceivable to humans change the decisions of neural networks.",
"This questions their reliability and makes them a target of adversarial attacks.",
"To mitigate the non-robustness of neural networks many empirical defenses have been proposed, e.g. by Gu & Rigazio (2015) ; Zheng et al. (2016) ; Papernot et al. (2016) ; Huang et al. (2016) ; Bastani et al. (2016) ; Madry et al. (2018) , but at the same time more sophisticated attacks have proven these defenses to be ineffective (Carlini & Wagner, 2017; Athalye et al., 2018; Mosbach et al., 2018) , with the exception of the adversarial training of Madry et al. (2018) .",
"However, even these l ∞ -adversarially trained models are not more robust than normal ones when attacked with perturbations of small l p -norms with p = ∞ (Sharma & Chen, 2019; Schott et al., 2019; Croce et al., 2019b; Kang et al., 2019) .",
"The situation becomes even more complicated if one extends the attack models beyond l p -balls to other sets of perturbations (Brown et al., 2017; Engstrom et al., 2017; Hendrycks & Dietterich, 2019; Geirhos et al., 2019) .",
"Another approach, which fixes the problem of overestimating the robustness of a model, is provable guarantees, which means that one certifies that the decision of the network does not change in a certain l p -ball around the target point.",
"Along this line, current state-of-theart methods compute either the norm of the minimal perturbation changing the decision at a point (e.g. Katz et al. (2017) ; Tjeng et al. (2019) ) or lower bounds on it (Hein & Andriushchenko, 2017; Raghunathan et al., 2018; Wong & Kolter, 2018) .",
"Several new training schemes like (Hein & Andriushchenko, 2017; Raghunathan et al., 2018; Wong & Kolter, 2018; Mirman et al., 2018; Croce et al., 2019a; Xiao et al., 2019; Gowal et al., 2018) aim at both enhancing the robustness of networks and producing models more amenable to verification techniques.",
"However, all of them are only able to prove robustness against a single kind of perturbations, typically either l 2 -or l ∞ -bounded, and not wrt all the l p -norms simultaneously, as shown in Section 5.",
"Some are also designed to work for a specific p (Mirman et al., 2018; Gowal et al., 2018) , and it is not clear if they can be extended to other norms.",
"The only two papers which have shown, with some limitations, non-trivial empirical robustness against multiple types of adversarial examples are Schott et al. (2019) and Tramèr & Boneh In this paper we aim at robustness against all the l p -bounded attacks for p ≥ 1.",
"We study the non-trivial case where none of the l p -balls is contained in another.",
"If p is the radius of the l p -ball for which we want to be provably robust, this requires:",
"q > p > q for p < q and d being the input dimension.",
"We show that, for normally trained models, for the l 1 -and l ∞ -balls we use in the experiments none of the adversarial examples constrained to be in the l 1 -ball (i.e. results of an l 1 -attack) belongs to the l ∞ -ball, and vice versa.",
"This shows that certifying the union of such balls is significantly more complicated than getting robust in only one of them, as in the case of the union the attackers have a much larger variety of manipulations available to fool the classifier.",
"We propose a technique which allows to train piecewise affine models (like ReLU networks) which are simultaneously provably robust to all the l p -norms with p ∈ [1, ∞].",
"First, we show that having guarantees on the l 1 -and l ∞ -distance to the decision boundary and region boundaries (the borders of the polytopes where the classifier is affine) is sufficient to derive meaningful certificates on the robustness wrt all l p -norms for p ∈ (1, ∞).",
"In particular, our guarantees are independent of the dimension of the input space and thus go beyond a naive approach where one just exploits that all l p -metrics can be upper-and lower-bounded wrt any other l q -metric.",
"Then, we extend the regularizer introduced in Croce et al. (2019a) so that we can directly maximize these bounds at training time.",
"Finally, we show the effectiveness of our technique with experiments on four datasets, where the networks trained with our method are the first ones having non-trivial provable robustness wrt l 1 -, l 2 -and l ∞ -perturbations.",
"We have presented the first method providing provable robustness guarantees for the union of multiple l p -balls beyond the trivial case of the union being equal to the largest one, establishing a baseline for future works.",
"Without loss of generality after a potential permutation of the coordinates it holds |x d | = x ∞ .",
"Then we get",
", which finishes the proof."
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"We introduce a method to train models with provable robustness wrt all the $l_p$-norms for $p\\geq 1$ simultaneously."
] |
[
"In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model.",
"The proposed control problem contains a restoration dynamics which is modeled by an RNN.",
"The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network.",
"We call the proposed model the dynamically unfolding recurrent restorer (DURR).",
"Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking.",
"Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.",
"Image restoration, including image denoising, deblurring, inpainting, etc., is one of the most important areas in imaging science.",
"Its major purpose is to obtain high quality reconstructions of images corrupted in various ways during imaging, acquisiting, and storing, and enable us to see crucial but subtle objects that reside in the images.",
"Image restoration has been an active research area.",
"Numerous models and algorithms have been developed for the past few decades.",
"Before the uprise of deep learning methods, there were two classes of image restoration approaches that were widely adopted in the field: transformation based approach and PDE approach.",
"The transformation based approach includes wavelet and wavelet frame based methods BID11 BID3 , dictionary learning based methods BID0 , similarity based methods BID2 BID10 , low-rank models BID21 BID18 , etc.",
"The PDE approach includes variational models BID31 BID35 BID1 , nonlinear diffusions BID33 BID6 BID38 , nonlinear hyperbolic equations BID32 , etc.",
"More recently, deep connections between wavelet frame based methods and PDE approach were established BID4 BID12 .One",
"of the greatest challenge for image restoration is to properly handle image degradations of different levels. In",
"the existing transformation based or PDE based methods, there is always at least one tuning parameter (e.g. the regularization parameter for variational models and terminal time for nonlinear diffusions) that needs to be manually selected. The",
"choice of the parameter heavily relies on the degradation level.Recent years, deep learning models for image restoration tasks have significantly advanced the state-of-the-art of the field. BID20",
"proposed a convolutional neural network (CNN) for image denoising which has better expressive power than the MRF models by BID22 . Inspired",
"by nonlinear diffusions, BID9 designed a deep neural network for image denoising and BID40 improves the capacity by introducing a deeper neural network with residual connections. use the",
"CNN to simulate a wide variety of image processing operators, achieving high efficiencies with little accuracy drop. However",
", these models cannot gracefully handle images with varied degradation levels. Although",
"one may train different models for images with different levels, this may limit the application of these models in practice due to lack of flexibility.Taking blind image denoising for example. BID40 designed",
"a 20-layer neural network for the task, called DnCNN-B, which had a huge number of parameters. To reduce number",
"of parameters, BID24 proposed the UNLNet 5 , by unrolling a projection gradient algorithm for a constrained optimization model. However, BID24 also",
"observed a drop in PSNR comparing to DnCNN. Therefore, the design",
"of a light-weighted and yet effective model for blind image denoising remains a challenge. Moreover, deep learning",
"based models trained on simulated gaussian noise images usually fail to handle real world noise, as will be illustrated in later sections.Another example is JPEG image deblocking. JPEG is the most commonly",
"used lossy image compression method. However, this method tend",
"to introduce undesired artifacts as the compression rate increases. JPEG image deblocking aims",
"to eliminate the artifacts and improve the image quality. Recently, deep learning based",
"methods were proposed for JPEG deblocking BID13 BID40 . However, most of their models",
"are trained and evaluated on a given quality factor. Thus it would be hard for these",
"methods to apply to Internet images, where the quality factors are usually unknown.In this paper, we propose a single image restoration model that can robustly restore images with varied degradation levels even when the degradation level is well outside of that of the training set. Our proposed model for image restoration",
"is inspired by the recent development on the relation between deep learning and optimal control. The relation between supervised deep learning",
"methods and optimal control has been discovered and exploited by BID39 ; BID26 BID7 BID16 . The key idea is to consider the residual block",
"x n+1 = x n + f (x n ) as an approximation to the continuous dynamicsẊ = f (X). In particular, BID26 BID16 demonstrated that the",
"training process of a class of deep models (e.g. ResNet by BID19 , PolyNet by BID42 , etc.) can be understood as solving the following control problem: DISPLAYFORM0 Here x 0 is the input, y is the regression target or label,Ẋ = f (X, w) is the deep neural network with parameter w(t), R is the regularization term and L can be any loss function to measure the difference between the reconstructed images and the ground truths.In the context of image restoration, the control dynamicẊ = f (X(t), ω(t)), t ∈ (0, τ ) can be, for example, a diffusion process learned using a deep neural network. The terminal time τ of the diffusion corresponds",
"to the depth of the neural network. Previous works simply fixed the depth of the network",
", i.e. the terminal time, as a fixed hyper-parameter. However BID30 showed that the optimal terminal time",
"of diffusion differs from image to image. Furthermore, when an image is corrupted by higher noise",
"levels, the optimal terminal time for a typical noise removal diffusion should be greater than when a less noisy image is being processed. This is the main reason why current deep models are not",
"robust enough to handle images with varied noise levels. In this paper, we no longer treat the terminal time as",
"a hyper-parameter. Instead, we design a new architecture (see Fig. 3 ) that",
"contains both a deep diffusion-like network and another network that determines the optimal terminal time for each input image. We propose a novel moving endpoint control model to train",
"the aforementioned architecture. We call the proposed architecture the dynamically unfolding",
"recurrent restorer (DURR).We first cast the model in the continuum setting. Let x 0 be",
"an observed degraded image and y be its corresponding",
"damage-free counterpart. We want to learn a time-independent dynamic systeṁ X = f (X(t),",
"w) with parameters w so that X(0) = x and X(τ ) ≈ y for some τ > 0. See Fig. 2 for an illustration of our idea. The reason that we",
"do not require X(τ ) = y is to avoid over-fitting",
". For varied degradation levels and different images, the optimal terminal",
"time τ of the dynamics may vary. Therefore, we need to include the variable τ in the learning process as",
"well. The learning of the dynamic system and the terminal time can be gracefully",
"casted as the following moving endpoint control problem: DISPLAYFORM1 Different from the previous control problem, in our model the terminal time τ is also a parameter to be optimized and it depends on the data x. The dynamic systemẊ = f (X(t), w) is modeled by a recurrent neural network",
"(RNN) with a residual connection, which can be understood as a residual network with shared weights BID25 . We shall refer to this RNN as the restoration unit. In order to learn the",
"terminal time of the dynamics, we adopt a policy network",
"to adaptively determine an optimal stopping time. Our learning framework is demonstrated in Fig. 3 . We note that the above moving",
"endpoint control problem can be regarded as the penalized",
"version of the well-known fixed endpoint control problem in optimal control BID15 , where instead of penalizing the difference between X(τ ) and y, the constraint X(τ ) = y is strictly enforced.In short, we summarize our contribution as following:• We are the first to use convolutional RNN for image restoration with unknown degradation levels, where the unfolding time of the RNN is determined dynamically at run-time by a policy unit (could be either handcrafted or RL-based).• The proposed model achieves state-of-the-art performances with significantly less parameters",
"and better running efficiencies than some of the state-of-the-art models.• We reveal the relationship between the generalization power and unfolding time of the RNN by",
"extensive experiments. The proposed model, DURR, has strong generalization to images with varied degradation levels and",
"even to the degradation level that is unseen by the model during training (Fig. 1 ).• The DURR is able to well handle real image denoising without further modification. Qualitative",
"results have shown that our processed images have better visual quality, especially",
"sharper details compared to others.",
"In this paper, we proposed a novel image restoration model based on the moving endpoint control in order to handle varied noise levels using a single model.",
"The problem was solved by jointly optimizing two units: restoration unit and policy unit.",
"The restoration unit used an RNN to realize the dynamics in the control problem.",
"A policy unit was proposed for the policy unit to determine the loop times of the restoration unit for optimal results.",
"Our model achieved the state-of-the-art results in blind image denoising and JPEG deblocking.",
"Moreover, thanks to the flexibility of the given policy, DURR has shown strong abilities of generalization in our experiments."
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] | SJfZKiC5FX | true | [
"We propose a novel method to handle image degradations of different levels by learning a diffusion terminal time. Our model can generalize to unseen degradation level and different noise statistic."
] |
[
"The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions.",
"It has found numerous applications in several hard problems, such as domain adaptation, dimensionality reduction or generative models.",
"However, its use is still limited by a heavy computational cost.",
"Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity.",
"It relies on the search of an embedding where the Euclidean distance mimics the Wasserstein distance.",
"We show that such an embedding can be found with a siamese architecture associated with a decoder network that allows to move from the embedding space back to the original input space.",
"Once this embedding has been found, computing optimization problems in the Wasserstein space (e.g. barycenters, principal directions or even archetypes) can be conducted extremely fast.",
"Numerical experiments supporting this idea are conducted on image datasets, and show the wide potential benefits of our method.",
"The Wasserstein distance is a powerful tool based on the theory of optimal transport to compare data distributions with wide applications in image processing, computer vision and machine learning BID29 .",
"In a context of machine learning, it has recently found numerous applications, e.g. domain adaptation , or word embedding BID22 .",
"In the context of deep learning, the Wasserstein appeared recently to be a powerful loss in generative models BID2 and in multi-label classification BID19 .",
"Its power comes from two major reasons:",
"i) it allows to operate on empirical data distributions in a non-parametric way",
"ii) the geometry of the underlying space can be leveraged to compare the distributions in a geometrically sound way.",
"The space of probability measures equipped with the Wasserstein distance can be used to construct objects of interest such as barycenters BID0 or geodesics BID34 that can be used in data analysis and mining tasks.More formally, let X be a metric space endowed with a metric d X .",
"Let p ∈ (0, ∞) and P p (X) the space of all Borel probability measures µ on X with finite moments of order p, i.e. X d X (x, x 0 ) p dµ(x) < ∞ for all x 0 in X. The p-Wasserstein distance between µ and ν is defined as: DISPLAYFORM0 Here, Π(µ, ν) is the set of probabilistic couplings π on (µ, ν).",
"As such, for every Borel subsets A ⊆ X, we have that µ(A) = π(X × A) and ν(A) = π(A × X).",
"It is well known that W p defines a metric over P p (X) as long as p ≥ 1 (e.g. BID39 ), Definition 6.2).When",
"p = 1, W 1 is also known as Earth Mover's distance (EMD) or Monge-Kantorovich distance. The",
"geometry of (P p (X), W 1 (X)) has been thoroughly studied, and there exists several works on computing EMD for point sets in R k (e.g. BID35 ). However",
", in a number of applications the use of W 2 (a.k.a root mean square bipartite matching distance) is a more natural distance arising in computer vision BID6 , computer graphics BID5 BID16 BID36 BID7 or machine learning BID14 . See BID16",
"for a discussion on the quality comparison between W 1 and W 2 .Yet, the deployment",
"of Wasserstein distances in a wide class of applications is somehow limited, especially because of an heavy computational burden. In the discrete version",
"of the above optimisation problem, the number of variables scale quadratically with the number of samples in the distributions, and solving the associated linear program with network flow algorithms is known to have a cubical complexity. While recent strategies",
"implying slicing technique BID6 BID26 , entropic regularization BID13 BID3 BID37 or involving stochastic optimization BID21 , have emerged, the cost of computing pairwise Wasserstein distances between a large number of distributions (like an image collection) is prohibitive. This is all the more true",
"if one considers the problem of computing barycenters BID14 BID3 or population means. A recent attempt by Staib",
"and colleagues BID38 use distributed computing for solving this problem in a scalable way.We propose in this work to learn an Euclidean embedding of distributions where the Euclidean norm approximates the Wasserstein distances. Finding such an embedding",
"enables the use of standard Euclidean methods in the embedded space and significant speedup in pairwise Wasserstein distance computation, or construction of objects of interests such as barycenters. The embedding is expressed",
"as a deep neural network, and is learnt with a strategy similar to those of Siamese networks BID11 . We also show that simultaneously",
"learning the inverse of the embedding function is possible and allows for a reconstruction of a probability distribution from the embedding. We first start by describing existing",
"works on Wasserstein space embedding. We then proceed by presenting our learning",
"framework and give proof of concepts and empirical results on existing datasets.",
"In this work we presented a computational approximation of the Wasserstein distance suitable for large scale data mining tasks.",
"Our method finds an embedding of the samples in a space where the Euclidean distance emulates the behavior of the Wasserstein distance.",
"Thanks to this embedding, numerous data analysis tasks can be conducted at a very cheap computational price.",
"We forecast that this strategy can help in generalizing the use of Wasserstein distance in numerous applications.However, while our method is very appealing in practice it still raises a few questions about the theoretical guarantees and approximation quality.",
"First it is difficult to foresee from a given network architecture if it is sufficiently (or too much) complex for finding a successful embedding.",
"It can be conjectured that it is dependent on the complexity of the data at hand and also the locality of the manifold where the data live in.",
"Second, the theoretical existence results on such Wasserstein embedding with constant distortion are still lacking.",
"Future works will consider these questions as well as applications of our approximation strategy on a wider range of ground loss and data mining tasks.",
"Also, we will study the transferability of one database to another (i.e. leveraging on previously computed embedding) to diminish the computational burden of computing Wasserstein distances on numerous pairs for the learning process, by considering for instance domain adaptation strategies between embeddings."
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"We show that it is possible to fastly approximate Wasserstein distances computation by finding an appropriate embedding where Euclidean distance emulates the Wasserstein distance"
] |
[
"Continuous Bag of Words (CBOW) is a powerful text embedding method.",
"Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute.",
"However, CBOW is not capable of capturing the word order.",
"The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same.",
"In order to address this shortcoming, we propose a\n",
"learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW).",
"Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text.",
"We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content.",
"Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW.",
"Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%.",
"As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%.",
"Word embeddings are perceived as one of the most impactful contributions from unsupervised representation learning to natural language processing from the past few years BID10 .",
"Word embeddings are learned once on a large-scale stream of words.",
"A key benefit is that these pre-computed vectors can be re-used almost universally in many different downstream applications.",
"Recently, there has been increasing interest in learning universal sentence embeddings.",
"BID24 have shown that the best encoding architectures are based on recurrent neural networks (RNNs) BID5 BID25 or the Transformer architecture BID2 .",
"These techniques are, however, substantially more expensive to train and apply than word embeddings BID14 BID2 .",
"Their usefulness is therefore limited when fast processing of large volumes of data is critical.More efficient encoding techniques are typically based on aggregated word embeddings such as Continuous Bag of Words (CBOW), which is a mere summation of the word vectors BID19 .",
"Despite CBOW's simplicity, it attains strong results on many downstream tasks.",
"Using sophisticated weighting schemes, the performance of aggregated word embeddings can be further increased BID0 , coming even close to strong LSTM baselines BID26 BID13 such as InferSent BID5 .",
"This raises the question how much benefit recurrent encoders actually provide over simple word embedding based methods BID32 .",
"In their analysis, BID13 suggest that the main difference may be the ability to encode word order.",
"In this paper, we propose an intuitive method to enhance aggregated word embeddings by word order awareness.The major drawback of these CBOW-like approaches is that they are solely based on addition.",
"However, addition is not all you need.",
"Since it is a commutative operation, the aforementioned methods are not able to capture any notion of word order.",
"However, word order information is crucial for some tasks, e.g., sentiment analysis BID13 .",
"For instance, the following two sentences yield the exact same embedding in an addition-based word embedding aggregation technique: \"The movie was not awful, it was rather great.\" and \"The movie was not great, it was rather awful.\"",
"A classifier based on the CBOW embedding of these sentences would inevitably fail to distinguish the two different meanings (Goldberg, 2017, p. 151) .To",
"alleviate this drawback, BID28 propose to model each word as a matrix rather than a vector, and compose multiple word embeddings via matrix multiplication rather than addition. This",
"so-called Compositional Matrix Space Model (CMSM) of language has powerful theoretical properties that subsume properties from vector-based models and symbolic approaches. The",
"most obvious advantage is the non-commutativity of matrix multiplication as opposed to addition, which results in order-aware encodings.In contrast to vector-based word embeddings, there is so far no solution to effectively train the parameters of word matrices on large-scale unlabeled data. Training",
"schemes from previous work were specifically designed for sentiment analysis BID34 BID1 . Those require",
"complex, multi-stage initialization, which indicates the difficulty of training CMSMs. We show that",
"CMSMs can be trained in a similar way as the well-known CBOW model of word2vec BID19 . We make two",
"simple yet critical changes to the initialization strategy and training objective of CBOW. Hence, we present",
"the first unsupervised training scheme for CMSMs, which we call Continual Multiplication Of Words (CMOW). We evaluate our model",
"'s capability to capture linguistic properties in the encoded text. We find that CMOW and",
"CBOW have properties that are complementary. On the one hand, CBOW",
"yields much stronger results at the word content memorization task. CMOW, on the other hand",
", offers an advantage in all other linguistic probing tasks, often by a wide margin. Thus, we propose a hybrid",
"model to jointly learn the word vectors of CBOW and the word matrices for CMOW.Our experimental results confirm the effectiveness of our hybrid CBOW-CMOW approach. At comparable embedding size",
", CBOW-CMOW retains CBOW's ability to memorize word content while at the same time improves the performance on the linguistic probing tasks by 8%. CBOW-CMOW outperforms CBOW",
"at 8 out of 11 supervised downstream tasks scoring only 0.6% lower on the tasks where CBOW is slightly better. On average, the hybrid model",
"improves the performance over CBOW by 1.2% on supervised downstream tasks, and by 0.5% on the unsupervised tasks.In summary, our contributions are: (1) For the first time, we present an unsupervised, efficient training scheme for the Compositional Matrix Space Model. Key elements of our scheme are",
"an initialization strategy and training objective that are specifically designed for training CMSMs. (2) We quantitatively demonstrate",
"that the strengths of the resulting embedding model are complementary to classical CBOW embeddings. (3) We successfully combine both",
"approaches into a hybrid model that is superior to its individual parts.After giving a brief overview of the related work, we formally introduce CBOW, CMOW, and the hybrid model in Section 3. We describe our experimental",
"setup and present the results in Section 4. The results are discussed in",
"Section 5, before we conclude.",
"Our CMOW model produces sentence embeddings that are approximately at the level of fastSent BID14 .",
"Thus, CMOW is a reasonable choice as a sentence encoder.",
"Essential to the success of our training schema for the CMOW model are two changes to the original word2vec training.",
"First, our initialization strategy improved the downstream performance by 2.8% compared to Glorot initialization.",
"Secondly, by choosing the target word of the objective at random, the performance of CMOW on downstream tasks improved by 20.8% on average.",
"Hence, our novel training scheme is the first that provides an effective way to obtain parameters for the Compositional Matrix Space Model of language from unlabeled, large-scale datasets.",
"Regarding the probing tasks, we observe that CMOW embeddings better encode the linguistic properties of sentences than CBOW.",
"CMOW gets reasonably close to CBOW on some downstream tasks.",
"However, CMOW does not in general supersede CBOW embeddings.",
"This can be explained by the fact that CBOW is stronger at word content memorization, which is known to highly correlate with the performance on most downstream tasks ).",
"Yet, CMOW has an increased performance on the TREC question type classification task (88.0 compared to 85.6).",
"The rationale is that this particular TREC task belongs to a class of downstream tasks that require capturing other linguistic properties apart from Word Content .Due",
"to joint training, our hybrid model learns to pick up the best features from CBOW and CMOW simultaneously. It",
"enables both models to focus on their respective strengths. This",
"can best be seen by observing that H-CMOW almost completely loses its ability to memorize word content. In return",
", H-CMOW has more capacity to learn other properties, as seen in the increase in performance at BShift and others. A complementary",
"behavior can be observed for H-CBOW, whose scores on Word Content are increased. Consequently, with",
"an 8% improvement on average, the hybrid model is substantially more linguistically informed than CBOW. This transfers to",
"an overall performance improvement by 1.2% on average over 11 supervised downstream tasks, with large improvements on sentiment analysis tasks (SST2, SST5), question classification (TREC), and the sentence representation benchmark (STS-B). The improvements",
"on these tasks is expected because they arguably depend on word order information. On the other tasks",
", the differences are small. Again, this can be",
"explained by the fact that most tasks in the SentEval framework mainly depend on word content memorization , where the hybrid model does not improve upon CBOW.Please note, the models in our study do not represent the state-of-the-art for sentence embeddings. BID24 show that better",
"scores are achieved by LSTMs and Transformer models, but also by averaging word embedding from fastText BID21 . These embeddings were",
"trained on the CBOW objective, and are thus very similar to our models. However, they are trained",
"on large corpora (600B tokens vs 3B in our study), use large vocabularies (2M vs 30k in our study), and incorporate numerous tricks to further enhance the quality of their models: word subsampling, subword-information, phrase representation, n-gram representations, position-dependent weighting, and corpus de-duplication. In the present study, we",
"focus on comparing CBOW, CMOW, and the hybrid model in a scenario where we have full control over the independent variables. To single out the effect",
"of the independent variables better, we keep our models relatively simple. Our analysis yields interesting",
"insights on what our models learn when trained separately or jointly, which we consider more valuable in the long term for the research field of text representation learning.We offer an efficient order-aware extension to embedding algorithms from the bag-of-words family. Our 784-dimensional CMOW embeddings",
"can be computed at the same rate as CBOW embeddings. We empirically measured in our experiments",
"71k for CMOW vs. 61k for CBOW in terms of encoding sentences per second. This is because of the fast implementation",
"of matrix multiplication in GPUs. It allows us to encode sentences approximately",
"5 times faster than using a simple Elman RNN of the same size (12k per second). Our matrix embedding approach also offers valuable",
"theoretical advantages over RNNs and other autoregressive models. Matrix multiplication is associative such that only",
"log 2 n sequential steps are necessary to encode a sequence of size n. Besides parallelization, also dynamic programming techniques",
"can be employed to further reduce the number of matrix multiplication steps, e. g., by pre-computing frequent bigrams. We therefore expect our",
"matrix",
"embedding approach to be specifically",
"well-suited for large-scale, time-sensitive text encoding applications. Our hybrid model serves as a blueprint for using CMOW in conjunction",
"with other existing embedding techniques such as fastText BID21 .",
"We have presented the first efficient, unsupervised learning scheme for the word order aware Compositional Matrix Space Model.",
"We showed that the resulting sentence embeddings capture linguistic features that are complementary to CBOW embeddings.",
"We thereupon presented a hybrid model with CBOW that is able to combine the complementary strengths of both models to yield an improved downstream task performance, in particular on tasks that depend on word order information.",
"Thus, our model narrows the gap in terms of representational power between simple word embedding based sentence encoders and highly non-linear recurrent sentence encoders.We made the code for this paper available at https://github.com/florianmai/ word2mat."
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"We present a novel training scheme for efficiently obtaining order-aware sentence representations."
] |
[
"This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit.",
"Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion.",
"This can be interpreted as a form of meta-gating and recursively parameterizing a recurrent model.",
"We postulate that our proposed inductive bias provides modeling benefits pertaining to learning with inherently hierarchically-structured sequence data (e.g., language, logical or music tasks).",
"To this end, we conduct extensive experiments on recursive logic tasks (sorting, tree traversal, logical inference), sequential pixel-by-pixel classification, semantic parsing, code generation, machine translation and polyphonic music modeling, demonstrating the widespread utility of the proposed approach, i.e., achieving state-of-the-art (or close) performance on all tasks.",
"Sequences are fundamentally native to the world we live in, i.e., language, logic, music and time are all well expressed in sequential form.",
"To this end, the design of effective and powerful sequential inductive biases has far-reaching benefits across many applications.",
"Across many of these domains, e.g., natural language processing or speech, the sequence encoder lives at the heart of many powerful state-of-the-art model architectures.",
"Models based on the notion of recurrence have enjoyed pervasive impact across many applications.",
"In particular, the best recurrent models operate with gating functions that not only ameliorate vanishing gradient issues but also enjoy fine-grain control over temporal compositionality (Hochreiter & Schmidhuber, 1997; .",
"Specifically, these gating functions are typically static and trained via an alternate transformation over the original input.",
"In this paper, we propose a new sequence model that recursively parameterizes the recurrent unit.",
"More concretely, the gating functions of our model are now parameterized repeatedly by instances of itself which imbues our model with the ability to reason deeply 1 and recursively about certain inputs.",
"To achieve the latter, we propose a soft dynamic recursion mechanism, which softly learns the depth of recursive parameterization at a per-token basis.",
"Our formulation can be interpreted as a form of meta-gating since temporal compositionality is now being meta-controlled at various levels of abstractions.",
"Our proposed method, Meta Gated Recursive Controller Units (METAGROSS), marries the benefits of recursive reasoning with recurrent models.",
"Notably, we postulate that this formulation brings about benefits pertaining to modeling data that is instrinsically hierarchical (recursive) in nature, e.g., natural language, music and logic, an increasingly prosperous and emerging area of research (Shen et al., 2018; Wang et al., 2019; Choi et al., 2018) .",
"While the notion of recursive neural networks is not new, our work is neither concerned with syntax-guided composition (Tai et al., 2015; Socher et al., 2013; nor unsupervised grammar induction (Shen et al., 2017; Choi et al., 2018; Havrylov et al., 2019; Yogatama et al., 2016) .",
"Instead, our work is a propulsion on a different frontier, i.e., learning recursively parameterized models which bears a totally different meaning.",
"Overall, the key contributions of this work are as follows:",
"• We propose a new sequence model.",
"Our model is distinctly characterized by recursive parameterization of recurrent gates, i.e., compositional flow is controlled by instances of itself,á la repeatedly and recursively.",
"We propose a soft dynamic recursion mechanism that dynamically and softly learns the recursive depth of the model at a token-level.",
"• We propose a non-autoregressive parallel variation of METAGROSS,that when equipped with the standard Transformer model (Vaswani et al., 2017) , leads to gains in performance.",
"• We evaluate our proposed method on a potpourri of sequence modeling tasks, i.e., logical recursive tasks (sorting, tree traversal, logical inference), pixel-wise sequential image classification, semantic parsing, neural machine translation and polyphonic music modeling.",
"METAGROSS achieves state-of-the-art performance (or close) on all tasks.",
"This section reports some analysis and discussion regarding the proposed model.",
"Table 9 : Optimal Maximum Depth N and base unit for different tasks.",
"Table 8 reports some ablation studies on the semantic parsing and code generation tasks.",
"We observe that the base unit and optimal maximum depth used is task dependent.",
"For ATIS dataset, using the linear transform as the base unit performs the best.",
"Conversely, the linear base unit performs worse than the recurrent base unit (LSTM) on the DJANGO dataset.",
"On a whole, we also observed this across other tasks, i.e., the base unit and maximum depth of METAGROSS is a critical choice for most tasks.",
"Table 9 reports the optimal max depth N and best base unit for each task.",
"3.6.2 ANALYSIS OF SOFT DYNAMIC RECURSION Figure 6 illustrates the depth gate values on CIFAR and MNIST datasets.",
"These values reflect the α and β values in METAGROSS, signifying how the parameter tree is being constructed during training.",
"This is reflected as L and R in the figures representing left and right gates.",
"Firstly, we observe that our model indeed builds data-specific parameterization of the network.",
"This is denoted by how METAGROSS builds different 6 trees for CIFAR and MNIST.",
"Secondly, we analyze the dynamic recursion depth with respect to time steps.",
"The key observation that all datasets have very diverse construction of recursive parameters.",
"The recursive gates fluctuate aggressively on CI-FAR while remaining more stable on Music modeling.",
"Moreover, we found that the recursive gates remain totally constant on MNIST.",
"This demonstrates that our model has the ability to adjust the dynamic construction adaptively and can revert to static recursion over time if necessary.",
"We find that compelling.",
"The adaptive recursive depth is made more intriguing by observing how the recursive parameterization alters on CIFAR and Music datasets.",
"From Figure 8 we observe that the structure of the network changes in a rhythmic fashion, in line with our intuition of musical data.",
"When dealing with pixel information, the tree structure changes adaptively according to the more complex information processed by the network.",
"We proposed Meta Gated Recursive Controller Units (METAGROSS) a sequence model characterized by recursive parameterization of gating functions.",
"Our proposed method achieves very promising and competitive results on a spectrum of benchmarks across multiple modalities (e.g., language, logic, music).",
"We propose a non-autoregressive variation of METAGROSS, which allows simple drop-in enhancement to state-of-the-art Transformers.",
"We study and visualise our network as it learns a dynamic recursive parameterization, shedding light on the expressiveness and flexibility to learn dynamic parameter structures depending on the data."
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"Recursive Parameterization of Recurrent Models improve performance "
] |
[
"Which generative model is the most suitable for Continual Learning?",
"This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks.",
"We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning.",
"We used two quantitative metrics to estimate the generation quality and memory ability.",
"We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).",
"We found that among all models, the original GAN performs best and among Continual Learning strategies, generative replay outperforms all other methods.",
"Even if we found satisfactory combinations on MNIST and Fashion MNIST, training generative models sequentially on CIFAR10 is particularly instable, and remains a challenge.",
"Learning in a continual fashion is a key aspect for cognitive development among biological species BID4 .",
"In Machine Learning, such learning scenario has been formalized as a Continual Learning (CL) setting BID30 BID21 BID27 BID29 BID26 .",
"The goal of CL is to learn from a data distribution that change over time without forgetting crucial information.",
"Unfortunately, neural networks trained with backpropagation are unable to retain previously learned information when the data distribution change, an infamous problem called \"catastrophic forgetting\" BID6 .",
"Successful attempts at CL with neural networks have to overcome the inexorable forgetting happening when tasks change.In this paper, we focus on generative models in Continual Learning scenarios.",
"Previous work on CL has mainly focused on classification tasks BID14 BID23 BID29 BID26 .",
"Traditional approaches are regularization, rehearsal and architectural strategies, as described in Section 2.",
"However, discriminative and generative models strongly differ in their architecture and learning objective.",
"Several methods developed for discriminative models are thus not directly extendable to the generative setting.",
"Moreover, successful CL strategies for generative models can be used, via sample generation as detailed in the next section, to continually train discriminative models.",
"Hence, studying the viability and success/failure modes of CL strategies for generative models is an important step towards a better understanding of generative models and Continual Learning in general.We conduct a comparative study of generative models with different CL strategies.",
"In our experiments, we sequentially learn generation tasks.",
"We perform ten disjoint tasks, using commonly used benchmarks for CL: MNIST (LeCun et al., 1998) , Fashion MNIST BID34 and CIFAR10 BID15 .",
"In each task, the model gets a training set from one new class, and should learn to generate data from this class without forgetting what it learned in previous tasks, see Fig. 1 for an example with tasks on MNIST.We evaluate several generative models: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), their conditional variant (CVAE ans CGAN), Wasserstein GANs (WGANs) and Figure 1 : The disjoint setting considered.",
"At task i the training set includes images belonging to category i, and the task is to generate samples from all previously seen categories.",
"Here MNIST is used as a visual example,but we experiment in the same way Fashion MNIST and CIFAR10.Wasserstein GANs Gradient Penalty (WGAN-GP).",
"We compare results on approaches taken from CL in a classification setting: finetuning, rehearsal, regularization and generative replay.",
"Generative replay consists in using generated samples to maintain knowledge from previous tasks.",
"All CL approaches are applicable to both variational and adversarial frameworks.",
"We evaluate with two quantitative metrics, Fréchet Inception Distance BID10 and Fitting Capacity BID17 , as well as visualization.",
"Also, we discuss the data availability and scalability of CL strategies.",
"Besides the quantitative results and visual evaluation of the generated samples, the evaluated strategies have, by design, specific characteristics relevant to CL that we discuss here.Rehearsal violates the data availability assumption, often required in CL scenarios, by recording part of the samples.",
"Furthermore the risk of overfitting is high when only few samples represent a task, as shown in the CIFAR10 results.",
"EWC and Generative Replay respect this assumption.",
"EWC has the advantage of not requiring any computational overload during training, but this comes at the cost of computing the Fisher information matrix, and storing its values as well as a copy of previous parameters.",
"The memory needed for EWC to save information from the past is twice the size of the model which may be expensive in comparison to rehearsal methods.",
"Nevertheless, with Rehearsal and Generative Replay, the model has more and more samples to learn from at each new task, which makes training more costly.Another point we discuss is about a recently proposed metric BID32 to evaluate CL for generative models.",
"Their evaluation is defined for conditional generative models.",
"For a given label l, they sample images from the generator conditioned on l and feed it to a pre-trained classifier.If the predicted label of the classifier matches l, then it is considered correct.",
"In our experiment we find that it gives a clear advantage to rehearsal methods.",
"As the generator may overfit the few samples kept in memory, it can maximizes the evaluation proposed by BID33 , while not producing diverse samples.",
"We present this phenomenon with our experiments in appendix D. Nevertheless, even if their metric is unable to detect mode collapse or overfitting, it can efficiently expose catastrophic forgetting in conditional models.",
"In this paper, we experimented with the viability and effectiveness of generative models on Continual Learning (CL) settings.",
"We evaluated the considered approaches on commonly used datasets for CL, with two quantitative metrics.",
"Our experiments indicate that on MNIST and Fashion MNIST, the original GAN combined to the Generative Replay method is particularly effective.",
"This method avoids catastrophic forgetting by using the generator as a memory to sample from the previous tasks and hence maintain past knowledge.",
"Furthermore, we shed light on how generative models can learn continually with various methods and present successful combinations.",
"We also reveal that generative models do not perform well enough on CIFAR10 to learn continually.",
"Since generation errors accumulate, they are not usable in a continual setting.",
"The considered approaches have limitations: we rely on a setting where task boundaries are discrete and given by the user.",
"In future work, we plan to investigate automatic detection of tasks boundaries.",
"Another improvement would be to experiment with smoother transitions between tasks, rather than the disjoint tasks setting.A SAMPLES AT EACH STEP Figure 11: Reproduction of EWC experiment BID27 with four tasks.",
"First task with 0 and 1 digits, then digits of 2 for task 2, digits of 3 for task 3 etc.",
"When task contains only one class, the Fisher information matrix cannot capture the importance of the class-index input vector because it is always fixed to one class.",
"This problem makes the learning setting similar to a non-conditional models one which is known to not work BID27 .",
"As a consequence 0 and 1 are well protected when following classes are not.",
"Figure 16: WGAN-GP samples on CIFAR10, with on training for each separate category.",
"The implementation we used is available here: https://github.com/caogang/wgan-gp.",
"Classes, from 0 to 9, are planes, cars, birds, cats, deers, dogs, frogs, horses, ships and trucks.Figure 17: WGAN-GP samples on 10 sequential tasks on CIFAR10, with Generative Replay.",
"Classes, from 0 to 9, are planes, cars, birds, cats, deers, dogs, frogs, horses, ships and trucks.",
"We observe that generation errors snowballs as tasks are encountered, so that the images sampled after the last task are completely blurry."
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"A comparative study of generative models on Continual Learning scenarios."
] |
[
"We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning.",
"Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space.",
"Using supervised regression, it then converts the optimal non-parameterized policy to a parameterized policy, from which it draws new samples.",
"The methodology is general in that it applies to both discrete and continuous action spaces, and can handle a wide variety of proximity constraints for the non-parameterized optimization problem.",
"We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology.",
"The SPU implementation is much simpler than TRPO.",
"In terms of sample efficiency, our extensive experiments show SPU outperforms TRPO in Mujoco simulated robotic tasks and outperforms PPO in Atari video game tasks.",
"The policy gradient problem in deep reinforcement learning (DRL) can be defined as seeking a parameterized policy with high expected reward.",
"An issue with policy gradient methods is poor sample efficiency BID10 BID21 BID27 BID29 .",
"In algorithms such as REINFORCE BID28 , new samples are needed for every gradient step.",
"When generating samples is expensive (such as robotic environments), sample efficiency is of central concern.",
"The sample efficiency of an algorithm is defined to be the number of calls to the environment required to attain a specified performance level BID10 .Thus",
", given the current policy and a fixed number of trajectories (samples) generated, the goal of the sample efficiency problem is to construct a new policy with the highest performance improvement possible. To",
"do so, it is desirable to limit the search to policies that are close to the original policy π θ k BID21 BID29 BID24 . Intuitively",
", if the candidate new policy π θ is far from the original policy π θ k , it may not perform better than the original policy because too much emphasis is being placed on the relatively small batch of new data generated by π θ k , and not enough emphasis is being placed on the relatively large amount of data and effort previously used to construct π θ k .This guideline",
"of limiting the search to nearby policies seems reasonable in principle, but requires a distance η(π θ , π θ k ) between the current policy π θ k and the candidate new policy π θ , and then attempt to solve the constrained optimization problem: DISPLAYFORM0 subject to η(π θ , π θ k ) ≤ δwhereĴ(π θ | π θ k , new data) is an estimate of J(π θ ), the performance of policy π θ , based on the previous policy π θ k and the batch of fresh data generated by π θ k . The objective",
"(1) attempts to maximize the performance of the updated policy, and the constraint (2) ensures that the updated policy is not too far from the policy π θ k that was used to generate the data. Several recent",
"papers BID21 BID24 belong to the framework (1)-(2).Our work also strikes",
"the right balance between performance and simplicity. The implementation is",
"only slightly more involved than PPO . Simplicity in RL algorithms",
"has its own merits. This is especially useful when",
"RL algorithms are used to solve problems outside of traditional RL testbeds, which is becoming a trend BID30 BID16 .We propose a new methodology, called",
"Supervised Policy Update (SPU), for this sample efficiency problem. The methodology is general in that it",
"applies to both discrete and continuous action spaces, and can address a wide variety of constraint types for (2). Starting with data generated by the current",
"policy, SPU optimizes over a proximal policy space to find an optimal non-parameterized policy. It then solves a supervised regression problem",
"to convert the non-parameterized policy to a parameterized policy, from which it draws new samples. We develop a general methodology for finding an",
"optimal policy in the non-parameterized policy space, and then illustrate the methodology for three different definitions of proximity. We also show how the Natural Policy Gradient and",
"Trust Region Policy Optimization (NPG/TRPO) problems and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology. While SPU is substantially simpler than NPG/TRPO",
"in terms of mathematics and implementation, our extensive experiments show that SPU is more sample efficient than TRPO in Mujoco simulated robotic tasks and PPO in Atari video game tasks.Off-policy RL algorithms generally achieve better sample efficiency than on-policy algorithms BID8 . However, the performance of an on-policy algorithm",
"can usually be substantially improved by incorporating off-policy training BID17 , BID26 ). Our paper focuses on igniting interests in separating",
"finding the optimal policy into a two-step process: finding the optimal non-parameterized policy, and then parameterizing this optimal policy. We also wanted to deeply understand the on-policy case",
"before adding off-policy training. We thus compare with algorithms operating under the same",
"algorithmic constraints, one of which is being on-policy. We leave the extension to off-policy to future work. We",
"do not claim state-of-the-art results."
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"first posing and solving the sample efficiency optimization problem in the non-parameterized policy space, and then solving a supervised regression problem to find a parameterized policy that is near the optimal non-parameterized policy."
] |
[
"Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream.",
"However, we neither know whether such task-optimized networks enable equally good models of the rodent visual system, nor if a similar hierarchical correspondence exists.",
"Here, we address these questions in the mouse visual system by extracting features at several layers of a convolutional neural network (CNN) trained on ImageNet to predict the responses of thousands of neurons in four visual areas (V1, LM, AL, RL) to natural images.",
"We found that the CNN features outperform classical subunit energy models, but found no evidence for an order of the areas we recorded via a correspondence to the hierarchy of CNN layers.",
"Moreover, the same CNN but with random weights provided an equivalently useful feature space for predicting neural responses.",
"Our results suggest that object recognition as a high-level task does not provide more discriminative features to characterize the mouse visual system than a random network.",
"Unlike in the primate, training on ethologically relevant visually guided behaviors -- beyond static object recognition -- may be needed to unveil the functional organization of the mouse visual cortex.",
"Visual object recognition is a fundamental and difficult task performed by the primate brain via a hierarchy of visual areas (the ventral stream) that progressively untangles object identity information, gaining invariance to a wide range of object-preserving visual transformations [1, 2] .",
"Fueled by the advances of deep learning, recent work on modeling neural responses in sensory brain areas builds upon hierarchical convolutional neural networks (CNNs) trained to solve complex tasks like object recognition [3] .",
"Interestingly, these models have not only achieved unprecedented performance in predicting neural responses in several brain areas of macaques and humans [4] [5] [6] [7] , but they also revealed a hierarchical correspondence between the layers of the CNNs and areas of the ventral stream [4, 6] : the higher the area in the ventral stream, the higher the CNN layer that explained it best.",
"The same approach also provided a quantitative signature of a previously unclear hierarchical organization of A1 and A2 in the human auditory cortex [7] .",
"These discoveries about the primate have sparked a still unresolved question: to what extent is visual object processing also hierarchically organized in the mouse visual cortex and how well can the mouse visual system be modeled using goal-driven deep neural networks trained on static object classification?",
"This question is important since mice are increasingly used to study vision due to the plethora of available experimental techniques such as the ability to genetically identify and manipulate neural circuits that are not easily available in primates.",
"Recent work suggests that rats are capable of complex visual discrimination tasks [8] and recordings from extrastriate areas show a gradual increase in the ability of neurons in higher visual areas to support discrimination of visual objects [9, 10] .",
"Here, we set out to study how well the mouse visual system can be characterized by goal-driven deep neural networks.",
"We extracted features from the hidden layers of a standard CNN (VGG16, [11] ) trained on object categorization, to predict responses of thousands of neurons in four mouse visual areas (V1, LM, AL, RL) to static natural images.",
"We found that VGG16 yields powerful features for predicting neural activity, outperforming a Gabor filter bank energy model in these four visual areas.",
"However, VGG16 does not significantly outperform a feature space produced by a network with an identical architecture but random weights.",
"In contrast to previous work in primates, our data provide no evidence so far for a hierarchical correspondence between the deep network layers and the visual areas we recorded.",
"trough the core (A) network (first n layers of VGG16) to produce a feature space shared by all neurons.",
"Then, the spatial transformer readout (B) finds a mapping between these features and the neural responses for each neuron separately.",
"The shifter network (an MLP with one hidden layer) corrects for eye movements.",
"The output of the readout is multiplied by a gain predicted by the modulator network (an MLP with one hidden layer) that uses running speed and pupil dilation.",
"A static nonlinearity converts the result into the predicted spike rate.",
"All components of the model are trained jointly end-to-end to minimize the difference between predicted and observed neural responses.",
"In contrast to similar work in the primate, we find no match between the hierarchy of mouse visual cortical areas and the layers of CNNs trained on object categorization.",
"Although VGG16 achieves state-of-the-art performance, it is matched by random weights.",
"There are three implications of our results: First, our work is in line with previous work in machine learning that shows the power of random features [15] .",
"Therefore, we argue that models based on random features should always be reported as baselines in studies on neural system identification.",
"Second, which VGG layer best predicted any given brain area depended strongly on the image resolution we used to feed into VGG16.",
"We observed a similar effect in our earlier work on primate V1 [5] .",
"Thus, the studies reporting a hierarchical correspondence between goal-driven deep neural networks and the primate ventral stream should be taken with a grain of salt, as they -to the best of our knowledge -do not include this control.",
"Third, optimizing the network for static object recognition alone as a high-level goal does not appear to be the right approximation to describe representations and the visual hierarchy in the mouse cortex.",
"Although our results do not exclude a potential object processing hierarchy in the mouse visual system, they suggest that training with more ethologically relevant visually guided tasks for the mouse could be a more fruitful goal-driven approach to characterize the mouse visual system [16] .",
"For instance, an approach with dynamic stimuli such as those found during prey capture tasks [17] could yield more meaningful features to unveil the functional organization of the mouse visual system."
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"A goal-driven approach to model four mouse visual areas (V1, LM, AL, RL) based on deep neural networks trained on static object recognition does not unveil a functional organization of visual cortex unlike in primates"
] |
[
"The reparameterization trick has become one of the most useful tools in the field of variational inference.",
"However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution functions or are expressible as deterministic transformations of such distributions.",
"In this paper, we generalized the reparameterization trick by allowing a general transformation.",
"We discover that the proposed model is a special case of control variate indicating that the proposed model can combine the advantages of CV and generalized reparameterization.",
"Based on the proposed gradient model, we propose a new polynomial-based gradient estimator which has better theoretical performance than the reparameterization trick under certain condition and can be applied to a larger class of variational distributions.",
"In studies of synthetic and real data, we show that our proposed gradient estimator has a significantly lower gradient variance than other state-of-the-art methods thus enabling a faster inference procedure.",
"Most machine learning objective function can be rewritten in the form of an expectation:",
"where θ is a parameter vector.",
"However, due to the intractability of the expectation, it's often impossible or too expensive to calculate the exact gradient w.r.t θ, therefore it's inevitable to estimate the gradient ∇ θ L in practical applications.",
"Stochastic optmization methods such as reparameterization trick and score function methods have been widely applied to address the stochastic gradient estimation problem.",
"Many recent advances in large-scale machine learning tasks have been brought by these stochastic optimization tricks.",
"Like in other stochastic optimzation related works, our paper mainly focus on variational inference tasks.",
"The primary goal of variational inference (VI) task is to approximate the posterior distribution in probabilistic models (Jordan et al., 1999; Wainwright & Jordan, 2008) .",
"To approximate the intractable posterior p(z|x) with the joint probability distribution p(x, z) over observed data x and latent random variables z given, VI introduces a parameteric family of distribution q θ (z) and find the best parameter θ by optimizing the Kullback-Leibler (KL) divergence D KL (q(z; θ) p(z|x)).",
"The performance of VI methods depends on the capacity of the parameteric family of distributions (often measured by Rademacher complexity) and the ability of the optimizer.",
"In this paper, our method tries to introduce a better optimizer for a larger class of parameteric family of distributions.",
"The main idea of our work is to replace the parameter-independent transformation in reparameterization trick with generalized transformation and construct the generalized transformation-based (G-TRANS) gradient with the velocity field which is related to the characteristic curve of the sublinear partial differential equation associated with the generalized transformation.",
"Our gradient model further generalizes the G-REP (Ruiz et al., 2016) and provides a more elegant and flexible way to construct gradient estimators.",
"We mainly make the following contributions:",
"1. We develop a generalized transformation-based gradient model based on the velocity field related to the generalized transformation and explicitly propose the unbiasedness constraint on the G-TRANS gradient.",
"The proposed gradient model provides a more poweful and flexible way to construct gradient estimators.",
"2. We show that our model is a generalization of the score function method and the reparameterization trick.",
"Our gradient model can reduce to the reparameterization trick by enforcing a transport equation constraint on the velocity field.",
"We also show our model's connection to control variate method.",
"3. We propose a polynomial-based gradient estimator that cannot be induced by any other existing generalized reparameterization gradient framework, and show its superiority over similar works on several experiments.",
"The rest of this paper is organized as follows.",
"In Sec.2 we review the stochastic gradient variational inference (SGVI) and stochastic gradient estimators.",
"In Sec.3 we propose the generalized transformation-based gradient.",
"In Sec.4 we propose the polynomial-based G-TRANS gradient estimator.",
"In Sec.5 we study the performance of our gradient estimator on synthetic and real data.",
"In Sec.6 we review the related works.",
"In Sec.7 we conclude this paper and discuss future work.",
"We proposed a generalized transformation-based (G-TRANS) gradient model which extends the reparameterization trick to a larger class of variational distributions.",
"Our gradient model hides the details of transformation by introducing the velocity field and provides a flexible way to construct gradient estimators.",
"Based on the proposed gradient model, we introduced a polynomial-based G-TRANS gradient estimator that cannot be induced by any other existing generalized reparameterization gradient framework.",
"In practice, our gradient estimator provides a lower gradient variance than other state-of-the-art methods, leading to a fast converging process.",
"For future work, We can consider how to construct G-TRANS gradient estimators for distributions that don't have analytical high-order moments.",
"We can also utilize the results from the approximation theory to find certain kinds of high-order polynomial functions that can approximate the test function effectively with cheap computations for the coefficients.",
"Constructing velocity fields with the optimal transport theory is also a promising direction."
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"a generalized transformation-based gradient model for variational inference"
] |
[
"To simultaneously capture syntax and semantics from a text corpus, we propose a new larger-context language model that extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation.",
"Moving beyond a conventional language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences.",
"For inference, we develop a hybrid of stochastic-gradient MCMC and recurrent autoencoding variational Bayes.",
"Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms state-of-the-art larger-context language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.",
"Both topic and language models are widely used for text analysis.",
"Topic models, such as latent Dirichlet allocation (LDA) (Blei et al., 2003; Griffiths & Steyvers, 2004; Hoffman et al., 2013) and its nonparametric Bayesian generalizations (Teh et al., 2006; Zhou & Carin, 2015) , are well suited to extract document-level word concurrence patterns into latent topics from a text corpus.",
"Their modeling power has been further enhanced by introducing multilayer deep representation (Srivastava et al., 2013; Mnih & Gregor, 2014; Gan et al., 2015; Zhou et al., 2016; Zhao et al., 2018; .",
"While having semantically meaningful latent representation, they typically treat each document as a bag of words (BoW), ignoring word order (Griffiths et al., 2004; Wallach, 2006) .",
"Language models have become key components of various natural language processing (NLP) tasks, such as text summarization (Rush et al., 2015; Gehrmann et al., 2018) , speech recognition (Mikolov et al., 2010; Graves et al., 2013) , machine translation (Sutskever et al., 2014; Cho et al., 2014) , and image captioning (Vinyals et al., 2015; Mao et al., 2015; Xu et al., 2015; Gan et al., 2017; Rennie et al., 2017) .",
"The primary purpose of a language model is to capture the distribution of a word sequence, commonly with a recurrent neural network (RNN) (Mikolov et al., 2011; Graves, 2013) or a Transformer based neural network (Vaswani et al., 2017; Dai et al., 2019; Devlin et al., 2019; Radford et al., 2018; 2019) .",
"In this paper, we focus on improving RNN-based language models that often have much fewer parameters and are easier to perform end-to-end training.",
"While RNN-based language models do not ignore word order, they often assume that the sentences of a document are independent to each other.",
"This simplifies the modeling task to independently assigning probabilities to individual sentences, ignoring their orders and document context (Tian & Cho, 2016) .",
"Such language models may consequently fail to capture the long-range dependencies and global semantic meaning of a document (Dieng et al., 2017; .",
"To relax the sentence independence assumption in language modeling, Tian & Cho (2016) propose larger-context language models that model the context of a sentence by representing its preceding sentences as either a single or a sequence of BoW vectors, which are then fed directly into the sentence modeling RNN.",
"An alternative approach attracting significant recent interest is leveraging topic models to improve RNN-based language models.",
"Mikolov & Zweig (2012) use pre-trained topic model features as an additional input to the RNN hidden states and/or output.",
"Dieng et al. (2017) ; Ahn et al. (2017) combine the predicted word distributions, given by both a topic model and a language model, under variational autoencoder (Kingma & Welling, 2013) .",
"Lau et al. (2017) introduce an attention based convolutional neural network to extract semantic topics, which are used to extend the RNN cell.",
"learn the global semantic coherence of a document via a neural topic model and use the learned latent topics to build a mixture-of-experts language model.",
"Wang et al. (2019) further specify a Gaussian mixture model as the prior of the latent code in variational autoencoder, where each mixture component corresponds to a topic.",
"While clearly improving the performance of the end task, these existing topic-guided methods still have clear limitations.",
"For example, they only utilize shallow topic models with only a single stochastic hidden layer in their data generation process.",
"Note several neural topic models use deep neural networks to construct their variational encoders, but still use shallow generative models (decoders) (Miao et al., 2017; Srivastava & Sutton, 2017) .",
"Another key limitation lies in ignoring the sentence order, as they treat each document as a bag of sentences.",
"Thus once the topic weight vector learned from the document context is given, the task is often reduced to independently assigning probabilities to individual sentences (Lau et al., 2017; 2019) .",
"In this paper, as depicted in Fig. 1 , we propose to use recurrent gamma belief network (rGBN) to guide a stacked RNN for language modeling.",
"We refer to the model as rGBN-RNN, which integrates rGBN , a deep recurrent topic model, and stacked RNN (Graves, 2013; Chung et al., 2017) , a neural language model, into a novel larger-context RNN-based language model.",
"It simultaneously learns a deep recurrent topic model, extracting document-level multi-layer word concurrence patterns and sequential topic weight vectors for sentences, and an expressive language model, capturing both short-and long-range word sequential dependencies.",
"For inference, we equip rGBN-RNN (decoder) with a novel variational recurrent inference network (encoder), and train it end-to-end by maximizing the evidence lower bound (ELBO).",
"Different from the stacked RNN based language model in Chung et al. (2017) , which relies on three types of customized training operations (UPDATE, COPY, FLUSH) to extract multi-scale structures, the language model in rGBN-RNN learns such structures purely under the guidance of the temporally and hierarchically connected stochastic layers of rGBN.",
"The effectiveness of rGBN-RNN as a new larger-context language model is demonstrated both quantitatively, with perplexity and BLEU scores, and qualitatively, with interpretable latent structures and randomly generated sentences and paragraphs.",
"Notably, rGBN-RNN can generate a paragraph consisting of a sequence of semantically coherent sentences.",
"We propose a recurrent gamma belief network (rGBN) guided neural language modeling framework, a novel method to learn a language model and a deep recurrent topic model simultaneously.",
"For scalable inference, we develop hybrid SG-MCMC and recurrent autoencoding variational inference, allowing efficient end-to-end training.",
"Experiments results conducted on real world corpora demonstrate that the proposed models outperform a variety of shallow-topic-model-guided neural language models, and effectively generate the sentences from the designated multi-level topics or noise, while inferring interpretable hierarchical latent topic structure of document and hierarchical multiscale structures of sequences.",
"For future work, we plan to extend the proposed models to specific natural language processing tasks, such as machine translation, image paragraph captioning, and text summarization.",
"Another promising extension is to replace the stacked-RNN in rGBN-RNN with Transformer, i.e., constructing an rGBN guided Transformer as a new larger-context neural language model."
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"We introduce a novel larger-context language model to simultaneously captures syntax and semantics, making it capable of generating highly interpretable sentences and paragraphs"
] |
[
"We present a novel approach to train a natural media painting using reinforcement learning.",
"Given a reference image, our formulation is based on stroke-based rendering that imitates human drawing and can be learned from scratch without supervision.",
"Our painting agent computes a sequence of actions that represent the primitive painting strokes.",
"In order to ensure that the generated policy is predictable and controllable, we use a constrained learning method and train the painting agent using the environment model and follows the commands encoded in an observation.",
"We have applied our approach on many benchmarks and our results demonstrate that our constrained agent can handle different painting media and different constraints in the action space to collaborate with humans or other agents.\n",
"Throughout human history, painting has been an essential element of artistic creation.",
"There are many diverse and complex artistic domains with various styles such as watercolor, oil painting, sketching, and so on.",
"As image processing and computer graphics have advanced, there has been a considerable effort to simulate these styles using non-photorealistic rendering (NPR) techniques (Kumar et al. (2019) ).",
"Hertzmann (1998) ; Winkenbach & Salesin (1996) generate compelling results using stroke-based rendering.",
"However, most prior methods in NPR are engineered for a specific application or task, and cannot easily adapt to new styles or medium.",
"Recent developments in machine learning have resulted in significant advancements in computer vision and computer graphics, including computer-based painting systems.",
"Many visual generative methods based on generative adversarial networks (Goodfellow et al. (2014) ) as Zhu et al. (2017) ; Zhou et al. (2018) ; ; Karras et al. (2017) ; Sangkloy et al. (2017) have demonstrated promising results.",
"Many of these machine learning methods have also been applied to stroke-based rendering tasks, including modeling the brush (Xie et al. (2012) ; Zheng et al. (2019) ), generating brushstroke paintings in an artist's style (Xie et al. (2015) ), reconstructing drawings for specific paintings styles (Tang et al. (2018) ), and constructing stroke-based drawings (Ha & Eck (2017a) ; Zhou et al. (2018) ; ; Jia et al. (2019a) ).",
"In this paper, we focus on a more general and challenging problem of training a natural media painting agent for interactive applications.",
"Given a reference image, our goal is to develop a stroke-based rendering approach that can imitate the human drawing or strokes used in generating the image.",
"A key challenge is to develop a method that can learn from scratch without any supervision.",
"In this regard, we present a technique that can handle all inputs and train an agent to manipulate natural painting media such as charcoal, pencil, watercolor, and so on.",
"We build a model-based natural media environment using deep CNN and train a natural media painting agent using model-based reinforcement learning.",
"In order to introduce controls to the agents for interactive applications, we use a constraint representation along with a different framework for training and use the constrained painting agent.",
"These constraints enable the agent to interact with a human or other agents and generate various styles without retraining the model.",
"The novel contributions of our work include:",
"• A method to train an agent that produces a stream of actions subject to constraint for each action.",
"These constraints can include restricting the start location, stroke width, color, and other stroke parameters.",
"• A method to roll out constrained agents so the user can produce new stylistic effects interactively or automatically, as the agent is painting by modulating the action stream.",
"• By incorporate coarse-to-fine strategy, our painting agents can generate high-resolution stylized images using various constraints and paintbrush configurations.",
"We evaluate our algorithm on different paintbrush configurations and datasets to highlights its benefits over prior reinforcement learning based methods.",
"We also employ differing constraint settings to validate our constrained agents and produce new stylistic effects with a single trained model.",
"In this paper, we train natural media painting agents that can generate artistic paintings using various natural media, and collaborate with humans and other agents to get different visual effects.",
"We build a model of natural media environment using deep CNN and train a natural media painting agent using model-based reinforcement learning.",
"To introduce controls to the agents for interactive purposes, we propose constraint representation, a framework for training a constrained painting agent, and various roll-out schemes to apply the agent.",
"We demonstrate our algorithm by applying the trained model using various paintbrushes from MyPaint and constraints set up.",
"The experimental results show that our algorithm can reproduce reference images in multiple artistic styles.",
"For future work, we aim to extend the proposed algorithm by building a unified model for differing paintbrush configuration.",
"In addition, we will train a hierarchical agent that uses a constrained agent as the low-level policy.",
"We would like to apply our approach on other reference images and use for interactive painting systems.",
"A APPENDIX Figure 9 : Roll-out results using Various PaintbrushesWe roll out our natural media painting agents trained with various brushes in MyPaint.",
"To increase the resolutions of the generated images, we incorporate the coarse-to-fine strategy.",
"We use 8 × 8 patches for first row and 4 × 4 for second row.",
"Figure 10: Reproduction of Starry Night using Charcoal We roll out our natural media painting agent trained with charcoal brush in MyPaint to reproduce Van Gogh's starry night.We incorporate the coarse-to-fine strategy by dividing the reference image and canvas into 16 × 16 patches.",
"Figure 11 : Reproduction of Starry Night using Watercolor We roll out our natural media painting agent trained with watercolor brush in MyPaint to reproduce Van Gogh's starry night.We incorporate the coarse-to-fine strategy by dividing the reference image and canvas into 16 × 16 patches."
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"We train a natural media painting agent using environment model. Based on our painting agent, we present a novel approach to train a constrained painting agent that follows the command encoded in the observation."
] |
[
"Delusional bias is a fundamental source of error in approximate Q-learning.",
"To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates.",
"In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are \"consistent\" with the underlying greedy policy class.",
"We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class.",
"We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments.",
"Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.",
"Q-learning (Watkins & Dayan, 1992; Sutton & Barto, 2018) lies at the heart of many of the recent successes of deep reinforcement learning (RL) (Mnih et al., 2015; , with recent advancements (e.g., van Hasselt (2010); Bellemare et al. (2017) ; Wang et al. (2016) ; Hessel et al. (2017) ) helping to make it among the most widely used methods in applied RL.",
"Despite these successes, many properties of Q-learning are poorly understood, and it is challenging to successfully apply deep Q-learning in practice.",
"When combined with function approximation, Q-learning can become unstable (Baird, 1995; Boyan & Moore, 1995; Tsitsiklis & Roy, 1996; Sutton & Barto, 2018) .",
"Various modifications have been proposed to improve convergence or approximation error (Gordon, 1995; 1999; Szepesvári & Smart, 2004; Melo & Ribeiro, 2007; Maei et al., 2010; Munos et al., 2016) ; but it remains difficult to reliably attain both robustness and scalability.",
"Recently, Lu et al. (2018) identified a source of error in Q-learning with function approximation known as delusional bias.",
"It arises because Q-learning updates the value of state-action pairs using estimates of (sampled) successor-state values that can be mutually inconsistent given the policy class induced by the approximator.",
"This can result in unbounded approximation error, divergence, policy cycling, and other undesirable behavior.",
"To handle delusion, the authors propose a policy-consistent backup operator that maintains multiple Q-value estimates organized into information sets.",
"Each information set has its own backed-up Q-values and corresponding \"policy commitments\" responsible for inducing these values.",
"Systematic management of these sets ensures that only consistent choices of maximizing actions are used to update Q-values.",
"All potential solutions are tracked to prevent premature convergence on any specific policy commitments.",
"Unfortunately, the proposed algorithms use tabular representations of Q-functions, so while this establishes foundations for delusional bias, the function approximator is used neither for generalization nor to manage the size of the state/action space.",
"Consequently, this approach is not scalable to RL problems of practical size.",
"In this work, we propose CONQUR (CONsistent Q-Update Regression), a general framework for integrating policy-consistent backups with regression-based function approximation for Q-learning and for managing the search through the space of possible regressors (i.e., information sets).",
"With suitable search heuristics, our framework provides a computationally effective means for minimizing the effects of delusional bias in Q-learning, while admitting scaling to practical problems.",
"Our main contributions are as follows.",
"First we define novel augmentations of standard Q-regression to increase the degree of policy consistency across training batches.",
"While testing exact consistency is expensive, we introduce an efficient soft-consistency penalty that promotes consistency of new labels with earlier policy commitments.",
"Second, drawing on the information-set structure of Lu et al. (2018) , we define a search space over Q-regressors to allow consideration of multiple sets of policy commitments.",
"Third, we introduce heuristics for guiding the search over regressors, which is critical given the combinatorial nature of information sets.",
"Finally, we provide experimental results on the Atari suite (Bellemare et al., 2013) demonstrating that CONQUR can offer (sometimes dramatic) improvements over Q-learning.",
"We also show that (easy-to-implement) consistency penalization on its own (i.e., without search) can improve over both standard and double Q-learning."
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"We developed a search framework and consistency penalty to mitigate delusional bias."
] |
[
"The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction.",
"The recent introduction of Disguised Face Identification (DFI) framework proves the applicability of deep neural networks for this very problem.",
"All the ATMs nowadays incorporate a hidden camera in them and capture the footage of their users.",
"However, it is impossible for the police to track down the impersonators with disguised faces from the ATM footage.",
"The proposed deep convolutional neural network is trained to identify, in real time, whether the user in the captured image is trying to cloak his identity or not.",
"The output of the DCNN is then reported to the ATM to take appropriate steps and prevent the swindler from completing the transaction.",
"The network is trained using a dataset of images captured in similar situations as of an ATM.",
"The comparatively low background clutter in the images enables the network to demonstrate high accuracy in feature extraction and classification for all the different disguises.",
"The widespread acceptance of Automated Teller Machine (ATM) and their omnipresence in the banking sector has engendered numerous security concerns.",
"One of the most imperative concerns being, verifying the authenticity of the user.",
"Evidently, most of the ATMs across the globe simply rely on a card and a Personal Identification Number (PIN) for authentication.",
"However, in either case, it is plausible that the user is not authorised to transact.",
"For instance, illegal practices like phishing, shoulder surfing, card fraud, stolen card can cause substantial monetary loss to the owner.To overcome and identify such practices, ATMs have an inbuilt camera which records 24x7.",
"The current state of art ATM security works in the following way: After a fraudulent transaction, the owner of the corresponding bank account reports about the fraud.",
"The police then investigates and goes through the footage recorded by the ATM camera to find the face of the imposter.",
"Once the face is identified, the police searches for the imposter.",
"Clearly, this security measure can be easily gamed by using artifacts or alterations like wigs, caps, eyeglasses, beard to cover the face for intentional disguises.",
"As a result, BID6 stated that such face alterations can substantially degrade the performance of the system.",
"Hence, this approach has a very low success rate which is unacceptable in banking sectors.",
"Additionally, BID7 explained different openings and vulnerabilities that exist at the time of transactions due to fake entries and fake cards.Apparently, this chaos can be prevented by ensuring that the transaction proceeds only if the face is undisguised and reveal identity of the user.",
"The proposed system extracts the user's face from the footage and checks if the face is disguised.",
"The system is trained cleverly to identify such faces by an extensive pool of disguised and undisguised faces.",
"If the face is disguised, the system will not allow the transaction to be proceeded, thereby preventing the imposter from stealing.",
"To achieve this, the proposed system uses Deep Convolutional Neural Networks for image classification using statistical dimensionality reduction method.",
"Deep networks have proved to be exceptional in computer vision problems BID9 BID4 .",
"BID9 stated a three-layer cascading style which superficially captures the high level features and refines them to detect deeper features.",
"Analogously, the proposed system uses a five-layer architecture, first 3 layers comprises of a convolutional layers followed by a pooling layers to learn the features of the following types of images : Disguised, Partially disguised and Undisguised.2",
"PROPOSED SYSTEM 2.1 EXISTING MECHANISMS Plenty of research work has been published in response to the ATM security problems and a lot of it relates to using machine learning to authenticate users.",
"BID3 proposed a facebased authentication as identity test for users and the system uses facial recognition with biometric features.",
"T. BID10 stated the applicability of image processing by amalgamation of Face Recognition System (FRS) in the identity verification process engaged in ATMs.",
"BID2 proposed a framework to classify local facial regions of both visible and thermal face images into biometric (regions without disguise) and non-biometric (regions with disguise) classes and used the biometric patches for facial feature extraction and matching."
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"Proposed System can prevent impersonators with facial disguises from completing a fraudulent transaction using a pre-trained DCNN."
] |
[
"Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.\n",
"When its activation function is linear and the encoding dimension (width of hidden layer) is smaller than the input dimension, it is well known that auto-encoder is optimized to learn the principal components of the data distribution (Oja1982).\n",
"However, when the activation is nonlinear and when the width is larger than the input dimension (overcomplete), auto-encoder behaves differently from PCA, and in fact is known to perform well empirically for sparse coding problems. \n\n",
"We provide a theoretical explanation for this empirically observed phenomenon, when rectified-linear unit (ReLu) is adopted as the activation function and the hidden-layer width is set to be large.\n",
"In this case, we show that, with significant probability, initializing the weight matrix of an auto-encoder by sampling from a spherical Gaussian distribution followed by stochastic gradient descent (SGD) training converges towards the ground-truth representation for a class of sparse dictionary learning models.\n",
"In addition, we can show that, conditioning on convergence, the expected convergence rate is O(1/t), where t is the number of updates.\n",
"Our analysis quantifies how increasing hidden layer width helps the training performance when random initialization is used, and how the norm of network weights influence the speed of SGD convergence.",
"d .",
"An auto-encoder can be decomposed into two parts, encoder and decoder.",
"The encoder can be viewed as a composition function s e • a e : R d → R n ; function a e : R d → R n is defined as a e",
"(x) := W e x + b e with W e ∈ R n×d , b e ∈ R n W e and b e are the network weights and bias associated with the encoder.",
"s e is a coordinate-wise activation function defined as s e",
"(y) j := s(y j ) where s : R → R is typically a nonlinear functionThe decoder takes the output of encoder and maps it back to R d .",
"Let x e := s e (a e",
"(x)).",
"The decoding function, which we denote asx, is defined aŝ DISPLAYFORM0 where (W d , b d ) and s d are the network parameters and the activation function associated with the decoder respectively.Suppose the activation functions are fixed before training.",
"One can viewx as a reconstruction of the original signal/data using the hidden representation parameterized by (W e , b e ) and (W d , b d ).",
"The goal of training an auto-encoder is to learn the \"right\" network parameters, (W e , b e , W d , b d ), so that x has low reconstruction error.Weight tying A folklore knowledge when training auto-encoders is that, it usually works better if one sets W d = W T e .",
"This trick is called \"weight tying\", which is viewed as a trick of regularization, since it reduces the total number of free parameters.",
"With tied weights, the classical auto-encoder is simplified asx(s e (a e",
"(x))) = s d (W T s e (W x + b e ) + b d )In the rest of the manuscript, we focus on weight-tied auto-encoder with the following specific architecture:x W,b",
"(x) = W T s ReLu (a(x",
")) = W T s ReLu (W x + b) with s ReLu (",
"y) i := max{0, y i }Here we abuse notation to usex W,b to denote the encoder-decoder function parametrized by weights W and bias",
"b. In the deep learning community, s ReLu is commonly referred to as the rectified-linear (ReLu) activation.Reconstruction error A classic measure of reconstruction error used by auto-encoders is the expected squared loss.",
"Assuming that the data fed to the auto-encoder is i.i.d distributed according to an unknown distribution, i.e., x ∼ p(x",
"), the population expected squared loss is defined as DISPLAYFORM1 Learning a \"good representation\" thus translates to adjusting the parameters (W, b) to minimize the squared loss function. The",
"implicit hope is that the squared loss will provide information about what is a good representation. In",
"other words, we have a certain level of belief that the squared loss characterizes what kind of network parameters are close to the parameters of the latent distribution p(x)",
". This",
"unwarranted belief leads to two natural questions that motivated our theoretical investigation:• Does the global minimum (or any of global minima, if more than one) of L(W, b) correspond to the latent model parameters of distribution p(x)?•",
"From",
"an optimization perspective, since L(W, b) is non-convex in W and is shown to have exponentially many local minima Safran & Shamir (2016) , one would expect a local algorithm like stochastic gradient descent, which is the go-to algorithm in practice for optimizing L(W, b), to be stuck in local minima and only find sub-optimal solutions. Then how",
"should we explain the practical observation that auto-encoders trained with SGD often yield good representation?Stochastic-gradient",
"based training Stochastic gradient descent (SGD) is a scalable variant of gradient descent commonly used in deep learning. At every time step",
"t, the algorithm evaluates a stochastic gradient g(·) of the population loss function with respect to the network parameters using back propagation by sampling one or a mini-batch of data points. The weight and bias",
"update has the following generic form DISPLAYFORM2 where η t w and η t b are the learning rates for updating W and b respectively, typically set to be a small number or a decaying function of time t. The unbiased gradient",
"estimate g(W t ) and g(b t ) can be obtained by differentiating the empirical loss function defined on a single or a mini-batch of size m, Then the stochastic or mini-batch gradient descent update can be written as DISPLAYFORM3 DISPLAYFORM4 n (width of hidden layer)Max-norm regularization A common trick called \"max-norm regularization\" Srivastava et al. (2014) or \"weight clipping\" is used in training deep neural networks. 1 In particular, after",
"each step of stochastic gradient descent, the updated weights is forced to satisfy DISPLAYFORM5 for some constant c. This means the row norm",
"of the weights can never exceed the prefixed constant c. In practice, whenever W",
"i, 2 > c, the max-norm constraint is enforced by projecting the weights back to a ball of radius c."
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"theoretical analysis of nonlinear wide autoencoder"
] |
[
"Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. ",
"In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously. ",
"Yet, learning multiple levels of policies in parallel is hard because it is inherently unstable: changes in a policy at one level of the hierarchy may cause changes in the transition and reward functions at higher levels in the hierarchy, making it difficult to jointly learn multiple levels of policies. ",
"In this paper, we introduce a new Hierarchical Reinforcement Learning (HRL) framework, Hierarchical Actor-Critic (HAC), that can overcome the instability issues that arise when agents try to jointly learn multiple levels of policies. ",
"The main idea behind HAC is to train each level of the hierarchy independently of the lower levels by training each level as if the lower level policies are already optimal. ",
"We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly accelerate learning relative to other non-hierarchical and hierarchical methods. ",
"Indeed, our framework is the first to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.",
"Hierarchy has the potential to accelerate learning in sequential decision making tasks because hierarchical agents can decompose problems into smaller subproblems.",
"In order to take advantage of these shorter horizon subproblems and realize the potential of HRL, an HRL algorithm must be able to learn the multiple levels within the hierarchy in parallel.",
"That is, at the same time one level in the hierarchy is learning the sequence of subtasks needed to solve a task, the level below should be learning the sequence of shorter time scale actions needed to solve each subtask.",
"Yet the existing HRL algorithms that are capable of automatically learning hierarchies in continuous domains BID11 BID4 BID1 BID15 BID9 do not efficiently learn the multiple levels within the hierarchy in parallel.",
"Instead, these algorithms often resort to learning the hierarchy one level at a time in a bottom-up fashion.Learning multiple levels of policies in parallel is challenging due to non-stationary state transition functions.",
"In nested, multi-level hierarchies, the transition function for any level above the ground level depends on the current policies below that level.",
"For instance, in a 2-level hierarchy, the Figure 1: An ant agent uses a 3-level hierarchy to traverse though rooms to reach its goal, represented by the yellow cube.",
"Π 2 uses as input the current state (joint positions θ and velocitiesθ) and goal state (yellow box) and outputs a subgoal state (green box) for Π 1 to achieve.",
"Π 1 takes in the current state and its goal state (green box) and outputs a subgoal state (purple box) for Π 0 to achieve.",
"Π 0 takes in the current state and goal state (purple box) and outputs a vector of joint torques.high-level policy may output a subgoal state for the low level to achieve, and the state to which this subgoal state leads will depend on the current low-level policy.",
"When all policies within the hierarchy are trained simultaneously, the transition function at each level above ground level will continue to change as long as the policies below that level continue to be updated.",
"In this setting of non-stationary transition functions, RL will likely struggle to learn the above ground level policies in the hierarchy because in order for RL methods to effectively value actions, the distribution of states to which those actions lead should be stable.",
"However, learning multiple policies in parallel is still possible because the transition function for each level above ground level will stabilize once all lower level policies have converged to optimal or near optimal policies.",
"Thus, RL can be used to learn all policies in parallel if each level above ground level had a way to simulate a transition function that uses the optimal versions of lower level policies.",
"Our framework is able to simulate a transition function that uses an optimal lower level policy hierarchy and thus can learn multiple levels of policies in parallel.We introduce a new HRL framework, Hierarchical Actor-Critic (HAC), that can significantly accelerate learning by enabling hierarchical agents to jointly learn a hierarchy of policies.",
"Our framework is primarily comprised of two components:",
"(i) a particular hierarchical architecture and",
"(ii) a method for learning the multiple levels of policies in parallel given sparse rewards.The hierarchies produced by HAC have a specific architecture consisting of a set of nested, goalconditioned policies that use the state space as the mechanism for breaking down a task into subtasks.",
"The hierarchy of nested policies works as follows.",
"The highest level policy takes as input the current state and goal state provided by the task and outputs a subgoal state.",
"This state is used as the goal state for the policy at the next level down.",
"The policy at that level takes as input the current state and the goal state provided by the level above and outputs its own subgoal state for the next level below to achieve.",
"This process continues until the lowest level is reached.",
"The lowest level then takes as input the current state and the goal state provided by the level above and outputs a primitive action.",
"Further, each level has a certain number of attempts to achieve its goal state.",
"When the level either runs out of attempts or achieves its goal state, execution at that level ceases and the level above outputs another subgoal.",
"Figure 1 shows how an ant agent trained with HAC uses its 3-level policy hierarchy (π 2 , π 1 , π 0 ) to move through rooms to reach its goal.",
"At the beginning of the episode, the ant's highest level policy, π 2 , takes as input the current state, which in this case is a vector containing the ant's joint positions and velocities ([θ,θ] ), and its goal state, represented by the yellow box.",
"π 2 then outputs a subgoal state, represented by the green box, for π 1 to achieve.",
"π 1 takes as input the current state and its goal state represented by the green box and outputs the subgoal state represented by the purple box.",
"Finally, π 0 takes as input the current state and the goal state represented by purple box and outputs a primitive action, which in this case is a vector of joint torques.",
"π 0 has a fixed number of attempts to move to the purple box before π 1 outputs another subgoal state.",
"Similarly, π 1 has a fixed number of subgoal states that it can output to try to move the agent to the green box before π 2 outputs another subgoal.In addition, HAC enables agents to learn multiple policies in parallel using only sparse reward functions as a result of two types of hindsight transitions.",
"Hindsight action transitions help agents learn multiple levels of policies simultaneously by training each subgoal policy with respect to a transition function that simulates the optimal lower level policy hierarchy.",
"Hindsight action transitions are implemented by using the subgoal state achieved in hindsight instead of the original subgoal state as the action component in the transition.",
"For instance, when a subgoal level proposes subgoal state A, but the next level policy is unsuccessful and the agent ends in state B after a certain number of attempts, the subgoal level receives a transition in which the state B is the action component, not state A. The key outcome is that now the action and next state components in the transition are the same, as if the optimal lower level policy hierarchy had been used to achieve subgoal state B.Training with respect to a transition function that uses the optimal lower level policy hierarchy is critical to learning multiple policies in parallel, because the subgoal policies can be learned independently of the changing lower level policies.",
"With hindsight action transitions, a subgoal level can focus on learning the sequences of subgoal states that can reach a goal state, while the lower level policies focus on learning the sequences of actions to achieve those subgoal states.",
"The second type of hindsight transition, hindsight goal transitions, helps each level learn a goal-conditioned policy in sparse reward tasks by extending the idea of Hindsight Experience Replay BID0 ) to the hierarchical setting.",
"In these transitions, one of the states achieved in hindsight is used as the goal state in the transition instead of the original goal state.We evaluated our approach on both grid world tasks and more complex simulated robotics environments.",
"For each task, we evaluated agents with 1, 2, and 3 levels of hierarchy.",
"In all tasks, agents using multiple levels of hierarchy substantially outperformed agents that learned a single policy.",
"Further, in all tasks, agents using 3 levels of hierarchy outperformed agents using 2 levels of hierarchy.",
"Indeed, our framework is the first to show empirically that it can jointly learn 3-level hierarchical policies in tasks with continuous state and action spaces.",
"In addition, our approach outperformed another leading HRL algorithm, HIRO BID9 , on three simulated robotics tasks.",
"Hierarchy has the potential to accelerate learning but in order to realize this potential, hierarchical agents need to be able to learn their multiple levels of policies in parallel.",
"We present a new HRL framework that can efficiently learn multiple levels of policies simultaneously.",
"HAC can overcome the instability issues that arise when agents try to learn to make decisions at multiple time scales because the framework trains each level of the hierarchy as if the lower levels are already optimal.",
"Our results in several discrete and continuous domains, which include the first 3-level agents in tasks with continuous state and action spaces, confirm that HAC can significantly improve sample efficiency.ONR through N000141410047, Amazon through an ARA to Platt, Google through a FRA to Platt, and DARPA.",
"• Key agent parameters: number of levels in hierarchy k, maximum subgoal horizon H, and subgoal testing frequency λ.",
"Output:• k trained actor and critic functions π 0 , ..., DISPLAYFORM0 Sample initial state and task goal DISPLAYFORM1 Begin training Update all actor and critic networks end for function TRAIN-LEVEL(i :: level, s :: state, g :: goal) s i ← s, g i ← g Set current state and goal for level i for H attempts or until g n , i ≤ n < k achieved do DISPLAYFORM2 DISPLAYFORM3 Replace original action with action executed in hindsight end ifEvaluate executed action on current goal and hindsight goals DISPLAYFORM4 Replay Buf f er i ← Perform HER using HER Storage i transitions return s iOutput current state end function"
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"We introduce the first Hierarchical RL approach to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces."
] |
[
"Positive-unlabeled (PU) learning addresses the problem of learning a binary classifier from positive (P) and unlabeled (U) data.",
"It is often applied to situations where negative (N) data are difficult to be fully labeled.",
"However, collecting a non-representative N set that contains only a small portion of all possible N data can be much easier in many practical situations.",
"This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning.",
"The fact that the training N data are biased also makes our work very different from those of standard semi-supervised learning.",
"We provide an empirical risk minimization-based method to address this PUbN classification problem.",
"Our approach can be regarded as a variant of traditional example-reweighting algorithms, with the weight of each example computed through a preliminary step that draws inspiration from PU learning.",
"We also derive an estimation error bound for the proposed method.",
"Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU leaning scenarios on several benchmark datasets.",
"In conventional binary classification, examples are labeled as either positive (P) or negative (N), and we train a classifier on these labeled examples.",
"On the contrary, positive-unlabeled (PU) learning addresses the problem of learning a classifier from P and unlabeled (U) data, without need of explicitly identifying N data BID6 BID42 ).PU",
"learning finds its usefulness in many real-world problems. For",
"example, in one-class remote sensing classification , we seek to extract a specific land-cover class from an image. While",
"it is easy to label examples of this specific land-cover class of interest, examples not belonging to this class are too diverse to be exhaustively annotated. The same",
"problem arises in text classification, as it is difficult or even impossible to compile a set of N samples that provides a comprehensive characterization of everything that is not in the P class BID24 BID8 . Besides,",
"PU learning has also been applied to other domains such as outlier detection BID13 BID36 ), medical diagnosis BID45 , or time series classification BID28 .By carefully",
"examining the above examples, we find out that the most difficult step is often to collect a fully representative N set, whereas only labeling a small portion of all possible N data is relatively easy. Therefore, in",
"this paper, we propose to study the problem of learning from P, U and biased N (bN) data, which we name PUbN learning hereinafter. We suppose that",
"in addition to P and U data, we also gather a set of bN samples, governed by a distribution distinct from the true N distribution. As described previously",
", this can be viewed as an extension of PU learning, but such bias may also occur naturally in some real-world scenarios. For instance, let us presume",
"that we would like to judge whether a subject is affected by a particular disease based on the result of a physical examination. While the data collected from",
"the patients represent rather well the P distribution, healthy subjects that request the examination are in general highly biased with respect to the whole healthy subject population.We are not the first to be interested in learning with bN data. In fact, both BID22 and BID7",
"attempted to solve similar problems in the context of text classification. BID22 simply discarded negative",
"samples and performed ordinary PU classification. It was also mentioned in the paper",
"that bN data could be harmful. BID7 adapted another strategy. The",
"authors considered even gathering",
"unbiased U data is difficult and learned the classifier from only P and bN data. However, their method is specific to",
"text classification because it relies on the use of effective similarity measures to evaluate similarity between documents. Therefore, our work differs from these",
"two in that the classifier is trained simultaneously on P, U and bN data, without resorting to domain-specific knowledge. The presence of U data allows us to address",
"the problem from a statistical viewpoint, and thus the proposed method can be applied to any PUbN learning problem in principle.In this paper, we develop an empirical risk minimization-based algorithm that combines both PU learning and importance weighting to solve the PUbN classification problem, We first estimate the probability that an example is sampled into the P or the bN set. Based on this estimate, we regard bN and U",
"data as N examples with instance-dependent weights. In particular, we assign larger weights to",
"U examples that we believe to appear less often in the P and bN sets. P data are treated as P examples with unity",
"weight but also as N examples with usually small or zero weight whose actual value depends on the same estimate.The contributions of the paper are three-fold:1. We formulate the PUbN learning problem as an",
"extension of PU learning and propose an empirical risk minimization-based method to address the problem. We also theoretically establish an estimation",
"error bound for the proposed method. 2. We experimentally demonstrate that the classification",
"performance can be effectively improved thanks to the use of bN data during training. In other words, PUbN learning yields better performance",
"than PU learning. 3. Our method can be easily adapted to ordinary PU learning",
". Experimentally we show that the resulting algorithm allows",
"us to obtain new state-of-the-art results on several PU learning tasks.Relation with Semi-supervised Learning With P, N and U data available for training, our problem setup may seem similar to that of semi-supervised learning BID2 BID29 . Nonetheless, in our case, N data are biased and often represent",
"only a small portion of the whole N distribution. Therefore, most of the existing methods designed for the latter",
"cannot be directly applied to the PUbN classification problem. Furthermore, our focus is on deducing a risk estimator using the",
"three sets of data, whereas in semi-supervised learning the main concern is often how U data can be utilized for regularization BID10 BID1 BID20 BID25 . The two should be compatible and we believe adding such regularization",
"to our algorithm can be beneficial in many cases.Relation with Dataset Shift PUbN learning can also be viewed as a special case of dataset shift 1 BID31 ) if we consider that P and bN data are drawn from the training distribution while U data are drawn from the test distribution. Covariate shift BID38 BID39 ) is another special case of dataset shift",
"that has been studied intensively. In the covariate shift problem setting, training and test distributions",
"have the same class conditional distribution and only differ in the marginal distribution of the independent variable. One popular approach to tackle this problem is to reweight each training",
"example according to the ratio of the test density to the training density BID15 . Nevertheless, simply training a classifier on a reweighted version of the",
"labeled set is not sufficient in our case since there may be examples with zero probability to be labeled. It is also important to notice that the problem of PUbN learning is intrinsically",
"different from that of covariate shift and neither of the two is a special case of the other.",
"This paper studied the PUbN classification problem, where a binary classifier is trained on P, U and bN data.",
"The proposed method is a two-step approach inspired from both PU learning and importance weighting.",
"The key idea is to attribute appropriate weights to each example to evaluate the classification risk using the three sets of data.",
"We theoretically established an estimation error bound for the proposed risk estimator and experimentally showed that our approach successfully leveraged bN data to improve the classification performance on several real-world datasets.",
"A variant of our algorithm was able to achieve state-of-the-art results in PU learning.",
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] | H1ldNoC9tX | true | [
"This paper studied the PUbN classification problem, where we incorporate biased negative (bN) data, i.e., negative data that is not fully representative of the true underlying negative distribution, into positive-unlabeled (PU) learning."
] |
[
"Reinforcement learning (RL) is frequently used to increase performance in text generation tasks,\n",
"including machine translation (MT), \n",
"notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). \n",
"However, little is known about what and how these methods learn in the context of MT. \n",
"We prove that one of the most common RL methods for MT does not optimize the \n",
"expected reward, as well as show that other methods take an infeasibly long time to converge.\n",
"In fact, our results suggest that RL practices in MT are likely to improve performance\n",
"only where the pre-trained parameters are already close to yielding the correct translation.\n",
"Our findings further suggest that observed gains may be due to effects unrelated to the training signal, concretely, changes in the shape of the distribution curve.",
"Reinforcement learning (RL) is an appealing path for advancement in Machine Translation (MT), as it allows training systems to optimize non-differentiable score functions, common in MT evaluation, as well as tackling the \"exposure bias\" (Ranzato et al., 2015) in standard training, namely that the model is not exposed during training to incorrectly generated tokens, and is thus unlikely to recover from generating such tokens at test time.",
"These motivations have led to much interest in RL for text generation in general and MT in particular (see §2).",
"Various policy gradient methods have been used, notably REINFORCE (Williams, 1992) and variants thereof (e.g., Ranzato et al., 2015; Edunov et al., 2018) and Minimum Risk Training (MRT; e.g., Och, 2003; .",
"Another popular use of RL is for training GANs (Yang et al., 2018; Tevet et al., 2018) .",
"Nevertheless, despite increasing interest and strong results, little is known about what accounts for these performance gains, and the training dynamics involved.",
"We present the following contributions.",
"First, our theoretical analysis shows that commonly used approximation methods are theoretically ill-founded, and may converge to parameter values that do not minimize the risk, nor are local minima thereof ( §2.2).",
"Second, using both naturalistic experiments and carefully constructed simulations, we show that performance gains observed in the literature likely stem not from making target tokens the most probable, but from unrelated effects, such as increasing the peakiness of the output distribution (i.e., the probability mass of the most probable tokens).",
"We do so by comparing a setting where the reward is informative, vs. one where it is constant.",
"In §4 we discuss this peakiness effect (PKE).",
"Third, we show that promoting the target token to be the mode is likely to take a prohibitively long time.",
"The only case we find, where improvements are likely, is where the target token is among the first 2-3 most probable tokens according to the pretrained model.",
"These findings suggest that REINFORCE ( §5) and CMRT ( §6) are likely to improve over the pre-trained model only under the best possible conditions, i.e., where the pre-trained model is \"nearly\" correct.",
"We conclude by discussing other RL practices in MT which should be avoided for practical and theoretical reasons, and briefly discuss alternative RL approaches that will allow RL to tackle a larger class of errors in pre-trained models ( §7).",
"Implementing a stochastic gradient ascent, REINFORCE is guaranteed to converge to a stationary point of R under broad conditions.",
"However, not much is known about its convergence rate under the prevailing conditions in NMT.",
"We begin with a qualitative, motivating analysis of these questions.",
"As work on language generation empirically showed, RNNs quickly learn to output very peaky distributions (Press et al., 2017) .",
"This tendency is advantageous for generating fluent sentences with high probability, but may also entail slower convergence rates when using RL to fine-tune the model, because RL methods used in text generation sample from the (pretrained) policy distribution, which means they mostly sample what the pretrained model deems to be likely.",
"Since the pretrained model (or policy) is peaky, exploration of other potentially more rewarding tokens will be limited, hampering convergence.",
"Intuitively, REINFORCE increases the probabilities of successful (positively rewarding) observations, weighing updates by how rewarding they were.",
"When sampling a handful of tokens in each context (source sentence x and generated prefix y <i ), and where the number of epochs is not large, it is unlikely that more than a few unique tokens will be sampled from P θ (·|x, y <i ).",
"(In practice, k is typically between 1 and 20, and the number of epochs between 1 and 100.)",
"It is thus unlikely that anything but the initially most probable candidates will be observed.",
"Consequently, REINFORCE initially raises their probabilities, even if more rewarding tokens can be found down the list.",
"We thus hypothesize the peakiness of the distribution, i.e., the probability mass allocated to the most probable tokens, will increase, at least in the first phase.",
"We call this the peakiness-effect (PKE), and show it occurs both in simulations ( §4.1) and in full-scale NMT experiments ( §4.2).",
"With more iterations, the most-rewarding tokens will be eventually sampled, and gradually gain probability mass.",
"This discussion suggests that training will be extremely sample-inefficient.",
"We assess the rate of convergence empirically in §5, finding this to be indeed the case.",
"A histogram of the update size (x-axis) to the total predicted probability of the 10 most probable tokens (left) or the most probable token (right) in the Constant Reward setting.",
"An update is overwhelmingly more probable to increase this probability than to decrease it.",
"In this paper, we showed that the type of distributions used in NMT entail that promoting the target token to be the mode is likely to take a prohibitively long times for existing RL practices, except under the best conditions (where the pretrained model is \"nearly\" correct).",
"This leads us to conclude that observed improvements from using RL for NMT are likely due either to fine-tuning the most probable tokens in the pretrained model (an effect which may be more easily achieved using reranking methods, and uses but little of the power of RL methods), or to effects unrelated to the signal carried by the reward, such as PKE.",
"Another contribution of this paper is in showing that CMRT does not optimize the expected reward and is thus theoretically unmotivated.",
"A number of reasons lead us to believe that in our NMT experiments, improvements are not due to the reward function, but to artefacts such as PKE.",
"First, reducing a constant baseline from r, so as to make the expected reward zero, disallows learning.",
"This is surprising, as REINFORCE, generally and in our simulations, converges faster where the reward is centered around zero, and so the fact that this procedure here disallows learning hints that other factors are in play.",
"As PKE can be observed even where the reward is constant (if the expected reward is positive; see §4.1), this suggests PKE may play a role here.",
"Second, we observe more peakiness in the reinforced model and in such cases, we expect improvements in BLEU (Caccia et al., 2018) .",
"Third, we achieve similar results with a constant reward in our NMT experiments ( §5.2).",
"Fourth, our controlled simulations show that asymptotic convergence is not reached in any but the easiest conditions ( §5.1).",
"Our analysis further suggests that gradient clipping, sometimes used in NMT (Zhang et al., 2016; Wieting et al., 2019) , is expected to hinder convergence further.",
"It should be avoided when using REINFORCE as it violates REINFORCE's assumptions.",
"The per-token sampling as done in our experiments is more exploratory than beam search (Wu et al., 2018) , reducing PKE.",
"Furthermore, the latter does not sample from the behavior policy, but does not properly account for being off-policy in the parameter updates.",
"Adding the reference to the sample S, which some implementations allow (Sennrich et al., 2017) may help reduce the problems of never sampling the target tokens.",
"However, as Edunov et al. (2018) point out, this practice may lower results, as it may destabilize training by leading the model to improve over outputs it cannot generalize over, as they are very different from anything the model assigns a high probability to, at the cost of other outputs.",
"The standard MT scenario poses several uncommon challenges for RL.",
"First, the action space in MT problems is a high-dimensional discrete space (generally in the size of the vocabulary of the target language or the product thereof for sentences).",
"This contrasts with the more common scenario studied by contemporary RL methods, which focuses mostly on much smaller discrete action spaces (e.g., video games (Mnih et al., 2015; 2016) ), or continuous action spaces of relatively low dimensions (e.g., simulation of robotic control tasks (Lillicrap et al., 2015) ).",
"Second, reward for MT is naturally very sparse -almost all possible sentences are \"wrong\" (hence, not rewarding) in a given context.",
"Finally, it is common in MT to use RL for tuning a pretrained model.",
"Using a pretrained model ameliorates the last problem.",
"But then, these pretrained models are in general quite peaky, and because training is done on-policy -that is, actions are being sampled from the same model being optimized -exploration is inherently limited.",
"Here we argued that, taken together, these challenges result in significant weaknesses for current RL practices for NMT, that may ultimately prevent them from being truly useful.",
"At least some of these challenges have been widely studied in the RL literature, with numerous techniques developed to address them, but were not yet adopted in NLP.",
"We turn to discuss some of them.",
"Off-policy methods, in which observations are sampled from a different policy than the one being currently optimized, are prominent in RL (Watkins & Dayan, 1992; Sutton & Barto, 1998) , and were also studied in the context of policy gradient methods (Degris et al., 2012; Silver et al., 2014) .",
"In principle, such methods allow learning from a more \"exploratory\" policy.",
"Moreover, a key motivation for using α in CMRT is smoothing; off-policy sampling allows smoothing while keeping convergence guarantees.",
"In its basic form, exploration in REINFORCE relies on stochasticity in the action-selection (in MT, this is due to sampling).",
"More sophisticated exploration methods have been extensively studied, for example using measures for the exploratory usefulness of states or actions (Fox et al., 2018) , or relying on parameter-space noise rather than action-space noise (Plappert et al., 2017) .",
"For MT, an additional challenge is that even effective exploration (sampling diverse sets of observations), may not be enough, since the state-action space is too large to be effectively covered, with almost all sentences being not rewarding.",
"Recently, diversity-based and multi-goal methods for RL were proposed to tackle similar challenges (Andrychowicz et al., 2017; Ghosh et al., 2018; Eysenbach et al., 2019) .",
"We believe the adoption of such methods is a promising path forward for the application of RL in NLP.",
"Let θ be a real number in [0, 0.5], and let P θ be a family of distributions over three values a, b, c such that:"
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] | H1eCw3EKvH | true | [
"Reinforcment practices for machine translation performance gains might not come from better predictions."
] |
[
"Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018.",
"The new approaches allow for accurate results, even when there is little labelled data, because these NLP models can benefit from training on both task-agnostic and task-specific unlabelled data.",
"However, these advantages come with significant size and computational costs.\n\n",
"This workshop paper outlines how our proposed convolutional student architecture, having been trained by a distillation process from a large-scale model, can achieve 300x inference speedup and 39x reduction in parameter count.",
"In some cases, the student model performance surpasses its teacher on the studied tasks.",
"The last year has seen several major advances in NLP modelling, stemming from previous innovations in embeddings BID0 [2] BID2 and attention models BID3 [5] BID5 that allow Language Models (LMs) to be trained on very large corpuses : For instance ELMo BID6 , OpenAI Transformer BID7 and recently BERT BID8 .In",
"addition, the power of building on LM-enhanced contextualised embeddings, using a fine-tuning approach on task-specific unlabelled data BID9 , has shown huge benefits for downstream tasks (such as text classification) -especially in a typical industrial setting where labelled data is scarce.In order to make use of these advances, this work shows how a model distillation process BID10 can be used to train a novel 'student' CNN structure from a much larger 'teacher' Language Model. The",
"teacher model can be fine-tuned on the specific task at hand, using both unlabelled data, and the (small number of) labelled training examples available. The",
"student network can then be trained using both labelled and unlabelled data, in a process akin to pseudo-labelling BID11 [13].Our",
"results show it is possible to achieve similar performance to (and surpass in some cases) large attention-based models with a novel, highly efficient student model with only convolutional layers.",
"For text classifications, mastery may require both high-level concepts gleaned from language under standing and fine-grained textual features such as key phrases.",
"Similar to the larval-adult form analogy made in BID10 , high-capacity models with task-agnostic pre-training may be well-suited for task mastery on small datasets (which are common in industry).",
"On the other hand, convolutional student architectures may be more ideal for practical applications by taking advantage of massively parallel computation and a significantly reduced memory footprint.Our results suggest that the proposed BlendCNN architecture can efficiently achieve higher scores on text classification tasks due to the direct leveraging of hierarchical representations, which are learnable (even in a label-sparse setting) from a strong teaching model.Further development of specialized student architectures could similarly surpass teacher performance if appropriately designed to leverage the knowledge gained from a pretrained, task-agnostic teacher model whilst optimizing for task-specific constraints."
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] | HJxM3hftiX | true | [
"We train a small, efficient CNN with the same performance as the OpenAI Transformer on text classification tasks"
] |
[
"Combining multiple function approximators in machine learning models typically leads to better performance and robustness compared with a single function.",
"In reinforcement learning, ensemble algorithms such as an averaging method and a majority voting method are not always optimal, because each function can learn fundamentally different optimal trajectories from exploration.",
"In this paper, we propose a Temporal Difference Weighted (TDW) algorithm, an ensemble method that adjusts weights of each contribution based on accumulated temporal difference errors.",
"The advantage of this algorithm is that it improves ensemble performance by reducing weights of Q-functions unfamiliar with current trajectories.",
"We provide experimental results for Gridworld tasks and Atari tasks that show significant performance improvements compared with baseline algorithms.",
"Using ensemble methods that combine multiple function approximators can often achieve better performance than a single function by reducing the variance of estimation (Dietterich (2000) ; Kuncheva (2014) ).",
"Ensemble methods are effective in supervised learning, and also reinforcement learning (Wiering & Van Hasselt (2008) ).",
"There are two situations where multiple function approximators are combined: combining and learning multiple functions during training (Freund & Schapire (1997) ) and combining individually trained functions to jointly decide actions during testing (Breiman (1996) ).",
"In this paper, we focus on the second setting of reinforcement learning wherein each function is trained individually and then combined them to achieve better test performance.",
"Though there is a body of research on ensemble algorithms in reinforcement learning, it is not as sizeable as the research devoted to ensemble methods for supervised learning.",
"Wiering & Van Hasselt (2008) investigated many ensemble approaches combining several agents with different valuebased algorithms in Gridworld settings.",
"Faußer & Schwenker (2011; 2015a) have shown that combining value functions approximated by neural networks improves performance greater than using a single agent.",
"Although previous work dealt with each agent equally contributing to the final output, weighting each contribution based on its accuracy is also a known and accepted approach in supervised learning (Dietterich (2000) ).",
"However, unlike supervised learning, reinforcement learning agents learn from trajectories resulting from exploration, such that each agent learns from slightly different data.",
"This characteristic is significant in tasks with high-dimensional state-space, where there are several possible optimal trajectories to maximize cumulative rewards.",
"In such a situation, the final joint policy function resulting from simple averaging or majority voting is not always optimal if each agent learned different optimal trajectories.",
"Furthermore, it is difficult to decide constant weights of each contribution as it is possible that agents with poor episode rewards have better performance in specific areas.",
"In this paper, we propose the temporal difference weighted (TDW) algorithm, an ensemble method for reinforcement learning at test time.",
"The most important point of this algorithm is that confident agents are prioritized to participate in action selection while contributions of agents unfamiliar with the current trajectory are reduced.",
"To do so in the TDW algorithm, the weights of the contributions at each Q-function are calculated as softmax probabilities based on accumulated TD errors.",
"Extending an averaging method and a majority voting method, actions are determined by weighted average or voting methods according to the weights.",
"The advantage of the TDW algorithm is that arbitrary training algorithms can use this algorithm without any modifications, because the TDW algorithm only cares about the joint decision problem, which could be easily adopted in competitions and development works using reinforcement learning.",
"In our experiment, we demonstrate that the TDW retains performance in tabular representation Gridworld tasks with multiple possible trajectories, where simple ensemble methods are significantly degraded.",
"Second, to demonstrate the effectiveness of our TDW algorithm in high-dimensional state-space, we also show that our TDW algorithm can achieve better performance than baseline algorithms in Atari tasks (Bellemare et al. (2013) ).",
"In this paper, we have introduced the TDW algorithm: an ensemble method that accumulates temporal difference errors as an uncertainties in order to adjust weights of each Q-function, improving performance especially in high-dimensional state-space or situations where there are multiple optimal trajectories.",
"We have shown performance evaluations in Gridworld tasks and Atari tasks, wherein the TDW algorithms have achieved significantly better performance than non-weighted algorithms and globally weighted algorithms.",
"However, it is difficult to correctly measure uncertainties with frequent reward occurrences because the intrinsic prediction errors are also accumulated.",
"Thus, these types of games did not realize the same performance improvements.",
"In future work, we intend to investigate an extension of this work into continuous action-space tasks because only the joint decision problem of Q-functions is considered in this paper.",
"We believe a similar algorithm can extend a conventional ensemble method (Huang et al. (2017) ) of Deep Deterministic Policy Gradients (Lillicrap et al. (2015) ) by measuring uncertainties of pairs of a policy function and a Q-function.",
"We will also consider a separate path, developing an algorithm that measures uncertainties without rewards because reward information is not always available especially in the case of real world application.",
"A Q-FUNCTION TABLES OBTAINED ON GRIDWORLDS table",
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[
"This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short.",
"bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives.",
"First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms.",
"Second, to study agent behaviour through their performance on these shared benchmarks.",
"To complement this effort, we open source this http URL, which automates evaluation and analysis of any agent on bsuite.",
"This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms.",
"Our code is Python, and easy to use within existing projects.",
"We include examples with OpenAI Baselines, Dopamine as well as new reference implementations.",
"Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.",
"The reinforcement learning (RL) problem describes an agent interacting with an environment with the goal of maximizing cumulative reward through time (Sutton & Barto, 2017) .",
"Unlike other branches of control, the dynamics of the environment are not fully known to the agent, but can be learned through experience.",
"Unlike other branches of statistics and machine learning, an RL agent must consider the effects of its actions upon future experience.",
"An efficient RL agent must address three challenges simultaneously:",
"1. Generalization: be able to learn efficiently from data it collects.",
"2. Exploration: prioritize the right experience to learn from.",
"3. Long-term consequences: consider effects beyond a single timestep.",
"The great promise of reinforcement learning are agents that can learn to solve a wide range of important problems.",
"According to some definitions, an agent that can perform at or above human level across a wide variety of tasks is an artificial general intelligence (AGI) (Minsky, 1961; Legg et al., 2007) .",
"Interest in artificial intelligence has undergone a resurgence in recent years.",
"Part of this interest is driven by the constant stream of innovation and success on high profile challenges previously deemed impossible for computer systems.",
"Improvements in image recognition are a clear example of these accomplishments, progressing from individual digit recognition (LeCun et al., 1998) , to mastering ImageNet in only a few years (Deng et al., 2009; Krizhevsky et al., 2012) .",
"The advances in RL systems have been similarly impressive: from checkers (Samuel) , to Backgammon (Tesauro, 1995) , to Atari games (Mnih et al., 2015a) , to competing with professional players at DOTA (Pachocki et al., 2019) or StarCraft (Vinyals et al., 2019) and beating the world champions at Go .",
"Outside of playing games, decision systems are increasingly guided by AI systems (Evans & Gao, 2016 ).",
"As we look towards the next great challenges for RL and AI, we need to understand our systems better (Henderson et al., 2017) .",
"This includes the scalability of our RL algorithms, the environments where we expect them to perform well, and the key issues outstanding in the design of a general intelligence system.",
"We have the existence proof that a single self-learning RL agent can master the game of Go purely from self-play (Silver et al., 2018 ).",
"We do not have a clear picture of whether such a learning algorithm will perform well at driving a car, or managing a power plant.",
"If we want to take the next leaps forward, we need to continue to enhance our understanding."
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"Bsuite is a collection of carefully-designed experiments that investigate the core capabilities of RL agents."
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"Sequence-to-sequence attention-based models are a promising approach for end-to-end speech recognition.",
"The increased model power makes the training procedure more difficult, and analyzing failure modes of these models becomes harder because of the end-to-end nature.",
"In this work, we present various analyses to better understand training and model properties.",
"We investigate on pretraining variants such as growing in depth and width, and their impact on the final performance, which leads to over 8% relative improvement in word error rate.",
"For a better understanding of how the attention process works, we study the encoder output and the attention energies and weights.",
"Our experiments were performed on Switchboard, LibriSpeech and Wall Street Journal.",
"The encoder-decoder framework with attention BID34 BID60 has been successfully applied to automatic speech recognition (ASR) BID26 BID61 BID58 BID47 and is a promising end-to-end approach.",
"The model outputs are words, sub-words or characters, and training the model can be done from scratch without any prerequisites except the training data in terms of audio features with corresponding transcriptions.In contrast to the conventional hybrid hidden Markov models (HMM) / neural network (NN) approach BID8 Morgan, 1994, Robinson, 1994] , the encoder-decoder model does not model the alignment explicitly.",
"In the hybrid HMM/NN approach, a latent variable of hidden states is introduced, which model the phone state for any given time position.",
"Thus by searching for the most probable sequence of hidden states, we get an explicit alignment.",
"There is no such hidden latent variable in the encoder decoder model.",
"Instead there is the attention process which can be interpreted as an implicit soft alignment.",
"As this is only implicit and soft, it is harder to enforce constraints such as monotonicity, i.e. that the attention of future label outputs will focus also only to future time frames.",
"Also, the interpretation of the attention weights as a soft alignment might not be completely valid, as the encoder itself can shift around and reorder evidence, i.e. the neural network could learn to pass over information in any possible way.",
"E.g. the encoder could compress all the information of the input into a single frame and the decoder can learn to just attend on this single frame.",
"We observed this behavior in early stages of the training.",
"Thus, studying the temporal \"alignment\" behavior of the attention model becomes more difficult.Other end-to-end models such as connectionist temporal classification BID21 has often been applied to ASR in the past BID20 BID23 BID35 BID1 BID51 BID2 BID26 BID63 BID67 .",
"Other approaches are e.g. the inverted hidden Markov / segmental encoder-decoder model BID5 , the recurrent transducer BID4 BID41 , or the recurrent neural aligner .",
"Depending on the interpretation, these can all be seen as variants of the encoder decoder approach.",
"In some of these models, the attention process is not soft, but a hard decision.",
"This hard decision can also become a latent variable such that we include several choices in the beam search.",
"This is also referred to as hard attention.",
"Examples of directly applying this idea on the usual attention approach are given by BID43 , BID0 , , BID33 , BID27 .We",
"study recurrent NN (RNN) encoder decoder models in this work, which use long short-term memory (LSTM) units BID24 . Recently",
"the transformer model BID57 gained attention, which only uses feed-forward and self-attention layers, and the only recurrence is the label feedback in the decoder. As this",
"does not include any temporal information, some positional encoding is added. This is",
"not necessary for a RNN model, as it can learn such encoding by itself, which we demonstrate later for our attention encoder.We study attention models in more detail here. We are",
"interested in when, why and how they fail and do an analysis on the search errors and relative error positions. We study",
"the implicit alignment behavior via the attention weights and energies. We also",
"analyze the encoder output representation and find that it contains information about the relative position and that it specially marks frames which should not be attended to, which correspond to silence.2 Related",
"work BID25 analyzes individual neuron activations of a RNN language model and finds a neuron which becomes sensitive to the position in line. BID7 analyzed",
"the hidden activations of the DeepSpeech 2 BID1 ] CTC end-to-end system and shows their correlation to a phoneme frame alignment. BID36 analyzed",
"the encoder state and the attention weights of an attention model and makes similar observations as we do. Attention plots",
"were used before to understand the behaviour of the model BID15 . BID6 performed",
"a comparison of the alignment behavior between hybrid HMM/NN models, the inverted HMM and attention models. BID42 investigate",
"the effects of varying block sizes, attention types, and sub-word units. Understanding the",
"inner working of a speech recognition system is also subject in , where the authors examine activation distribution and temporal patterns, focussing on the comparison between LSTM and GRU systems.A number of saliency methods BID50 BID32 BID52 are used for interpreting model decisions.",
"We provided an overview of our recent attention models results on Switchboard, LibriSpeech and WSJ.",
"We performed an analysis on the beam search errors.",
"By our improved pretraining scheme, we improved our Switchboard baseline by over 8% relative in WER.",
"We pointed out the high training variance of attention models compared to hybrid HMM/NN models.",
"We analyzed the encoder output and identified the representation of the relative input position, both clearly visible in the PCA reduction of the encoder but even represented by individual neurons.",
"Also we found indications that the encoder marks frames which can be skipped by decoder, which correlate to silence."
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"improved pretraining, and analysing encoder output and attention"
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[
"Validation is a key challenge in the search for safe autonomy.",
"Simulations are often either too simple to provide robust validation, or too complex to tractably compute.",
"Therefore, approximate validation methods are needed to tractably find failures without unsafe simplifications.",
"This paper presents the theory behind one such black-box approach: adaptive stress testing (AST).",
"We also provide three examples of validation problems formulated to work with AST.",
"An open question when robots operate autonomously in uncertain, real-world environments is how to tractably validate that the agent will act safely.",
"Autonomous robotic systems may be expected to interact with a number of other actors, including humans, while handling uncertainty in perception, prediction and control.",
"Consequently, scenarios are often too high-dimensional to tractably simulate in an exhaustive manner.",
"As such, a common approach is to simplify the scenario by constraining the number of non-agent actors and the range of actions they can take.",
"However, simulating simplified scenarios may compromise safety by eliminating the complexity needed to find rare, but important failures.",
"Instead, approximate validation methods are needed to elicit agent failures while maintaining the full complexity of the simulation.One possible approach to approximate validation is adaptive stress testing (AST) BID6 .",
"In AST, the validation problem is cast as a Markov decision process (MDP).",
"A specific reward function structure is then used with reinforcement learning algorithms in order to identify the most-likely failure of a system in a scenario.",
"Knowing the most-likely failure is useful for two reasons:",
"1) all other failures are at most as-likely, so it provides a bound on the likelihood of failures, and",
"2) it uncovers possible failure modes of an autonomous system so they can be addressed.",
"AST is not a silver bullet: it requires accurate models of all actors in the scenario and is susceptible to local convergence.",
"However, it allows failures to be identified tractably in simulation for complicated autonomous systems acting in high-dimensional spaces.",
"This paper briefly presents the latest methodology for using AST and includes example validation scenarios formulated as AST problems.",
"This paper presents the latest formulation of adaptive stress testing, and examples of how it can be applied.",
"AST is an approach to validation that can tractably find failures in autonomous systems in simulation without reducing scenario complexity.",
"Autonomous systems are difficult to validate because they interact with many other actors in high-dimensional spaces according to complicated policies.",
"However, validation is essential for producing autonomous systems that are safe, robust, and reliable."
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"A formulation for a black-box, reinforcement learning method to find the most-likely failure of a system acting in complex scenarios."
] |
[
"Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go).",
"In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration.",
"These algorithms iteratively solve short-horizon decision problems and converge to the optimal solution of the original one.",
"By using model-free algorithms as solvers of the short-horizon problems we derive fully model-free algorithms which are instances of the multi-step DP framework.",
"As model-free algorithms are prone to instabilities w.r.t. the decision problem horizon, this simple approach can help in mitigating these instabilities and results in an improved model-free algorithms.",
"We test this approach and show results on both discrete and continuous control problems.",
"The field of Reinforcement learning (RL) span a wide variety of algorithms for solving decisionmaking problems through repeated interaction with the environment.",
"By incorporating deep neural networks into RL algorithms, the field of RL has recently witnessed remarkable empirical success (e.g., Mnih et al. 2015; Lillicrap et al. 2015; Silver et al. 2017 ).",
"Much of this success had been achieved by model-free RL algorithms, such as Q-learning and policy gradient.",
"These algorithms are known to suffer from high variance in their estimations (Greensmith et al., 2004) and to have difficulties handling function approximation (e.g., Thrun & Schwartz 1993; Baird 1995; Van Hasselt et al. 2016; Lu et al. 2018 ).",
"These problems are intensified in decision problems with long horizon, i.e., when the discount factor, γ, is large.",
"Although using smaller values of γ addresses the γ-dependent issues and leads to more stable algorithms (Petrik & Scherrer, 2009; Jiang et al., 2015) , it comes with a cost, as the algorithm may return a biased solution, i.e., it may not converge to an optimal solution of the original decision problem (the one with large value of γ).",
"Efroni et al. (2018a) recently proposed another approach to mitigate the γ-dependant instabilities in RL in which they study a multi-step greedy versions of the well-known dynamic programming (DP) algorithms policy iteration (PI) and value iteration (VI) (Bertsekas & Tsitsiklis, 1996) .",
"Efroni et al. (2018a) also proposed an alternative formulation of the multi-step greedy policy, called κ-greedy policy, and studied the convergence of the resulted PI and VI algorithms: κ-PI and κ-VI.",
"These two algorithms iteratively solve γκ-discounted decision problems, whose reward has been shaped by the solution of the decision problem at the previous iteration.",
"Unlike the biased solution obtained by solving the decision problem with a smaller value of γ, by iteratively solving decision problems with a smaller γκ horizon, the κ-PI and κ-VI algorithms could converge to an optimal policy of the original decision problem.",
"In this work, we derive and empirically validate model-free deep RL (DRL) implementations of κ-PI and κ-VI.",
"In these implementations, we use DQN (Mnih et al., 2015) and TRPO (Schulman et al., 2015) for (approximately) solving γκ-discounted decision problems (with shaped reward), which is the main component of the κ-PI and κ-VI algorithms.",
"The experiments illustrate the performance of model-free algorithms can be improved by using them as solvers of multi-step greedy PI and VI schemes, as well as emphasize important implementation details while doing so.",
"In this work we formulated and empirically tested simple generalizations of DQN and TRPO derived by the theory of multi-step DP and, specifically, of κ-PI and κ-VI algorithms.",
"The empirical investigation reveals several points worth emphasizing.",
"1. κ-PI is better than κ-VI for the Atari domains..",
"In most of the experiments on the Atari domains κ-PI-DQN has better performance than κ-VI-DQN. This might be expected as the former uses extra information not used by the latter: κ-PI estimates the value of current policy whereas κ-VI ignores this information. 2",
". For the Gym domains κ-VI performs slightly better than κ-PI.",
"For the Gym domains κ-VI-TRPO performs slightly better than κ-PI-TRPO.",
"We conjecture that the reason for the discrepancy relatively to the Atari domains lies in the inherent structure of the tasks of the Gym domains: they are inherently short horizon decision problems.",
"For this reason, the problems can be solved with smaller discount factor (as empirically demonstrated in Section 5.3) and information on the policy's value is not needed.",
"3. Non trivial κ value improves the performance.",
"In the vast majority of our experiments both κ-PI and κ-VI improves over the performance of their vanilla counterparts (i.e., κ = 1), except for the Swimmer and BeamRider domains from Mujoco and Atari suites.",
"Importantly, the performance of the algorithms was shown to be 'smooth' in the parameter κ.",
"This suggests careful hyperparameter tuning of κ is not of great necessity.",
"4. Using the 'naive' choice of N (κ) = T deteriorates the performance.",
"Choosing the number of iteration by Eq. 8 improves the performance on the tested domains.",
"An interesting future work would be to test model-free algorithms which use other variants of greedy policies (Bertsekas & Tsitsiklis, 1996; Bertsekas, 2018; Efroni et al., 2018a; Sun et al., 2018; Shani et al., 2019) .",
"Furthermore, and although in this work we focused on model-free DRL, it is arguably more natural to use multi-step DP in model-based DRL (e.g., Kumar et al., 2016; Talvitie, 2017; Luo et al., 2018; Janner et al., 2019) .",
"Taking this approach, the multi-step greedy policy would be solved with an approximate model.",
"We conjecture that in this case one may set κ -or more generally, the planning horizon -as a function of the approximate model's 'quality': as the approximate model gets closer to the real model larger κ can be used.",
"We leave investigating such relation in theory and practice to future work.",
"Lastly, an important next step in continuation to our work is to study algorithms with an adaptive κ parameter.",
"This, we believe, would greatly improve the resulting methods, and possibly be done by studying the relation between the different approximation errors (i.e., errors in gradient and value estimation, Ilyas et al., 2018) , the performance and the κ value that should be used by the algorithm.",
"A DQN IMPLEMENTATION OF κ-PI AND κ-VI"
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"Use model free algorithms like DQN/TRPO to solve short horizon problems (model free) iteratively in a Policy/Value Iteration fashion."
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[
"The adversarial training procedure proposed by Madry et al. (2018) is one of the most effective methods to defend against adversarial examples in deep neural net- works (DNNs).",
"In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on test set) of adversarial training has a strong correlation with the distance between a test point and the manifold of training data embedded by the network.",
"Test examples that are relatively far away from this manifold are more likely to be vulnerable to adversarial attacks.",
"Consequentially, an adversarial training based defense is susceptible to a new class of attacks, the “blind-spot attack”, where the input images reside in “blind-spots” (low density regions) of the empirical distri- bution of training data but is still on the ground-truth data manifold.",
"For MNIST, we found that these blind-spots can be easily found by simply scaling and shifting image pixel values.",
"Most importantly, for large datasets with high dimensional and complex data manifold (CIFAR, ImageNet, etc), the existence of blind-spots in adversarial training makes defending on any valid test examples difficult due to the curse of dimensionality and the scarcity of training data.",
"Additionally, we find that blind-spots also exist on provable defenses including (Kolter & Wong, 2018) and (Sinha et al., 2018) because these trainable robustness certificates can only be practically optimized on a limited set of training data.",
"Since the discovery of adversarial examples in deep neural networks (DNNs) BID28 , adversarial training under the robustness optimization framework BID24 has become one of the most effective methods to defend against adversarial examples.",
"A recent study by BID1 showed that adversarial training does not rely on obfuscated gradients and delivers promising results for defending adversarial examples on small datasets.",
"Adversarial training approximately solves the following min-max optimization problem: where X is the set of training data, L is the loss function, θ is the parameter of the network, and S is usually a norm constrained p ball centered at 0.",
"propose to use projected gradient descent (PGD) to approximately solve the maximization problem within S = {δ | δ ∞ ≤ }, where = 0.3 for MNIST dataset on a 0-1 pixel scale, and = 8 for CIFAR-10 dataset on a 0-255 pixel scale.",
"This approach achieves impressive defending results on the MNIST test set: so far the best available white-box attacks by BID37 can only decrease the test accuracy from approximately 98% to 88% 1 .",
"However, on CIFAR-10 dataset, a simple 20-step PGD can decrease the test accuracy from 87% to less than 50% 2 .The",
"effectiveness of adversarial training is measured by the robustness on the test set. However",
", the adversarial training process itself is done on the training set. Suppose",
"we can optimize (1) perfectly, then certified robustness may be obtained on those training data points. However",
", if the empirical distribution of training dataset differs from the true data distribution, a test point drawn from the true data distribution might lie in a low probability region in the empirical distribution of training dataset and is not \"covered\" by the adversarial training procedure. For datasets",
"that are relatively simple and have low intrinsic dimensions (MNIST, Fashion MNIST, etc), we can obtain enough training examples to make sure adversarial training covers most part of the data distribution. For high dimensional",
"datasets (CIFAR, ImageNet), adversarial training have been shown difficult (Kurakin et al., 2016; BID29 and only limited success was obtained.A recent attack proposed by shows that adversarial training can be defeated when the input image is produced by a generative model (for example, a generative adversarial network) rather than selected directly from the test examples. The generated images",
"are well recognized by humans and thus valid images in the ground-truth data distribution. In our interpretation",
", this attack effective finds the \"blind-spots\" in the input space that the training data do not well cover.For higher dimensional datasets, we hypothesize that many test images already fall into these blindspots of training data and thus adversarial training only obtains a moderate level of robustness. It is interesting to",
"see that for those test images that adversarial training fails to defend, if their distances (in some metrics) to the training dataset are indeed larger. In our paper, we try",
"to explain the success of robust optimization based adversarial training and show the limitations of this approach when the test points are slightly off the empirical distribution of training data. Our main contributions",
"are:• We show that on the original set of test images, the effectiveness of adversarial training is highly correlated with the distance (in some distance metrics) from the test image to the manifold of training images. For MNIST and Fashion",
"MNIST datasets, most test images are close to the training data and very good robustness is observed on these points. For CIFAR, there is a",
"clear trend that the adversarially trained network gradually loses its robustness property when the test images are further away from training data.• We identify a new class",
"of attacks, \"blind-spot attacks\", where the input image resides in a \"blind-spot\" of the empirical distribution of training data (far enough from any training examples in some embedding space) but is still in the ground-truth data distribution (well recognized by humans and correctly classified by the model). Adversarial training cannot",
"provide good robustness on these blind-spots and their adversarial examples have small distortions.• We show that blind-spots can",
"be easily found on a few strong defense models including , BID32 and BID24 . We propose a few simple transformations",
"(slightly changing contrast and background), that do not noticeably affect the accuracy of adversarially trained MNIST and Fashion MNIST models, but these models become vulnerable to adversarial attacks on these sets of transformed input images. These transformations effectively move",
"the test images slightly out of the manifold of training images, which does not affect generalization but poses a challenge for robust learning.Our results imply that current adversarial training procedures cannot scale to datasets with a large (intrinsic) dimension, where any practical amount of training data cannot cover all the blind-spots. This explains the limited success for",
"applying adversarial training on ImageNet dataset, where many test images can be sufficiently far away from the empirical distribution of training dataset.",
"In this paper, we observe that the effectiveness of adversarial training is highly correlated with the characteristics of the dataset, and data points that are far enough from the distribution of training data are prone to adversarial attacks despite adversarial training.",
"Following this observation, we defined a new class of attacks called \"blind-spot attack\" and proposed a simple scale-and-shift scheme for conducting blind-spot attacks on adversarially trained MNIST and Fashion MNIST datasets with high success rates.",
"Our findings suggest that adversarial training can be challenging due to the prevalence of blind-spots in high dimensional datasets."
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"We show that even the strongest adversarial training methods cannot defend against adversarial examples crafted on slightly scaled and shifted test images."
] |
[
"Edge intelligence especially binary neural network (BNN) has attracted considerable attention of the artificial intelligence community recently.",
"BNNs significantly reduce the computational cost, model size, and memory footprint. ",
"However, there is still a performance gap between the successful full-precision neural network with ReLU activation and BNNs.",
"We argue that the accuracy drop of BNNs is due to their geometry. \n",
"We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart.",
"This comparison suggests random bias initialization as a remedy to activation saturation in full-precision networks and leads us towards an improved BNN training.",
"Our numerical experiments confirm our geometric intuition.",
"Convolutional neural network has become one of the most powerful tools for solving computer vision, natural language processing, speech recognition, machine translation, and many other complex tasks.",
"The most successful and widely-used recipe for deep neural network is ReLU-style activation function with MSRA style weight initialization (He et al., 2015) .",
"The standard sigmoid and the hyperbolic tangent were the most common activation functions, before the introductio of ReLU.",
"ReLU-like activation functions are widely proved to be superior in terms of accuracy and convergence speed.",
"It is more common to use low-bit quantized networks such as Binary Neural Networks (BNNs) (Courbariaux et al., 2016) to implement such deep neural networks on edge devices such as cell phones, smart wearables, etc.",
"BNNs only keeps the sign of weights and compute the sign of activations {−1, +1} by applying Sign function in the forward pass.",
"In backward propagation, BNN uses Straight-Through-Estimator (STE) to estimate the backward gradient through the Sign function and update on full-precision weights.",
"The forward and backward loop of a BNN, therefore, becomes similar to the full-precision neural network with hard hyperbolic tangent htanh activation.",
"The htanh function is a piece-wise linear version of the nonlinear hyper-bolic tangent, and is known to be inferior in terms of accuracy compared to ReLU-like activation function.",
"We examine a full-precision network with htanh activation to provide a new look in improving BNN performance.",
"We conclude that the bias initialization is the key to mimic ReLU geometric behavior in networks with htanh activation.",
"This conclusion challenges the common practice of deterministic bias initialization for neural networks.",
"Although the analysis is based on htanh function, this conclusion equally applies to BNNs that use STE, a htanh-like, back propagation scheme.",
"Other saturating activations like hyperbolic tangent and sigmoid commonly applied in recurrent neural networks may benefit from this resolution as well.",
"Our novelties can be summarized in four items",
"i) we analyze the geometric properties of ReLU and htanh activation.",
"This provides an insight into the training efficiency of the unbounded asymmetric activation functions such as ReLU.",
"ii) we propose bias initialization strategy as a remedy to the bounded activations such as htanh.",
"iii) We back up our findings with experiments on full-precision to reduce the performance gap between htanh and ReLU activations.",
"iv) We show this strategy also improves BNNs, whose geometric behavior is similar to the full-precision neural network with htanh activation.",
"There are very few works that focus on the initialization strategy of the bias term of the neural network.",
"To the best of our knowledge, we are the first to propose random bias initialization as a remedy to the saturating full-precision neural network, also as a method to improve BNN training.",
"2 RELATED WORKS (Glorot et al., 2011) proposed training deep neurals network with ReLU activation, and argued that ReLU activation alleviates the vanishing gradient problem and encourages sparsity in the model.",
"The hyperbolic tangent only allowed training of shallow neural networks.",
"Since AlexNet (Krizhevsky et al., 2012) , almost every successful neural network architectures use ReLU activation or its variants, such as adaptive ReLU, leaky ReLU, etc.",
"Although many works reported that ReLU activation outperforms the traditional saturating activation functions, the reason for its superior performance remains an open question.",
"(Ramachandran et al., 2017) utilized automatic search techniques on searching different activation functions.",
"Most top novel activation functions found by the searches have an asymmetric saturating regime, which is similar to ReLU.",
"Farhadi et al. (2019) adapts ReLU and sigmoid while training.",
"To improve the performance of saturating activations, Xu et al. (2016) proposed penalized tanh activation, which introduces asymmetric saturating regime to tanh by inserting leaky ReLU before tanh.",
"The penalized tanh could achieve the same level of performance as ReLU activating CNN.",
"It is worth to mention that similar ideas also appear in the related works of binarized neural network.",
"Gulcehre et al. (2016) improved the performance of saturating activations by adding random noise when the neuron is saturated, so the backward signal can easily pass through the whole model, and the model becomes easier to optimize.",
"In this works, we proposed to randomize the non-saturated regime by using random bias initialization.",
"This initialization can guarantee all backward signals can pass through the whole model equally.",
"The initial work on BNN appeared in Courbariaux et al. (2016) , which limits both weights and activations to −1 and +1, so the weighted sum can be computed by bit-wise XNOR and PopCount instructions.",
"This solution reduces memory usage and computational cost up to 32X compared with its full-precision counterpart.",
"In the original paper, BNN was tested on VGG-7 architecture.",
"Although it is an over-parameterized architecture for CIFAR 10 dataset, there is a performance gap between BNN and full-precision with ReLU activation.",
"We believe the different between the two activations, BNN using the sign and full-precision using ReLU, is partially responsible for this gap.",
"XNOR-Net (Rastegari et al., 2016) developed the idea of BNN and proposed to approximate the full-precision neural network by using scaling factors.",
"They suggest inserting non-Binary activation (like ReLU) after the binary convolution layer.",
"This modification helps training considerably.",
"Later, Tang et al. replaced replacing ReLU activation with PReLU in XNOR-Net to improve the accuracy.",
"Note that XNOR-Net and many relaated works require to store the full-precision activation map during the inference stage, therefore their memory occupation is significantly larger than the pure 1-bit solution like the vanilla BNN."
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] | SJx4Ogrtvr | true | [
"Improve saturating activations (sigmoid, tanh, htanh etc.) and Binarized Neural Network with Bias Initialization"
] |
[
"Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire.",
"Decades of psychological research have yielded a variety of formal theories of categories, yet validating these theories with naturalistic stimuli remains a challenge.",
"The problem is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires having a workable representation of these stimuli.",
"Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images.",
"In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners.",
"We provide qualitative and quantitative results as a proof of concept for the feasibility of the method.",
"Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories.",
"Categorization (or classification) is a central problem in cognitive science BID1 , artificial intelligence, and machine learning BID3 .",
"In its most general form, the categorization problem concerns why and how we divide the world into discrete units (and various levels of abstraction), and what we do with this information.",
"The biggest challenge for studying human categorization is that the content of mental category representations cannot be directly observed, which has led to development of laboratory methods for estimating this content from human behavior.",
"Because these methods rely on small artificial stimulus sets with handcrafted or lowdimensional feature sets, they are ill-suited to the study of categorization as an intelligent process, which is principally motivated by people's robust categorization performance in complex ecological settings.One of the challenges of applying psychological methods to realistic stimuli such as natural images is finding a way to represent them.",
"Recent work in machine learning has shown that deep learning models, such as convolutional neural networks, perform well on a range of computer vision tasks BID10 .",
"The features discovered by these models provide a way to represent complex images compactly.",
"It may be possible to express human category structure using these features, an idea supported by recent work in cognitive science BID8 BID12 .Ideally",
", experimental methods could be combined with state-of-the-art deep learning models to estimate the structure of human categories with as few assumptions as possible and while avoiding the problem of dataset bias. In what",
"follows, we propose a method that uses a human in the loop to directly estimate arbitrary distributions over complex feature spaces, adapting a framework that can exploit advances in deep architectures and computing power to increasingly capture and sharpen the precise structure of human category representations. Such knowledge",
"is crucial to forming an ecological theory of intelligent categorization behavior and to providing a ground-truth benchmark to guide and inspire future work in machine learning.",
"Our results demonstrate the potential of our method, which leverages both psychological methods and deep surrogate representations to make the problem of capturing human category representations tractable.",
"The flexibility of our method in fitting arbitrary generative models allows us to visualize multi-modal category templates for the first time, and improve on human-based classification performance benchmarks.",
"It is difficult to guarantee that our chains explored enough of the relevant space to actually capture the concepts in their entirety, but the diversity in the modes visualized and the improvement in class separation achieved are positive indications that we are on the right track.",
"Further, the framework we present can be straightforwardly improved as generative image models advance, and a number of known methods for improving the speed, reach, and accuracy of MCMC algorithms can be applied to MCMCP make better use of costly human trials.There are several obvious limitations of our method.",
"First, the structure of the underlying feature spaces used may either lack the expressiveness (some features may be missing) or the constraints (too many irrelevant features or possible images wastes too many trials) needed to map all characteristics of human mental categories in a practical number of trials.",
"Even well-behaved spaces are very large and require many trials to reach convergence.",
"Addressing this will require continuing exploration of a variety of generative image models.",
"We see our work are as part of an iterative refinement process that can yield more granular human observations and inform new deep network"
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"using deep neural networks and clever algorithms to capture human mental visual concepts"
] |
[
"The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems.",
"Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence.",
"Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known.",
"In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task.",
"We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity."
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"We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription."
] |
[
"Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning.",
"However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations.",
"To bridge this gap, we focus on one class of interactive tasks---sitting onto a chair.",
"We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task.",
"We experimentally demonstrate the strength of our approach over different single level and hierarchical baselines.",
"We also show that our approach can be applied to motion prediction given an image input.",
"A video highlight can be found at https://youtu.be/XWU3wzz1ip8/.\n",
"The capability of synthesizing realistic human-scene interactions is an important basis for simulating human living space, where robots can be trained to collaborate with humans, e.g. avoiding collisions or expediting the completion of assistive tasks.",
"Motion capture (mocap) data, by offering high quality recordings of articulated human pose, has provided a crucial resource for human motion synthesis.",
"With large mocap datasets and deep learning algorithms, kinematics-based approaches have recently made rapid progress on motion synthesis and prediction (Fragkiadaki et al., 2015; Jain et al., 2016; Holden et al., 2016; Ghosh et al., 2017; Bütepage et al., 2017; Martinez et al., 2017; Holden et al., 2017; Zhou et al., 2018; Gui et al., 2018a; b; Yan et al., 2018) .",
"However, the lack of physical interpretability in their synthesized motion has been a major limitation of these approaches.",
"The problem becomes especially clear when it comes to motions that involve substantial human-object or human-human interactions.",
"Without modeling the physics, the sythensized interactions are often physically unrealistic, e.g. body parts penetrating obstacles or not reacting to collision.",
"This generally limits the use of these approaches to either non-interactive motions, or a carefully set up virtual scene with high fidelity to the captured one.",
"The graphics community has recently witnessed impressive progress on physics-based character animation (Peng et al., 2017; b) .",
"These approaches, through imitating mocap examples via deep reinforcement learning, can synthesize realistic motions in physics simulated environments.",
"Consequently, they can adapt to different physical contexts and thus attain a better generalization performance for interaction-based motions, e.g. walking on uneven terrain or stunt performance under obstacle disturbance.",
"Nonetheless, these approaches still suffer from a drawback-a single model is trained for performing a single task with a distinct motion pattern (often time from a single mocap clip).",
"As a result, they might not generalize to higher-level interactive tasks that require flexible motion patterns.",
"Take the example of a person sitting down on a chair.",
"A person can start in any location and orientation relative to the chair (Fig. 1) .",
"A fixed motion pattern (e.g. turn left and sit) will be incapable of handling such variations.",
"In this paper, we focus on one class of high-level interactive tasks-sitting onto a chair.",
"As earlier mentioned, there are many possible human-chair configurations and different configurations may require different sequences of actions to accomplish the goal.",
"For example, if the human is facing the chair, it needs to walk, turn either left or right, and sit; if the human is behind the chair, it needs to walk, side-walk and sit.",
"To this end, we propose a hierarchical reinforcement learning (RL) method to address the challenge of generalization.",
"Our key idea is the use of hierarchical control:",
"(1) we assume the main task (e.g. sitting onto a chair) can be decomposed into several subtasks (e.g. walk, turn, sit, etc.), where the motion of each subtask can be reliably learned from mocap data, and (2) we train a meta controller using RL which can execute the subtasks properly to \"complete\" the main task from a given configuration.",
"Such strategy is in line with the observation that humans have a repertoire of motion skills, and different subset of skills is selected and executed for different high-level tasks.",
"Our contributions are three folds: (1) we extend the prior work on physics-based motion imitation to the context of higher-level interactive tasks using a hierarchical approach; (2) we experimentally demonstrate the strength of our hierarchical approach over different single level and hierarchical baselines; (3) we show at the end that our approach can be applied to motion synthesis in human living space with the help of 3D scene reconstruction."
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"Synthesizing human motions on interactive tasks using mocap data and hierarchical RL."
] |
[
"We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions.",
"We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens.",
"We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse.",
"We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points.",
"We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN.",
"We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.",
"Generative modeling involves taking a set of samples drawn from an unknown data generating distribution P real and finding an estimate P model that closely resembles it.",
"Generative adversarial networks (GAN) BID6 ) is a powerful framework used for fitting implicit generative models.",
"The basic setup consists of two networks, the generator and the discriminator, playing against each other in a repeated zero-sum game setting.",
"The goal here is to reach an equilibrium where P real , P model are close, and the alternating gradient updates procedure (AGD) is used to achieve this.",
"However, this process is highly unstable and often results in mode collapse BID7 .",
"This calls for an deeper investigation into training dynamics of GANs.In this paper, we propose studying GAN training dynamics as a repeated game in which both the players are using no-regret algorithms BID2 and discuss how AGD 1 falls under this paradigm.",
"In contrast, much of the theory BID6 BID0 and recent developments BID15 BID8 are based on the unrealistic assumption that the discriminator is playing optimally (in the function space) at each step and as a result, there is consistent minimization of a divergence between real and generated distributions.",
"This corresponds to at least one player using the best-response algorithm (in the function space), and the resulting game dynamics can be completely different in both these cases BID14 .",
"Thus, there is a clear disconnect between theoretical arguments used as motivation in recent literature and what actually happens in practice.We would like to point out that the latter view can still be useful for reasoning about the asymptotic equilibrium situation but we argue that regret minimization is the more appropriate way to think about GAN training dynamics.",
"So, we analyze the convergence of GAN training from this new point of view to understand why mode collapse happens.",
"We start with a short analysis of the artificial convex-concave case of the GAN game in section 2.2.",
"This setting has a unique solution and guaranteed convergence (of averaged iterates) using no-regret algorithms can be shown with standard arguments from game theory literature.",
"Here, we make explicit, the critical (previously not widely known) connection between AGD used in GAN training and regret minimization.",
"This immediately yields a novel proof for the asymptotic convergence of GAN training, in the non-parametric limit.",
"Prior to our work, such a result BID6 ) required a strong assumption that the discriminator is optimal at each step.However, these convergence results do not hold when the game objective function is non-convex, which is the practical case when deep neural networks are used.",
"In non-convex games, global regret minimization and equilibrium computation are computationally hard in general.",
"Recent gametheoretic literature indicates that AGD can end up cycling BID11 or converging to a (potentially bad) local equilibrium, under some conditions BID9 .",
"We hypothesize these to be the reasons for cycling and mode collapse observed during GAN training, respectively (section 2.3).",
"In this work, we do not explore the cycling issue but focus our attention on the mode collapse problem.",
"In contrast to our hypothesis, the prevalent view of mode collapse and instability BID0 is that it results from attempting to minimize a strong divergence during training.",
"However, as we argued earlier, GAN training with AGD does not consistently minimize a divergence and therefore, such a theory is not suitable to discuss convergence or to address the stability issue.Next, if mode collapse is indeed the result of an undesirable local equilibrium, a natural question then is how we can avoid it?",
"We make a simple observation that, in the GAN game, mode collapse situations are often accompanied by sharp gradients of the discriminator function around some real data points (section 2.4).",
"Therefore, a simple strategy to mitigate mode collapse is to regularize the discriminator so as to constrain its gradients in the ambient data space.",
"We demonstrate that this improves the stability using a toy experiment with one hidden layer neural networks.",
"This gives rise to a new explanation for why WGAN and gradient penalties might be improving the stability of GAN training -they are mitigating the mode collapse problem by keeping the gradients of the discriminator function small in data space.",
"From this motivation, we propose a training algorithm involving a novel gradient penalty scheme called DRAGAN (Deep Regret Analytic Generative Adversarial Networks) which enables faster training, achieves improved stability and modeling performance (over WGAN-GP BID8 which is the state-of-the-art stable training procedure) across a variety of architectures and objective functions.Below, we provide a short literature review.",
"Several recent works focus on stabilizing the training of GANs.",
"While some solutions BID17 BID18 require the usage of specific architectures (or) modeling objectives, some BID4 BID20 significantly deviate from the original GAN framework.",
"Other promising works in this direction BID12 BID16 BID8 ) impose a significant computational overhead.",
"Thus, a fast and versatile method for consistent stable training of GANs is still missing in the literature.",
"Our work is aimed at addressing this.To summarize, our contributions are as follows:• We propose a new way of reasoning about the GAN training dynamics -by viewing AGD as regret minimization.•",
"We provide a novel proof for the asymptotic convergence of GAN training in the nonparametric limit and it does not require the discriminator to be optimal at each step.•",
"We discuss how AGD can converge to a potentially bad local equilibrium in non-convex games and hypothesize this to be responsible for mode collapse during GAN training.•",
"We characterize mode collapse situations with sharp gradients of the discriminator function around some real data points.•",
"A novel gradient penalty scheme called DRAGAN is introduced based on this observation and we demonstrate that it mitigates the mode collapse issue.",
"In this paper, we propose to study GAN training process as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions.",
"We analyze the convergence of GAN training from this new point of view and hypothesize that mode collapse occurs due to the existence of undesirable local equilibria.",
"A simple observation is made about how the mode collapse situation often exhibits sharp gradients of the discriminator function around some real data points.",
"This characterization partly explains the workings of previously proposed WGAN and gradient penalties, and motivates our novel penalty scheme.",
"We show evidence of improved stability using DRAGAN and the resulting improvements in modeling performance across a variety of settings.",
"We leave it to future works to explore our ideas in more depth and come up with improved training algorithms."
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"Analysis of convergence and mode collapse by studying GAN training process as regret minimization"
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[
"Deep neural networks (DNNs) have attained surprising achievement during the last decade due to the advantages of automatic feature learning and freedom of expressiveness.",
"However, their interpretability remains mysterious because DNNs are complex combinations of linear and nonlinear transformations.",
"Even though many models have been proposed to explore the interpretability of DNNs, several challenges remain unsolved:",
"1) The lack of interpretability quantity measures for DNNs, 2) the lack of theory for stability of DNNs, and",
"3) the difficulty to solve nonconvex DNN problems with interpretability constraints.",
"To address these challenges simultaneously, this paper presents a novel intrinsic interpretability evaluation framework for DNNs.",
"Specifically, Four independent properties of interpretability are defined based on existing works.",
"Moreover, we investigate the theory for the stability of DNNs, which is an important aspect of interpretability, and prove that DNNs are generally stable given different activation functions.",
"Finally, an extended version of deep learning Alternating Direction Method of Multipliers (dlADMM) are proposed to solve DNN problems with interpretability constraints efficiently and accurately.",
"Extensive experiments on several benchmark datasets validate several DNNs by our proposed interpretability framework."
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"We propose a novel framework to evaluate the interpretability of neural network."
] |
[
"Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. ",
"The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation.",
"However, evidence from psychology, economics and neuroscience suggests that humans and animals instead have hyperbolic time-preferences. ",
"Here we extend earlier work of Kurth-Nelson and Redish and propose an efficient deep reinforcement learning agent that acts via hyperbolic discounting and other non-exponential discount mechanisms.",
"We demonstrate that a simple approach approximates hyperbolic discount functions while still using familiar temporal-difference learning techniques in RL. ",
"Additionally, and independent of hyperbolic discounting, we make a surprising discovery that simultaneously learning value functions over multiple time-horizons is an effective auxiliary task which often improves over state-of-the-art methods.",
"The standard treatment of the reinforcement learning (RL) problem is the Markov Decision Process (MDP) which includes a discount factor 0 ≤ γ ≤ 1 that exponentially reduces the present value of future rewards (Bellman, 1957; Sutton & Barto, 1998) .",
"A reward r t received in t-time steps is devalued to γ t r t , a discounted utility model introduced by Samuelson (1937) .",
"This establishes a timepreference for rewards realized sooner rather than later.",
"The decision to exponentially discount future rewards by γ leads to value functions that satisfy theoretical convergence properties (Bertsekas, 1995) .",
"The magnitude of γ also plays a role in stabilizing learning dynamics of RL algorithms (Prokhorov & Wunsch, 1997; Bertsekas & Tsitsiklis, 1996) and has recently been treated as a hyperparameter of the optimization (OpenAI, 2018; Xu et al., 2018) .",
"However, both the magnitude and the functional form of this discounting function establish priors over the solutions learned.",
"The magnitude of γ chosen establishes an effective horizon for the agent of 1/(1 − γ), far beyond which rewards are neglected (Kearns & Singh, 2002) .",
"This effectively imposes a time-scale of the environment, which may not be accurate.",
"Further, the exponential discounting of future rewards is consistent with a prior belief that there is a known constant per-time-step hazard rate (Sozou, 1998) or probability of dying of 1 − γ (Lattimore & Hutter, 2011).",
"Additionally, discounting future values exponentially and according to a single discount factor γ does not harmonize with the measured value preferences in humans 1 and animals (Mazur, 1985; Ainslie, 1992; Green & Myerson, 2004; Maia, 2009) .",
"A wealth of empirical evidence has been amassed that humans, monkeys, rats and pigeons instead discount future returns hyperbolically, where d k (t) = 1 1+kt , for some positive k > 0 (Ainslie, 1975; 1992; Mazur, 1985; Frederick et al., 2002; Green et al., 1981; Green & Myerson, 2004) .",
"This discrepancy between the time-preferences of animals from the exponential discounted measure of value might be presumed irrational.",
"But Sozou (1998) showed that hyperbolic time-preferences is mathematically consistent with the agent maintaining some uncertainty over the prior belief of the hazard rate in the environment.",
"Hazard rate h(t) measures the per-time-step risk the agent incurs as it acts in the environment due to a potential early death.",
"Precisely, if s(t) is the probability that the agent is alive at time t then the hazard rate is h(t) = − d dt lns(t).",
"We consider the case where there is a fixed, but potentially unknown hazard rate h(t) = λ ≥ 0.",
"The prior belief of the hazard rate p(λ) implies a specific discount function Sozou (1998) .",
"Under this formalism, the canonical case in RL of discounting future rewards according to d(t) = γ t is consistent with the belief that there exists a single hazard rate λ = e −γ known with certainty.",
"Further details are available in Appendix A. Figure 1: Hyperbolic versus exponential discounting.",
"Humans and animals often exhibit hyperbolic discounts (blue curve) which have shallower discount declines for large horizons.",
"In contrast, RL agents often optimize exponential discounts (orange curve) which drop at a constant rate regardless of how distant the return.",
"Common RL environments are also characterized by risk, but often in a narrower sense.",
"In deterministic environments like the original Arcade Learning Environment (ALE) (Bellemare et al., 2013) stochasticity is often introduced through techniques like no-ops (Mnih et al., 2015) and sticky actions (Machado et al., 2018) where the action execution is noisy.",
"Physics simulators may have noise and the randomness of the policy itself induces risk.",
"But even with these stochastic injections the risk to reward emerges in a more restricted sense.",
"In Section 2 we show that a prior distribution reflecting the uncertainty over the hazard rate, has an associated discount function in the sense that an MDP with either this hazard distribution or the discount function, has the same value function for all policies.",
"This equivalence implies that learning policies with a discount function can be interpreted as making them robust to the associated hazard distribution.",
"Thus, discounting serves as a tool to ensure that policies deployed in the real world perform well even under risks they were not trained under.",
"We propose an algorithm that approximates hyperbolic discounting while building on successful Qlearning (Watkins & Dayan, 1992) tools and their associated theoretical guarantees.",
"We show learning many Q-values, each discounting exponentially with a different discount factor γ, can be aggregated to approximate hyperbolic (and other non-exponential) discount factors.",
"We demonstrate the efficacy of our approximation scheme in our proposed Pathworld environment which is characterized both by an uncertain per-time-step risk to the agent.",
"Conceptually, Pathworld emulates a foraging environment where an agent must balance easily realizable, small meals versus more distant, fruitful meals.",
"We then consider higher-dimensional deep RL agents in the ALE, where we measure the benefits of hyperbolic discounting.",
"This approximation mirrors the work of Kurth-Nelson & Redish (2009); Redish & Kurth-Nelson (2010) which empirically demonstrates that modeling a finite set of µAgents simultaneously can approximate hyperbolic discounting function.",
"Our method then generalizes to other non-hyperbolic discount functions and uses deep neural networks to model the different Q-values from a shared representation.",
"Surprisingly and in addition to enabling new non-exponential discounting schemes, we observe that learning a set of Q-values is beneficial as an auxiliary task (Jaderberg et al., 2016) .",
"Adding this multi-horizon auxiliary task often improves over a state-of-the-art baseline, Rainbow (Hessel et al., 2018) in the ALE (Bellemare et al., 2013) .",
"This work questions the RL paradigm of learning policies through a single discount function which exponentially discounts future rewards through the following contributions:",
"1. Hazardous MDPs.",
"We formulate MDPs with hazard present and demonstrate an equivalence between undiscounted values learned under hazards and (potentially nonexponentially) discounted values without hazard.",
"2. Hyperbolic (and other non-exponential)-agent.",
"A practical approach for training an agent which discounts future rewards by a hyperbolic (or other non-exponential) discount function and acts according to this.",
"3. Multi-horizon auxiliary task.",
"A demonstration of multi-horizon learning over many γ simultaneously as an effective auxiliary task."
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"A deep RL agent that learns hyperbolic (and other non-exponential) Q-values and a new multi-horizon auxiliary task."
] |
[
"Emotion is playing a great role in our daily lives.",
"The necessity and importance of an automatic Emotion recognition system is getting increased.",
"Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc.",
"However, these features can potentially be changed to fake features.",
"So to detect hidden and real features that is not controlled by the person are data measured from brain signals.",
"There are various ways of measuring brain waves: EEG, MEG, FMRI, etc.",
"On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work.",
"The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli.",
"The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements). ",
"The signals were filtered and type of frequency band was computed and detected.",
"The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli.",
"Finally, the performance of the proposed approach was tested.",
"The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively. ",
"In this study, we also applied EEG applications in the context of neuro-marketing.",
"The results empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service."
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"This paper presents EEG based emotion detection of a person towards an image stimuli and its applicability on neuromarketing."
] |
[
" We present a probabilistic framework for session based recommendation. ",
"A latent variable for the user state is updated as the user views more items and we learn more about their interests. ",
"We provide computational solutions using both the re-parameterization trick and using the Bouchard bound for the softmax function, we further explore employing a variational auto-encoder and a variational Expectation-Maximization algorithm for tightening the variational bound. ",
"Finally we show that the Bouchard bound causes the denominator of the softmax to decompose into a sum enabling fast noisy gradients of the bound giving a fully probabilistic algorithm reminiscent of word2vec and a fast online EM algorithm.\n"
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"Fast variational approximations for approximating a user state and learning product embeddings"
] |
[
"An important question in task transfer learning is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.",
"Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning.",
"In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems.",
"Inspired by a principled information theoretic approach, H-score has a direct connection to the asymptotic error probability of the decision function based on the transferred feature.",
"This formulation of transferability can further be used to select a suitable set of source tasks in task transfer learning problems or to devise efficient transfer learning policies.",
"Experiments using both synthetic and real image data show that not only our formulation of transferability is meaningful in practice, but also it can generalize to inference problems beyond classification, such as recognition tasks for 3D indoor-scene understanding.",
"Transfer learning is a learning paradigm that exploits relatedness between different learning tasks in order to gain certain benefits, e.g. reducing the demand for supervision BID22 ).",
"In task transfer learning, we assume that the input domain of the different tasks are the same.",
"Then for a target task T T , instead of learning a model from scratch, we can initialize the parameters from a previously trained model for some related source task T S .",
"For example, deep convolutional neural networks trained for the ImageNet classification task have been used as the source network in transfer learning for target tasks with fewer labeled data BID7 ), such as medical image analysis BID24 ) and structural damage recognition in buildings (Gao & Mosalam) .",
"An imperative question in task transfer learning is transferability, i.e. when a transfer may work and to what extent.",
"Given a metric capable of efficiently and accurately measuring transferability across arbitrary tasks, the problem of task transfer learning, to a large extent, is simplified to search procedures over potential transfer sources and targets as quantified by the metric.",
"Traditionally, transferability is measured purely empirically using model loss or accuracy on the validation set (Yosinski et al. (2014) ; Zamir et al. (2018) ; BID5 ).",
"There have been theoretical studies that focus on task relatedness BID1 ; BID19 ; BID21 ; BID2 ).",
"However, they either cannot be computed explicitly from data or do not directly explain task transfer performance.",
"In this study, we aim to estimate transferability analytically, directly from the training data.We quantify the transferability of feature representations across tasks via an approach grounded in statistics and information theory.",
"The key idea of our method is to show that the error probability of using a feature of the input data to solve a learning task can be characterized by a linear projection of this feature between the input and output domains.",
"Hence we adopt the projection length as a metric of the feature's effectiveness for the given task, and refer to it as the H-score of the feature.",
"More generally, H-score can be applied to evaluate the performance of features in different tasks, and is particularly useful to quantify feature transferability among tasks.",
"Using this idea, we define task transferability as the normalized H-score of the optimal source feature with respect to the target task.As we demonstrate in this paper, the advantage of our transferability metric is threefold.",
"(i) it has a strong operational meaning rooted in statistics and information theory;",
"(ii) it can be computed directly and efficiently from the input data, with fewer samples than those needed for empirical learning;",
"(iii) it can be shown to be strongly consistent with empirical transferability measurements.In this paper, we will first present the theoretical results of the proposed transferability metric in Section 2-4.",
"Section 5 presents several experiments on real image data , including image classificaton tasks using the Cifar 100 dataset and 3D indoor scene understanding tasks using the Taskonomy dataset created by Zamir et al. (2018) .",
"A brief review of the related works is included in Section 6.",
"In this paper, we presented H-score, an information theoretic approach to estimating the performance of features when transferred across classification tasks.",
"Then we used it to define a notion of task transferability in multi-task transfer learning problems, that is both time and sample complexity efficient.",
"The resulting transferability metric also has a strong operational meaning as the ratio between the best achievable error exponent of the transferred representation and the minium error exponent of the target task.Our transferability score successfully predicted the performance for transfering features from ImageNet-1000 classification task to Cifar-100 task.",
"Moreover, we showed how the transferability metric can be applied to a set of diverse computer vision tasks using the Taskonomy dataset.In future works, we plan to extend our theoretical results to non-classification tasks, as well as relaxing the local assumptions on the conditional distributions of the tasks.",
"We will also investigate properties of higher order transferability, developing more scalable algorithms that avoid computing the H-score of all task pairs.",
"On the application side, as transferability tells us how different tasks are related, we hope to use this information to design better task hierarchies for transfer learning.",
"DISPLAYFORM0 x m with the following hypotheses: DISPLAYFORM1 Let P x m be the empirical distribution of the samples.",
"The optimal test, i.e., the log likelihood ratio test can be stated in terms of information-theoretic quantities as follows: DISPLAYFORM2 Figure 10: The binary hypothesis testing problem.",
"The blue curves shows the probility density functions for P 1 and P 2 .",
"The rejection region and the acceptance region are highlighted in red and blue, respectively.",
"The vertical line indicates the decision threshold.Further, using Sannov's theorem, we have that asymptotically the probability of type I error DISPLAYFORM3 where P * DISPLAYFORM4 m log T } denotes the rejection region.",
"Similarly, for type II error DISPLAYFORM5 where P * 2 = argmin P ∈A D(P ||P 2 ) and A = {x m : FIG1 The overall probability of error is P (m) e = αP r(H 0 ) + βP r(H 1 ) and the best achievable exponent in the Bayesian probability of error (a.k.a. Chernoff exponent) is defined as: DISPLAYFORM6 DISPLAYFORM7 See Cover & BID6 for more background information on error exponents and its related theorems.Under review as a conference paper at ICLR 2019"
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"We present a provable and easily-computable evaluation function that estimates the performance of transferred representations from one learning task to another in task transfer learning."
] |
[
"This paper presents a generic framework to tackle the crucial class mismatch problem in unsupervised domain adaptation (UDA) for multi-class distributions. ",
"Previous adversarial learning methods condition domain alignment only on pseudo labels, but noisy and inaccurate pseudo labels may perturb the multi-class distribution embedded in probabilistic predictions, hence bringing insufficient alleviation to the latent mismatch problem. ",
"Compared with pseudo labels, class prototypes are more accurate and reliable since they summarize over all the instances and are able to represent the inherent semantic distribution shared across domains.",
"Therefore, we propose a novel Prototype-Assisted Adversarial Learning (PAAL) scheme, which incorporates instance probabilistic predictions and class prototypes together to provide reliable indicators for adversarial domain alignment. ",
"With the PAAL scheme, we align both the instance feature representations and class prototype representations to alleviate the mismatch among semantically different classes. ",
"Also, we exploit the class prototypes as proxy to minimize the within-class variance in the target domain to mitigate the mismatch among semantically similar classes. ",
"With these novelties, we constitute a Prototype-Assisted Conditional Domain Adaptation (PACDA) framework which well tackles the class mismatch problem.",
"We demonstrate the good performance and generalization ability of the PAAL scheme and also PACDA framework on two UDA tasks, i.e., object recognition (Office-Home,ImageCLEF-DA, andOffice) and synthetic-to-real semantic segmentation (GTA5→CityscapesandSynthia→Cityscapes).",
"Unsupervised domain adaptation (UDA) aims to leverage the knowledge of a labeled data set (source domain) to help train a predictive model for a unlabeled data set (target domain).",
"Deep UDA methods bring noticeable performance gain to many tasks (Long et al., 2015; Saito et al., 2017; Richter et al., 2016; Tsai et al., 2018; Lee et al., 2019; Vu et al., 2019a) by exploiting supervision from heterogeneous sources.",
"Some methods exploit maximum mean discrepancy (MMD) (Gretton et al., 2008; Long et al., 2015) or other distribution statistics like central moments (Sun & Saenko, 2016; Zellinger et al., 2017; Koniusz et al., 2017) for domain adaptation.",
"Recently, generative adversarial learning (Goodfellow et al., 2014) provides a promising alternative solution to UDA problem.",
"Since the labels of the target instances are not given in UDA, adversarial learning scheme for adaptation (Ganin & Lempitsky, 2015) suffers from the cross-domain misalignment, where the target instances from a class A are potentially misaligned with source instances from another class B. Inspired by the pseudo-labeling strategy from semi-supervised learning, previous methods either used the pseudo labels in the target domain to perform joint distribution discrepancy minimization (Long et al., 2013; or developed conditional adversarial learning methods that involve one high-dimensional domain discriminator or multiple discriminators (Chen et al., 2017b; Pei et al., 2018) .",
"Though effective, these conditional domain adversarial learning methods align different instances from different domains relying only on their own predictions.",
"Simple probabilistic predictions or pseudo labels may not accurately represent the semantic information of input instances, misleading the alignment.",
"A toy example is given in Fig. 1(a) .",
"The pseudo label of the chosen instance x is inclined to be class 'square' while the ground truth label is class 'circle'.",
"Only guided by the instance prediction, the 'circle' class in the target domain and the 'square' class in the source domain are easily confused, causing the misalignment in the adversarial domain adaptation.",
"To remedy the misalignment, we propose to exploit the class prototypes for adversarial domain alignment, instead of using only the possibly inaccurate predictions.",
"Prototypes are global feature representations of different classes and are relevant to the inherent semantic structures shared across",
"(a) conditional adversarial learning",
"(b) prototype-assisted adversarial learning",
"Figure 1: Illustration of two adversarial learning schemes.",
"Different from class-agnostic adversarial learning that pursues the marginal distribution alignment but ignores the semantic consistency,",
"(a) conditional adversarial learning relies heavily on the instance-level pseudo labels to perform conditional distribution alignment, while",
"(b) our prototype-assisted adversarial learning integrates the instance-level pseudo labels and global class prototypes to make the conditional indicators more reliable.",
"Class information is denoted in different shapes with source in solid and target in hollow.",
"domains.",
"As shown in Fig. 1(b) , class prototypes are expected to remedy the negative effects of inaccurate probabilistic predictions.",
"Motivated by this, we propose a Prototype-Assisted Adversarial Learning (PAAL) scheme which complements instance predictions with class prototypes to obtain more reliable conditional information for guiding the source-target feature representation alignment.",
"Specifically, we summarize the class prototypes from all instances according to their predictions.",
"In this way, on one hand, we lower the dependence of class prototypes on instance predictions which may be inaccurate, and on the other hand, we encourage the instances with greater certainty to contribute more to their corresponding class prototypes.",
"The prototypes are updated dynamically through a moving average strategy to make them more accurate and reliable.",
"Then by broadcasting class prototypes to each instance according to its probability prediction, the inaccurate semantic distribution depicted by instance predictions can be alleviated.",
"Based on reliable prototype-based conditional information, we align both the instance feature representations and the class prototypes through the proposed PAAL scheme to relieve the alignment among semantically dissimilar instances.",
"However, such a conditional domain alignment may promote the confusion among semantically similar instances across domains to some degree.",
"To further alleviate it, we introduce an intra-class objective in the target domain to pursue the class compactness.",
"Built on the proposed PAAL scheme and this intra-class compactness objective, we develop a Prototype-Assisted Conditional Domain Adaptation (PACDA) framework for solving UDA problems.",
"Extensive experimental evaluations on both object recognition and semantic segmentation tasks clearly demonstrate the advantages of our approaches over previous state-of-the-arts Xu et al., 2019; Tsai et al., 2019) .",
"The contributions of this work can be summarized into three folds:",
"1) To the best of our knowledge, we are the first to leverage the class prototypes in conditional adversarial learning to prevent the misalignment in UDA;",
"2) We propose a simple yet effective domain adversarial learning framework PACDA to remedy the misalignment among semantically similar instances as well as semantically dissimilar instances;",
"3) The proposed PAAL scheme and PACDA framework are generic, and our framework achieves the state-of-the-art results on several unsupervised domain adaptation tasks including object recognition and semantic segmentation.",
"In this work, we developed the prototype-assisted adversarial learning scheme to remedy the misalignment for UDA tasks.",
"Unlike previous conditional ones whose performance is vulnerable to inaccurate instance predictions, our proposed scheme leverages the reliable and accurate class prototypes for aligning multi-class distributions across domains and is demonstrated to be more effective to prevent the misalignment.",
"Then we further augment this scheme by imposing the intra-class compactness with the prototypes as proxy.",
"Extensive evaluations on both object recognition and semantic segmentation tasks clearly justify the effectiveness and superiority of our UDA methods over well-established baselines."
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] | Byg79h4tvB | true | [
"We propose a reliable conditional adversarial learning scheme along with a simple, generic yet effective framework for UDA tasks."
] |
[
"We present a new methodology that constructs a family of \\emph{positive definite kernels} from any given dissimilarity measure on structured inputs whose elements are either real-valued time series or discrete structures such as strings, histograms, and graphs. \n",
"Our approach, which we call D2KE (from Distance to Kernel and Embedding), draws from the literature of Random Features.\n",
"However, instead of deriving random feature maps from a user-defined kernel to approximate kernel machines, we build a kernel from a random feature map, that we specify given the distance measure. \n",
"We further propose use of a finite number of random objects to produce a random feature embedding of each instance.\n",
"We provide a theoretical analysis showing that D2KE enjoys better generalizability than universal Nearest-Neighbor estimates. \n",
"On one hand, D2KE subsumes the widely-used \\emph{representative-set method} as a special case, and relates to the well-known \\emph{distance substitution kernel} in a limiting case. \n",
"On the other hand, D2KE generalizes existing \\emph{Random Features methods} applicable only to vector input representations to complex structured inputs of variable sizes. \n",
"We conduct classification experiments over such disparate domains as time series, strings, and histograms (for texts and images), for which our proposed framework compares favorably to existing distance-based learning methods in terms of both testing accuracy and computational time."
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] | HyldojC9t7 | false | [
"From Distance to Kernel and Embedding via Random Features For Structured Inputs"
] |
[
"Deep Convolution Neural Networks (CNNs), rooted by the pioneer work of \\cite{Hinton1986,LeCun1985,Alex2012}, and summarized in \\cite{LeCunBengioHinton2015}, have been shown to be very useful in a variety of fields. ",
"The state-of-the art CNN machines such as image rest net \\cite{He_2016_CVPR} are described by real value inputs and kernel convolutions followed by the local and non-linear rectified linear outputs. ",
"Understanding the role of these layers, the accuracy and limitations of them, as well as making them more efficient (fewer parameters) are all ongoing research questions. \n \n ",
"Inspired in quantum theory, we propose the use of complex value kernel functions, followed by the local non-linear absolute (modulus) operator square.",
"We argue that an advantage of quantum inspired complex kernels is robustness to realistic unpredictable scenarios (such as clutter noise, data deformations).",
"We study a concrete problem of shape detection and show that when multiple overlapping shapes are deformed and/or clutter noise is added, a convolution layer with quantum inspired complex kernels outperforms the statistical/classical kernel counterpart and a \"Bayesian shape estimator\" .",
"The superior performance is due to the quantum phenomena of interference, not present in classical CNNs. ",
"The convolution process in machine learning maybe summarized as follows.",
"Given an input f L−1",
"(x) ≥ 0 to a convolution layer L, it produces an output DISPLAYFORM0 From g L",
"(y) a local and non-linear function is applied, f L",
"(y) = f (g L",
"(y)), e.g., f = ReLu (rectified linear units) or f = |.|",
", the magnitude operator.",
"This output is then the input to the next convolution layer (L+1) or simply the output of the whole process.",
"We can also write a discrete form of these convolutions, as it is implemented in computers.",
"We write g DISPLAYFORM1 , where the continuous variables y, x becomes the integers i, j respectively, the kernel function K(y −",
"x) → w ij becomes the weights of the CNN and the integral over dx becomes the sum over j.These kernels are learned from data so that an error (or optimization criteria) is minimized.",
"The kernels used today a real value functions.",
"We show how our understanding of the optimization criteria \"dictate\" the construction of the quantum inspired complex value kernel.",
"In order to concentrate and study our proposal of quantum inspired kernels, we simplify the problem as much as possible hoping to identify the crux of the limitation of current use of real value kernels.We place known shapes in an image, at any location, and in the presence of deformation and clutter noise.",
"These shapes may have been learned by a CNN.",
"Our main focus is on the feedforward performance, when new inputs are presented.",
"Due to this focus, we are able to construct a Bayesian a posteriori probability model to the problem, which is based on real value prior and likelihood models, and compare it to the quantum inspired kernel method.The main advantage of the quantum inspired method over existing methods is its high resistance to deviations from the model, such as data deformation, multiple objects (shapes) overlapping, clutter noise.",
"The main new factor is the quantum interference phenomenon BID1 BID0 , and we argue it is a desired phenomena for building convolution networks.",
"It can be carried out by developing complex value kernels driven by classic data driven optimization criteria.",
"Here we demonstrate its strength on a shape detection problem where we can compare it to state of the art classical convolution techniques.",
"We also can compare to the MAP estimator of the Bayesian model for the shape detection problem.To be clear, we do not provide (yet) a recipe on how to build kernels for the full CNN framework for machine learning, and so the title of this paper reflects that.",
"Here, we plant a seed on the topic of building complex value kernels inspired in quantum theory, by demonstrating that for a given one layer problem of shape detection (where the classic data optimization criteria is well defined), we can build such complex value kernel and demonstrate the relevance of the interference phenomena.To our knowledge such a demonstration is a new contribution to the field.",
"We also speculate on how this process can be generalized.",
"Deep Convolution Neural Networks (CNNs), rooted on the pioneer work of BID8 ; BID4 ; BID3 , and summarized in BID5 , have been shown to be very useful in a variety of fields.Inspired in quantum theory, we investigated the use of complex value kernel functions, followed by the local non-linear absolute (modulus) operator square.",
"We studied a concrete problem of .",
"For each of the figures 5a, 5b,5c we vary we vary b = 1 2 a, a, 2a (or center displacements δµ = 0.25, 0.5, 1), respectively.",
"These figures depict ratios Q(a, b, ) × (blue) for ∈ (0.047, 0.2802) and H(a, b, α) × ← − α (red) for ← − α ∈ (22.727, 2.769) (The reverse arrow implies the x-axis start at the maximum value and decreases thereafter).",
"All plots have 200 points, with uniform steps in their respective range.",
"Note that our proposed parameter value is = 0.1401, the solution to equation FORMULA42 , and indeed gives a high ratio.",
"Also, α = 2.769 is the smallest value to yield all Hough votes in the center.",
"Clearly the quantum ratio outperforms the best classical Hough method, which does not vary much across α values.",
"As the center displacement increases, the quantum method probability, for = 0.1401, decreases much faster than the Hough method probability.",
"Final figure 5d display values of |ψ| 2 (µ * ) × (at the true center) in blue, for ∈ (0.047, 0.2802), with 200 uniform steps.",
"In red, V (µ * ) × ← − α for ← − α ∈ (22.727, 2.769), with 200 uniform steps.",
"DISPLAYFORM0 shape detection and showed that when multiple overlapping shapes are deformed and/or clutter noise is added, a convolution layer with quantum inspired complex kernels outperforms the statistical/classical kernel counterpart and a \"Bayesian shape estimator\".",
"It is worth to mention that the Bayesian shape estimator is the best method as long as the data satisfy the model assumptions.",
"Once we add multiple shapes, or add clutter noise (not uniform noise), the Bayesian method breaks down rather easily, but not the quantum method nor the statistical version of it (the Hough method being an approximation to it).",
"An analysis comparing the Quantum method to the Hough method was carried out to demonstrate the superior accuracy performance of the quantum method, due to the quantum phenomena of interference, not present in the classical CNN.We have not focused on the problem of learning the shapes here.",
"Given the proposed quantum kernel method, the standard techniques of gradient descent method should also work to learn the kernels, since complex value kernels are also continuous and differentiable.",
"Each layer of the networks carries twice as many parameters, since complex numbers are a compact notation for two numbers, but the trust of the work is to suggest that they may perform better and reduce the size of the entire network.",
"These are just speculations and more investigation of the details that entice such a construction are needed.",
"Note that many articles in the past have mentioned \"quantum\" and \"neural networks\" together.",
"Several of them use Schrödinger equation, a quantum physics modeling of the world.",
"Here in no point we visited a concept in physics (forces, energies), as Schrödinger equation would imply, the only model is the one of shapes (computer vision model).",
"Quantum theory is here used as an alternative statistical method, a purely mathematical construction that can be applied to different models and fields, as long as it brings benefits.",
"Also, in our search, we did not find an article that explores the phenomena of interference and demonstrate its advantage in neural networks.",
"The task of brining quantum ideas to this field must require demonstrations of its utility, and we think we did that here."
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"A quantum inspired kernel for convolution network, exhibiting interference phenomena, can be very useful (and compared it with real value counterpart)."
] |
[
"We present an artificial intelligence research platform inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs).",
"We demonstrate how this platform can be used to study behavior and learning in large populations of neural agents.",
"Unlike currently popular game environments, our platform supports persistent environments, with variable number of agents, and open-ended task descriptions.",
"The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources.",
"Our platform aims to simulate this setting in microcosm: we conduct a series of experiments to test how large-scale multiagent competition can incentivize the development of skillful behavior.",
"We find that population size magnifies the complexity of the behaviors that emerge and results in agents that out-compete agents trained in smaller populations.",
"Life on Earth can be viewed as a massive multiagent competition.",
"The cheetah evolves an aerodynamic profile in order to catch the gazelle, the gazelle develops springy legs to run even faster: species have evolved ever new capabilities in order to outcompete their adversaries.The success of biological evolution has inspired many attempts to emulate it in silico, ranging from genetic algorithms that bear only loose resemblance to natural processes, to full-blown simulations of \"artificial life\".",
"A recurring question has been: at what level of abstraction should we simulate the competitive game of life?In",
"recent years, the field of deep reinforcement learning (RL) has embraced a related approach: train algorithms by having them compete in simulated games BID16 BID14 BID8 . Such",
"games are immediately interpretable and provide easy metrics derived from the game's \"score\" and win conditions. However",
", popular game benchmarks are currently still limited: they typically define a narrow, episodic task, with a small fixed number of players. In contrast",
", life on Earth involves a persistent environment, an unbounded number of players, and a seeming \"open-endedness\", where ever new and more complex species emerge over time, with no end in sight BID18 .Our aim is",
"to develop a simulation platform (see FIG3 ) that captures important properties of life on Earth, while also borrowing from the interpretability and abstractions of human-designed games. To this end",
", we turn to the game genre of Massively Multiplayer Online Role-Playing Games (MMORPGs, or MMOs for short). These games",
"involve a large, variable number of players competing to survive and prosper in persistent and far-flung environments. Our platform",
"simulates a \"Neural MMO\" -an MMO in which each agent is a neural net that learns to survive using RL.We demonstrate the capabilities of this platform through a series of experiments that investigate emergent complexity as a function of the number of agents and species that compete in the simulation. We find that",
"large populations act as competitive pressure that encourages exploration of the environment and the development of skillful behavior. In addition,",
"we find that when agents are organized into species (share policy parameters), each species naturally diverges from the others to occupy its own behavioral niche. Upon publication",
", we will opensource the platform in full. We alternate between",
"collecting experience across 100 procedurally generated worlds and updating agents' parameters via policy gradients. Test time visualization",
"provides insight into the learned policies through value function estimates, map tile visitation distribution, and agent-agent dependencies."
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"An MMO-inspired research game platform for studying emergent behaviors of large populations in a complex environment"
] |
[
"In this paper, we propose an improved quantitative evaluation framework for Generative Adversarial Networks (GANs) on generating domain-specific images, where we improve conventional evaluation methods on two levels: the feature representation and the evaluation metric.",
"Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation.",
"Moreover, for datasets with multiple classes, we propose Class-Aware Frechet Distance (CAFD), which employs a Gaussian mixture model on the feature space to better fit the multi-manifold feature distribution.",
"Experiments and analysis on both the feature level and the image level were conducted to demonstrate improvements of our proposed framework over the recently proposed state-of-the-art FID method.",
"To our best knowledge, we are the first to provide counter examples where FID gives inconsistent results with human judgments.",
"It is shown in the experiments that our framework is able to overcome the shortness of FID and improves robustness.",
"Code will be made available.",
"Generative Adversarial Networks (GANs) have shown outstanding abilities on many computer vision tasks including generating domain-specific images BID7 , style transfer , super resolution BID20 , etc.",
"The basic idea of GANs is to hold a two-player game between generator and discriminator, where the discriminator aims to distinguish between real and fake samples while the generator tries to generate samples as real as possible to fool the discriminator.Researchers have been continuously exploring better GAN architectures.",
"However, developing a widely-accepted GAN evaluation framework remains to be a challenging topic BID35 .",
"Due to a lack of GAN benchmark results, newly proposed GAN variants are validated on different evaluation frameworks and therefore incomparable.",
"Because human judgements are inherently limited by manpower resource, good quantitative evaluation frameworks are of very high importance to guide future research on designing, selecting, and interpreting GAN models.There have been varieties of efforts on designing sample-based evaluation for GANs on its ability of generating domain-specific images.",
"The goal is to measure the distance between the generated samples and the real in the dataset.",
"Most existing methods utilized the ImageNet BID29 inception model to map images onto the feature space.",
"The most widely used criteria is probably the Inception Score BID31 , which measures the distance via Kullback-Leiber Divergence (KLD).",
"However, it is probability based and is unable to report overfitting.",
"Recently, Frechet Inception Distance (FID) was proposed BID11 on improving Inception Score.",
"It directly measures Frechet Distance on the feature space with the Gaussian assumption.",
"It has been proved that FID is far better than Inception Score BID13 BID15 BID24 .",
"However, we argue that assuming normality on the whole feature distribution may lose class information on labeled datasets.In this work, we propose an improved quantitative sample-based evaluating criteria.",
"We improve conventional evaluation methods on two levels: the feature representation and the evaluation metric.Unlike most existing methods including the Inception Score BID31 and FID BID11 , our framework uses a specialized encoder trained on the dataset to get domain-specific representation.",
"We argue that applying the ImageNet model to either labeled or unlabeled datasets is ineffective.",
"Moreover, we propose Class-Aware Frechet Distance (CAFD) in our framework to measure the distribution distance of each class (mode) respectively on the feature space to include class information.",
"Instead of the single Gaussian assumption, we employ a Gaussian mixture model (GMM) to better fit the feature distribution.",
"We also include KL divergence (KLD) between mode distribution of real data and generated samples into the framework to help detect mode dropping.Experiments and analysis on both the feature level and the image level were conducted to demonstrate the improved effectiveness of our proposed framework.",
"To our best knowledge, we are the first BID4 to provide counter examples where FID is inconsistent with human judgements (See FIG0 ).",
"It is shown in the experiments that our framework is able to overcome the shortness of existing methods.",
"Our method is sensitive to different representations.",
"Different selection of encoders can result in changes on the evaluation results.",
"Experiments in Section 5.1 demonstrate that the ImageNet inception model will give misleading results (See FIG0 .",
"Thus, a domain-specific encoder should be used in each evaluation pipeline.",
"Because the representation is not fixed, the correct use (with",
"In this paper, we aimed to tackle the very important problem of evaluating the Generative Adversarial Networks.",
"We presented an improved sample-based evaluation, which improves conventional methods on both representation and evaluation metric.",
"We argue that a domain-specific encoder is needed and propose Class-Aware Frechet Distance to better fit the feature distribution.",
"To our best knowledge, we are the first to provide counter examples where the state-of-the-art FID method is inconsistent with human judgements.",
"Experiments and analysis on both the feature level and the image level have shown that our framework is more effective.",
"Therefore, the encoder should be specifically trained for datasets of which the labels are different from ImageNet.",
"To attain effective representations on non-ImageNet datasets, we need to ensure that the class labels of data used for training GAN models are consistent with those of data used for training the encoder."
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] | HJlY0jA5F7 | true | [
"This paper improves existing sample-based evaluation for GANs and contains some insightful experiments."
] |
[
"Recent efforts on training light-weight binary neural networks offer promising execution/memory efficiency.",
"This paper introduces ResBinNet, which is a composition of two interlinked methodologies aiming to address the slow convergence speed and limited accuracy of binary convolutional neural networks.",
"The first method, called residual binarization, learns a multi-level binary representation for the features within a certain neural network layer.",
"The second method, called temperature adjustment, gradually binarizes the weights of a particular layer.",
"The two methods jointly learn a set of soft-binarized parameters that improve the convergence rate and accuracy of binary neural networks.",
"We corroborate the applicability and scalability of ResBinNet by implementing a prototype hardware accelerator.",
"The accelerator is reconfigurable in terms of the numerical precision of the binarized features, offering a trade-off between runtime and inference accuracy.\n",
"Convolutional Neural Networks (CNNs) have shown promising inference accuracy for learning applications in various domains.",
"These models are generally over-parameterized to facilitate the convergence during the training phase BID7 ; BID4 ).",
"A line of optimization methodologies such as tensor decomposition BID9 ; BID16 ), parameter quantization ; BID5 ), sparse convolutions BID10 ; ), and binary networks ; BID11 ) have been proposed to reduce the complexity of neural networks for efficient execution.",
"Among these works, binary neural networks result in two particular benefits:",
"(i) They reduce the memory footprint by a factor of 32 compared to the full-precision model; this is specifically important since memory access plays an essential role in the execution of CNNs on resource-constrained devices.",
"(ii) Binary networks replace the costly multiplications with simple XNOR operations BID11 ; BID12 ), reducing the execution time and energy consumption significantly.Considering the prior art, there exist two major challenges associated with binary neural networks.",
"First, the convergence rate of the existing solutions for training binary CNNs is considerably slower than their full-precision counterparts.",
"Second, in order to achieve comparable classification accuracy, binarized neural networks often compensate for the numerical precision loss by employing high dimensional feature maps in a wide CNN topology, which in turn reduces the effective compression rate.",
"As a result, full-precision networks often surpass binary networks in terms of convergence rate and final achievable accuracy.In this paper, we propose ResBinNet, a novel solution for increasing the convergence rate and the final accuracy of binary networks.",
"The global flow of ResBinNet is depicted in FIG0 .",
"The first phase, which we call Soft Binarization, includes two methodologies that we propose to address the aforementioned challenges for training binary CNNs.",
"First, we introduce a Residual Binarization scheme which allows the number of possible values for activation units to be reconfigurable at runtime.",
"To this purpose, we learn a multi-level residual representation for the features within the CNN to adaptively increase the numerical precision of the activation units.",
"Second, we introduce a novel weight binarization approach, called Tempreture Adjustment, which aims to gradually enforce binarization constraints over the weight parameters throughout the training phase.",
"The two interlinked methods significantly improve both the convergence rate and the final accuracy of ResBinNet compared to prior art.",
"Once the soft training phase is finished, we convert the weights to actual binary values (0,1).",
"Fine-tuning of the model is then performed in Hard Binarization phase using existing training algorithms (e.g. BinaryNets )) in few epochs (e.g. one epoch).",
"ResBinNet is designed to fulfill certain goals:",
"(i) It should enable reconfigurability for binary neural networks; in other words, the number of residual binary representatives should be adjustable to offer a trade-off between inference accuracy and computation time.(ii",
") The multi-level binarized features should be compatible with the XNOR multiplication approach proposed in the existing literature.(iii",
") ResBinNet should speed up the convergence rate of binarized CNNs. (iv",
") Current hardware accelerators for binary CNNs should be able to benefit from ResBinNet with minimum modification in their design. In",
"summary, the contributions of this paper are as follows:• Proposing residual binarization, a methodology for learning multi-level residual representations for each feature map in binary CNNs.• Introducing",
"temperature adjustment as a practical approach for gradual (soft) binarization of CNN weights.• Analyzing the",
"trade-off between accuracy and execution time of ResBinNet on a real hardware design.• Evaluating ResBinNet",
"convergence rate and accuracy on three datasets: MNIST, SVHN, and CIFAR-10.• Development of an open-source Application Program Interface (API) for ResBinNet 1 .The remainder of the paper",
"is organized as follows: In Section 2, we describe the residual binarization method for binarizing activations. Section 3 explains the temperature",
"adjustment technique for binarizing weights. In Section 4, we discuss how particular",
"ResBinNet operations (e.g. multi-level XNOR-popcount) can be efficiently implemented on existing hardware accelerators. Experiments are discussed in Section 5.",
"Finally, we discuss the related work and",
"conclusion in Sections 6 and 7.",
"This paper introduces ResBinNet, a novel reconfigurable binarization scheme which aims to improve the convergence rate and the final accuracy of binary CNNs.",
"The proposed training is twofold:",
"(i) In the first phase, called soft binarization, we introduce two distinct methodologies designed for binarizing weights and feature within CNNs, namely residual binarization, and temperature adjustment.",
"Residual binarization learns a multi-level representation for features of CNN to provide an arbitrary numerical precision during inference.",
"Temperature adjustment gradually imposes binarization constraints on the weights.",
"(ii) In the second phase, which we call hard binarization, the model is fine-tuned in few training epochs.",
"Our experiments demonstrate that the joint use of residual binarization and temperature adjustment improves the convergence rate and the accuracy of the binarized CNN.",
"We argue that ResBinNet methodology can be adopted by current CNN hardware accelerators as it requires minimal modification to existing binarized CNN solutions.",
"Developers can integrate the approaches proposed in this paper into their deep learning systems to provide users with a trade-off between application latency and inference accuracy."
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"Residual Binary Neural Networks significantly improve the convergence rate and inference accuracy of the binary neural networks."
] |
[
"In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely.\n",
"Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis.",
"Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalous if the reconstruction error exceeds some threshold.",
"In this paper, we proposed an unsupervised method based on subset scanning over autoencoder activations.",
"The contributions of our work are threefold.",
"First, we propose a novel method combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining.",
"Second, we provide the ability to inspect and visualize the set of anomalous nodes in the reconstruction error space that make a sample noised.",
"Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder.",
"We provide detection power results for several untargeted adversarial noise models under standard datasets.",
"Neural networks generate a large amount of activation data when processing an input.",
"This work applies anomalous pattern detection techniques on this activation data in order to determine if the input is anomalous.",
"Examples of an anomalous input can be noised samples by an adversary (Szegedy et al., 2013; Goodfellow et al., 2014; Kurakin et al., 2016a; Dalvi et al., 2004a) , human annotation errors (Klebanov et al., 2008) , etc.",
"The goal of anomalous pattern detection is to quantify, detect, and characterize the data that are generated by an alternative process.",
"Since anomalies are rare and come from diverse sources, it is not feasible to obtain labeled datasets of all possible anomalies/attacks.",
"If an observation deviates from the learned model, it is classified as an anomaly (Chandola et al., 2009) .",
"In real-world problems, large outliers and pervasive perturbations are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders (Beggel et al., 2019) due to reasons such as human annotation errors (Klebanov et al., 2008) and poisoning techniques (Dalvi et al., 2004b) .",
"Autoencoders differ from classical classifier networks such as Convolutional Neural Networks (CNNs) .",
"Autoencoders do not require labels because the expected output is the input data.",
"The autoencoder is trained to minimize the reconstruction error L(x, x ).",
"During the prediction step, anomaly detection can be performed by looking at the distribution of mean reconstruction error L(w, d(e(w))) when w ∈ X clean and L(w , d(e(w ))) when w ∈ X adv (Frosst et al., 2018 ).",
"An example of both, clean and noise reconstruction error distribution can be seen in Figure 4 (b).",
"Using this type of anomaly detection with autoencoders assumes that the autoencoder is properly trained with clean data.",
"Otherwise, this manifold can be used advantageously by training the autoencoder with corrupted samples that are mapped to clean samples.",
"As a result, the autoencoder will learn an underlying vector field that points in the direction of the manifold in which the clean samples lie.",
"Thus, upon the introduction of a perturbation, the magnitude of each arrow in the vector field will indicate the direction in which the data must be moved to map the sample to its clean representation (Sahay et al., 2019) .",
"Further detail on the autoencoder architecture and training setup for the experiments can be found in the Section A.4.",
"Subset scanning frames the detection problem as a search over subsets of data in order to find a subset that maximizes a scoring function F (S), typically a likelihood ratio.",
"Subset scanning exploits a property of these scoring functions that allow for efficient maximization over the exponentially large search space (Neill, 2012) .",
"In this paper, we show how subset scanning methods can enhance the anomaly detection power of autoencoders in an unsupervised manner and without a retraining step.",
"We treat this anomaly detection approach as a search for a subset of node activations that are higher than expected.",
"This is formally quantified as the subset with the highest score according to a non-parametric scan statistic.",
"The contributions of our work are threefold.",
"First, we propose a novel approach combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining.",
"Second, we provide the ability to identify and visualize the set of anomalous nodes in the reconstruction error space that make noised samples.",
"Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder.",
"Figure 1: Example of subset scanning score distributions across layers of an autoencoder for adversarial BIM noise = 0.01.",
"In the top of the graph we can see subset score distributions per nodes in a layer.",
"The distributions of subset scanning scores are shown in blue for clean images (C) (expected distribution), and in orange for noised samples A t .",
"Higher AUCs are expected when distributions are separated from each other and lower AUCs when they overlap.",
"The purple structure corresponds to convolutional layers at the Encoder, while the red structure corresponds to the convolution layers for the Decoder.",
"The computed AUC for the subset score distributions can be found in Table 1 .",
"The highest mutual information exchange with the adversarial input happens on the first layers (convolutional and maxpooling).",
"This is why the greatest divergence in both C and A t subset scores distributions is seen.",
"In the latent space, due to properties described in Section 4, the autoencoder abstracts basic representations of the images, losing subset scanning power due to the autoencoder mapping the new sample to the expected distribution.",
"This can be seen as an almost perfect overlap of distribution in conv 2d 7.",
"In this work, we proposed a novel unsupervised method for adversarial noise detection with off-theshelf autoencoders and subset scanning.",
"We have successfully demonstrated how subset scanning can be used to gain detection strength against multiple adversarial attacks on images across several datasets, without requiring any retraining or complex deep autoencoder network structures.",
"Furthermore, we tested subset scanning over the reconstruction error space and observed significant variations depending on the dataset, autoencoder architecture, and training setup.",
"We performed Figure 5 : Anomalous nodes visualization.",
"Overlap of anomalous nodes (white) and reconstruction error (darker blue) per sample.",
"(a) Noised samples with BIM.",
"We can observe that nodes outside the contour will make the sample be classified as noised.",
"(b) Whereas clean we expect the anomalous nodes will be along the contour of the figure.",
"preliminary experiments that yielded a relation between a decrease in the loss of the trained autoencoder and an increase in the detection power of subset scanning under the reconstruction error space.",
"Nonetheless, applying our method under this space provides introspection capabilities that allow us to identify the nodes or portions of the input image look anomalous.",
"Consequently, we are able to not only point out which image looks anomalous but also characterize the nodes that make the input a noised sample.",
"We also evaluated the performance of applying subset scanning over the autoencoder's activations.",
"We observed a consistent and high detection power results across noise attacks, datasets, autoencoders architectures and different noised training levels in the initial layers (Convolutional and MaxPooling layers).",
"Due to versatile properties of subset scanning under neural network activation analysis it may be used for several other studies, including unsupervised classification in the latent space of an autoencoder.",
"We would expect that same class images will identify as a subset of inputs (images) that have higher-than-expected activations (i.e. large number of low empirical p−values) at a subset of nodes.",
"Subset scanning applied to autoencoders activations is a novel, unsupervised anomaly detector that can be applied to any pre-trained, off-the-shelf neural network, previously only used in classifier neural networks such as CNNs and ResNet (Speakman et al., 2018) ."
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] | S1lLvyBtPB | true | [
"Unsupervised method to detect adversarial samples in autoencoder's activations and reconstruction error space"
] |
[
"Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion.",
"Conventional KGEs often suffer from limited knowledge representation, which causes less accuracy especially when training on sparse knowledge graphs.",
"To remedy this, we present Pretrain-KGEs, a training framework for learning better knowledgeable entity and relation embeddings, leveraging the abundant linguistic knowledge from pretrained language models.",
"Specifically, we propose a unified approach in which we first learn entity and relation representations via pretrained language models and use the representations to initialize entity and relation embeddings for training KGE models.",
"Our proposed method is model agnostic in the sense that it can be applied to any variant of KGE models.",
"Experimental results show that our method can consistently improve results and achieve state-of-the-art performance using different KGE models such as TransE and QuatE, across four benchmark KG datasets in link prediction and triplet classification tasks.",
"Knowledge graphs (KGs) constitute an effective access to world knowledge for a wide variety of NLP tasks, such as question-answering, entity linking and information retrieval.",
"A typical KG such as Freebase (Bollacker et al., 2008) and WordNet (Miller, 1995) consists of a set of triplets in the form of (h, r, t) with the head entity h and the tail entity t as nodes and relations r as edges in the graph.",
"A triplet represents the relation between two entities, e.g., (Steve Jobs, founded, Apple Inc.).",
"Despite their effectiveness, KGs in real applications suffer from incompleteness and there have been several attempts for knowledge graph completion among which knowledge graph embedding is one of prominent approaches.",
"Knowledge graph embedding (KGE) models have been designed extensively in recent years (Bordes et al., 2013; Ji et al., 2015; Lin et al., 2015; Sun et al., 2019; Ebisu and Ichise, 2018; Nickel et al., 2011; Kazemi and Poole, 2018; Trouillon et al., 2016; Zhang et al., 2019) .",
"The general methodology of these models is to model entities and relations in vector spaces based on a score function for triplets (h, r, t).",
"The score function measures the plausibility of each candidate triplet (h, r, t) compared to corrupted false triplets (h , r, t) or (h, r, t ).",
"However, traditional KGE models often suffer from limited knowledge representation due to the simply symbolic representation of entities and relations.",
"Some recent works take advantages of both fact triplets and textual description to enrich knowledge representation (Socher et al., 2013a; Xu et al., 2017; Xiao et al., 2017; Xie et al., 2016; , but without exploitation of contextual information of the textual descriptions.",
"Moreover, much of this research effort has been dedicated to developing novel architectures for knowledge representation without applications to KGE models.",
"Unlike many existing works which try to propose new architectures for KGEs or knowledge representation, we focus on model-agnostic pretraining technique for KGE models.",
"We present a unified training framework named as PretrainKGEs which consists of three phases: fine-tuning phase, initializing phase and training phase (see Fig. 1 ).",
"During the fine-tuning phase, we learn better knowledgeable entity and relation representations via pretrained language models using textual descriptions as input sequence.",
"Different from previous works incorporating textual information into knowledge representation, we use pretrained langauge models such as BERT (Devlin et al., 2019) to better understand textual description by making full use of syntactic and semantic information in large- scale corpora on which BERT is pretrained.",
"Thus, we enable to incorporate rich linguistic knowledge learned by BERT into entity and relation representations.",
"Then during the initializing phase, we use knowledgeable entity and relation representations to initialize entity and relation embeddings so that the initialized KGEs inherit the rich knowledge.",
"Finally, during the training phase, we train a KGE model the same way as a traditional KGE model to learn entity and relation embeddings.",
"Extensive experiments using six public KGE models across four benchmark KG datasets show that our proposed training framework can consistently improve results and achieve state-of-the-art performance in link prediction and triplet classification tasks.",
"Our contributions are as follows:",
"• We propose a model-agnostic training framework for learning knowledge graph embeddings by first learning knowledge representation via pretrained language models.",
"• Results on several benchmark datasets show that our method can improve results and achieve state-of-the-art performance over variants of knowledge graph embedding models in link prediction and triplet classification tasks.",
"• Further analysis demonstrates the effects of knowledge incorporation in our method and shows that our Pretrain-KGEs outperforms baselines especially in the case of fewer training triplets, low-frequency and the out-ofknowledge-base (OOKB) entities.",
"2 Background and Related Work",
"We present Pretrain-KGEs, a simple and efficient pretraining technique for knowledge graph embedding models.",
"Pretrain-KGEs is a general technique that can be applied to any KGE model.",
"It contributes to learn better knowledgeable entity and relation representations from pretrained language models, which are leveraged during the initializing and the training phases for a KGE model to learn entity and relation embeddings.",
"Through extensive experiments, we demonstrate state-of-the-art performances using this effective pretraining technique on various benchmark datasets.",
"Further, we verify the effectiveness of our method by demonstrating promising results in the case of fewer training triplets, infrequent and OOKB entities which are particularly hard to handle due to lack of knowledge representation.",
"We finally analyze the effects of knowledge incorporation by demonstrating the sensitivity of MR and MRR metrics and visualizing the process of knowledge incorporation.",
"A Detailed Implementation A.1",
"Implementation Our implementations of TransE (Bordes et al., 2013) , DistMult , ComplEx (Trouillon et al., 2016) , RotatE (Sun et al., 2019) , pRotatE (Sun et al., 2019) are based on the framework provided by Sun et al. (2019) 6 .",
"Our implementation of QuatE is based on on the framework provided by Zhang et al. (2019) 7 .",
"In fine-tuning phase, we adopt the following non-linear pointwise function σ(·):",
"x i e i ∈ F (where F can be real number filed R, complex number filed C or quaternion number ring H):",
"where x i ∈ R and e i is the K-dimension hypercomplex-value unit.",
"For instance, when K = 1, F = R; when K = 2, F = C, e 1 = i (the imaginary unit); when K = 4, F = H, e 1,2,3 = i, j, k (the quaternion units).",
"The score functions of baselines are listed in Table 4 ."
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"We propose to learn knowledgeable entity and relation representations from Bert for knowledge graph embeddings."
] |
[
"We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. ",
"This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs.",
"The sparse-matrix reified KB can be distributed across multiple GPUs, can scale to tens of millions of entities and facts, and is orders of magnitude faster than naive sparse-matrix implementations. ",
"The reified KB enables very simple end-to-end architectures to obtain competitive performance on several benchmarks representing two families of tasks: KB completion, and learning semantic parsers from denotations.",
"There has been much prior work on using neural networks to generalize the contents of a KB (e.g., (Xiong et al., 2017; Bordes et al., 2013; Dettmers et al., 2018) ), typically by constructing low-dimensional embeddings of the entities and relations in the KB, which are then used to score potential triples as plausible or implausible elements of the KB.",
"We consider here the related but different problem of incorporating a symbolic KB into a neural system, so as to inject knowledge from an existing KB directly into a neural model.",
"More precisely, we consider the problem of designing neural KB inference modules that are (1) fully differentiable, so that any loss based on their outputs can be backpropagated to their inputs; (2) accurate, in that they are faithful to the original semantics of the KB; (3) expressive, so they can perform non-trivial inferences; and (4) scalable, so that realistically large KBs can be incorporated into a neural model.",
"To motivate the goal of incorporating a symbolic KB into a neural network, consider the task of learning neural semantic parsers from denotations.",
"Many questions-e.g., what's the most recent movie that Quentin Tarantino directed?",
"or which nearby restaurants have vegetarian entrees and take reservations?-are",
"best answered by knowledge-based question-answering (KBQA) methods, where an answer is found by accessing a KB. Within",
"KBQA, a common approach is neural semantic parsing-i.e., using neural methods to translate a natural-language question into a structured query against the KB (e.g., (Zhong et al., 2017; Finegan-Dollak et al., 2018; Shaw et al., 2019) ), which is subsequently executed with a symbolic KB query engine. While",
"this approach can be effective, it requires training data pairing natural-language questions with structured queries, which is difficult to obtain. Hence",
"researchers have also considered learning semantic parsers from denotations (e.g., (Berant et al., 2013; Yih et al., 2015) ), where training data consists of pairs (q, A), where q is a natural-language question and A is the desired answer. Typically",
"A is a set of KB entities-e.g., if q is the first sample question above, A would be 1 the singleton set containing Once Upon a Time in Hollywood.",
"Learning semantic parsers from denotations is difficult because the end-to-end process to be learned includes a non-differentiable operation-i.e., reasoning with the symbolic KB that contains the answers.",
"To circumvent this difficulty, prior systems have used three different approaches.",
"Some have used heuristic search to infer structured queries from denotations (e.g., (Pasupat & Liang, 2016; Dasigi et al., 2019) ): this works in some cases but often an answer could be associated with many possible structured queries, introducing noise.",
"Others have supplemented gradient approaches 1 At the time of this writing.",
"x: an entity X: weighted set of entities x: vector encoding X NE: # entities in KB r: an relation R: weighted set of relations r: vector encoding R NR: # relations in KB Mr: matrix for r MR: weighted sum of Mr's, see Eq 1 follow(x, r): see Eq 2 NT : # triples in KB M subj , M obj , M rel : the reified KB, encoded as matrices mapping triple id to subject, object, and relation ids Table 1 : Summary of notation used in the paper.",
"(This excludes notation used in defining models for the KB completion and QA tasks of Section 3.) with reinforcement learning (e.g., (Misra et al., 2018) ).",
"Some systems have also \"neuralized\" KB reasoning, but to date only over small KBs: this approach is natural when answers are naturally constrained to depend on a small set of facts (e.g., a single table (Zhong et al., 2017; Gupta & Lewis, 2018) ), but more generally requires coupling a learner with some (non-differentiable) mechanism to retrieve an appropriate small question-dependent subset of the KB (e.g., (Sun et al., 2018; ).",
"In this paper, we introduce a novel scheme for incorporating reasoning on a large question-independent KB into a neural network, by representing a symbolic KB with an encoding called a sparse-matrix reified KB.",
"A sparse-matrix reified KB is very compact, can be distributed across multiple GPUs if necessary, and is well-suited to modern GPU architecture.",
"For KBs with many relations, a reified KB can be up to four orders of magnitude faster than alternative implementations (even alternatives based on sparse-matrix representations), and in our experiments we demonstrate scalability to a KB with over 13 million entities and nearly 44 million facts.",
"This new architectural component leads to radically simpler architectures for neural semantic parsing from denotations-architectures based on a single end-to-end differentiable process, rather than cascades of retrieval and neural processes.",
"We show that very simple instantiations of these architectures are still highly competitive with the state of the art for several benchmark tasks.",
"To our knowledge these models are the first fully end-to-end neural parsers from denotations that have been applied to these benchmark tasks.",
"We also demonstrate that these architectures scale to long chains of reasoning on synthetic tasks, and demonstrate similarly simple architectures for a second task, KB completion.",
"2 NEURAL REASONING WITH A SYMBOLIC KB 2.1 BACKGROUND KBs, entities, and relations.",
"A KB consists of entities and relations.",
"We use x to denote an entity and r to denote a relation.",
"Each entity has an integer index between 1 and N E , where N E is the number of entities in the KB, and we write x i for the entity that has index i.",
"A relation is a set of entity pairs, and represents a relationship between entities: for instance, if x i represents \"Quentin Tarantino\" and x j represents \"Pulp Fiction\" then (x i , x j ) would be an member of the relation director_of.",
"A relation r is a subset of {1, . . . , N E } × {1, . . . , N E }.",
"Finally a KB consists a set of relations and a set of entities.",
"Weighted sets as \"k-hot\" vectors.",
"Our differentiable operations are based on weighted sets, where each element x of weighted set X is associated with a non-negative real number.",
"It is convenient to define this weight to be zero for all x ∈ X while for x ∈ X, a weight less than 1 is a confidence that the set contains x, and weights more than 1 make X a multiset.",
"If all elements of X have weight 1, we say X is a hard set.",
"A weighted set X can be encoded as an entity-set vector x ∈ R N E , where the i-th component of x is the weight of x i in X. If X is a hard entity set, then this will be a \"k-hot\" vector, for k = |X|.",
"The set of indices of x with non-zero values is called the support of x.",
"Sets of relations, and relations as matrices Often we would like to reason about sets of relations 2 , so we also assume every relation r in a KB is associated with an entity and hence an integer index.",
"We write r k for the relation with index k, and we assume that relation entities are listed first in the index of entities, so the index k for r k is between 1 and N R , where N R is the number of relations in the KB.",
"We use R for a set of relations, e.g., R = {writer_of, director_of} might be such a set, and use r for a vector encoding of a set.",
"A relation r can be encoded as a relation matrix M r ∈ R N E ×N E , where the value for M r [i, j] is (in general) the weight of the assertion r(x i , x j ) in the KB.",
"In the experiments of this paper, all KB relations are hard sets, so M r [i, j] ∈ {0, 1}.",
"Sparse vs. dense matrices for relations.",
"Scalably representing a large KB requires careful consideration of the implementation.",
"One important issue is that for all but the smallest KBs, a relation matrix must be implemented using a sparse matrix data structure, as explicitly storing all N 2 E values is impractical.",
"For instance, consider a KB containing 10,000 movie entities and 100,000 person entities.",
"A relationship like writer_of would have only a few tens of thousands of facts, since most movies have only one or two writers, but a dense matrix would have more than 1 billion values.",
"We thus model relations as sparse matrices.",
"Let N r be the number of entity pairs in the relation r.",
"A common sparse matrix data structure is a sparse coordinate pair (COO) encoding: with a COO encoding, each KB fact requires storing only two integers and one float.",
"Our implementations are based on Tensorflow (Abadi et al., 2016) , which offers limited support for sparse matrices.",
"In particular, driven by the limitations of GPU architecture, Tensorflow only supports matrix multiplication between a sparse matrix COO and a dense matrix, but not between two sparse matrices, or between sparse higher-rank tensors and dense tensors.",
"Entity types.",
"It is often possible to easily group entities into disjoint sets by some notion of \"type\": for example, in a movie domain, all entities might be either of the type \"movie\", \"person\", or \"movie studio\".",
"It is straightforward to extend the formalism above to typed sets of entities, and doing this can lead to some useful optimizations.",
"We use these optimizations below where appropriate: in particular, one can assume that relation-set vectors r are of dimension N R , not N E , in the sections below.",
"The full formal extension of the definitions above to typed entities and relations is given in Appendix A.",
"We introduced here a novel way of representing a symbolic knowledge base (KB) called a sparsematrix reified KB.",
"This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs.",
"In a reified KB, all KB relations are represented with three sparse matrices, which can be distributed across multiple GPUs, and symbolic reasoning on realistic KBs with many relations is much faster than with naive implementations-more than four orders of magnitude faster on synthetic-data experiments compared to naive sparse-matrix implementations.",
"This new architectural component leads to radically simpler architectures for neural semantic parsing from denotations and KB completion-in particular, they make it possible to learn neural KBQA models in a completely end-to-end way, mapping from text to KB entity sets, for KBs with tens of millions of triples and entities and hundreds of relations."
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"A scalable differentiable neural module that implements reasoning on symbolic KBs."
] |
[
"We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters.",
"In the machine learning setting this brings CEM inside of the end-to-end learning pipeline in cases this has otherwise been impossible.",
"We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control.",
"In the control setting we show on the simulated cheetah and walker tasks that we can embed their optimal action sequences with DCEM and then use policy optimization to fine-tune components of the controller as a step towards combining model-based and model-free RL.",
"Recent work in the machine learning community has shown how optimization procedures can create new building-blocks for the end-to-end machine learning pipeline (Gould et al., 2016; Johnson et al., 2016; Domke, 2012; Metz et al., 2016; Finn et al., 2017; Belanger et al., 2017; Rusu et al., 2018; Srinivas et al., 2018; Amos et al., 2018) .",
"In this paper we focus on the setting of optimizing an unconstrained, non-convex, and continuous objective function f θ (x) : R n × Θ → R asx = arg min x f θ (x), where f is parameterized by θ ∈ Θ and has inputs x ∈ R n .",
"If it exists, some (sub-)derivative ∇ θx is useful in the machine learning setting to make the output of the optimization procedure end-to-end learnable.",
"For example, θ could parameterize a predictive model that is generating potential outcomes conditional on x happening that you want to optimize over.",
"End-to-end learning in these settings can be done by defining a loss function L on top ofx and taking gradient steps ∇ θ L. If f θ were convex this gradient is easy to analyze and compute when it exists and is unique (Gould et al., 2016; Johnson et al., 2016; .",
"Unfortunately analyzing and computing a \"derivative\" through the non-convex arg min here is not as easy and is challenging in theory and practice.",
"No such derivative may exist in theory, it might not be unique, and even if it uniquely exists, the numerical solver being used to compute the solution may not find a global or even local optimum of f .",
"One promising direction to sidestep these issues is to approximate the arg min operation with an explicit optimization procedure that is interpreted as just another compute graph and unrolled through.",
"This is most commonly done with gradient descent as in Domke (2012) ; Metz et al. (2016) ; Finn et al. (2017) ; Belanger et al. (2017) ; Rusu et al. (2018) ; Srinivas et al. (2018) ; Foerster et al. (2018) .",
"This approximation adds significant definition and structure to an otherwise extremely ill-defined desiderata at the cost of biasing the gradients and enabling the learning procedure to over-fit to the hyper-parameters of the optimization algorithm, such as the number of gradient steps or the learning rate.",
"In this paper we show that the Cross-Entropy Method (CEM) (De Boer et al., 2005 ) is a reasonable alternative to unrolling gradient descent for approximating the derivative through an unconstrained, non-convex, and continuous arg min.",
"CEM for optimization is a zeroth-order optimizer and works by generating a sequence of samples from the objective function.",
"We show a simple and computationally negligible way of making CEM differentiable that we call DCEM by using the smooth top-k operation from Amos et al. (2019) .",
"This also brings CEM into the end-to-end learning process in cases where there is otherwise a disconnection between the objective that is being learned and the objective that is induced by deploying CEM on top of those models.",
"We first quickly study DCEM in a simple non-convex energy-based learning setting for regression.",
"We contrast using unrolled gradient descent and DCEM for optimizing over a SPEN (Belanger & McCallum, 2016) .",
"We show that unrolling through gradient descent in this setting over-fits to the number of gradient steps taken and that DCEM generates a more reasonable energy surface.",
"Our main application focus is on using DCEM in the context of non-convex continuous control.",
"This setting is especially interesting as vanilla CEM is the state-of-the-art method for solving the control optimization problem with neural network transition dynamics as in Chua et al. (2018) ; Hafner et al. (2018) .",
"We show that DCEM is useful for embedding action sequences into a lower-dimensional space to make solving the control optimization process significantly less computationally and memory expensive.",
"This gives us a controller that induces a differentiable policy class parameterized by the model-based components.",
"We then use PPO (Schulman et al., 2017) to fine-tune the modelbased components, demonstrating that it is possible to use standard policy learning for model-based RL in addition to just doing maximum-likelihood fitting to observed trajectories.",
"We have laid the foundations for differentiating through the cross-entropy method and have brought CEM into the end-to-end learning pipeline.",
"Beyond further explorations in the energy-based learning and control contexts we showed here, DCEM can be used anywhere gradient descent is unrolled.",
"We find this especially promising for meta-learning, potentially building on LEO (Rusu et al., 2018) .",
"Inspired by DCEM, other more powerful sampling-based optimizers could be made differentiable in the same way, potentially optimizers that leverage gradient-based information in the inner optimization steps (Sekhon & Mebane, 1998; Theodorou et al., 2010; Stulp & Sigaud, 2012; Maheswaranathan et al., 2018) or by also learning the hyper-parameters of structured optimizers (Li & Malik, 2016; Volpp et al., 2019; Chen et al., 2017) ."
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"DCEM learns latent domains for optimization problems and helps bridge the gap between model-based and model-free RL --- we create a differentiable controller and fine-tune parts of it with PPO"
] |
[
"We propose a new framework for entity and event extraction based on generative adversarial imitation learning -- an inverse reinforcement learning method using generative adversarial network (GAN).",
"We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse.",
"We utilize discriminators to estimate proper rewards according to the difference between the labels committed by ground-truth (expert) and the extractor (agent). ",
"Experiments also demonstrate that the proposed framework outperforms state-of-the-art methods.",
"Event extraction (EE) is a crucial information extraction (IE) task that focuses on extracting structured information (i.e., a structure of event trigger and arguments, \"what is happening\", and \"who or what is involved \") from unstructured texts.",
"In most recent five years, many event extraction approaches have brought forth encouraging results by retrieving additional related text documents BID18 , introducing rich features of multiple categories [Li et al., 2013 BID26 , incorporating relevant information within or beyond context BID23 , Judea and Strube, 2016 BID24 BID7 and adopting neural network frameworks BID4 , Nguyen and Grishman, 2015 BID8 , Nguyen et al., 2016 BID8 , Nguyen and Grishman, 2018 BID17 , Huang et al., 2018 BID13 BID27 .There",
"are still challenging cases: for example, in the following sentences: \"Masih's alleged comments of blasphemy are punishable by death under Pakistan Penal Code\" and \"Scott is charged with first-degree homicide for the death of an infant.\", the word death can trigger an Execute event in the former sentence and a Die event in the latter one. With",
"similar local information (word embeddings) or contextual features (both sentences include legal events), supervised models pursue the probability distribution which resembles that in the training set (in ACE2005 data, we have overwhelmingly more Die annotation on death than Execute), and will label both as Die event, causing error in the former instance.Such mistake is due to the lack of a mechanism that explicitly deals with wrong and confusing labels. Many",
"multi-classification approaches utilize cross-entropy loss, which aims at boosting the probability of the correct labels. Many",
"approaches -including AdaBoost which focuses weights on difficult cases -usually treat wrong labels equally and merely inhibits them indirectly. Models",
"are trained to capture features and weights to pursue correct labels, but will become vulnerable and unable to avoid mistakes when facing ambiguous instances, where the probabilities of the confusing and wrong labels are not sufficiently \"suppressed\". Therefore",
", exploring information from wrong labels is a key to make the models robust.In this paper, we propose a dynamic mechanism -inverse reinforcement learning -to directly assess correct and wrong labels on instances in entity and event extraction. We assign",
"explicit scores on cases -or rewards in terms of Reinforcement Learning (RL). We adopt",
"discriminators from generative adversarial networks (GAN) to estimate the reward values. Discriminators",
"ensures the highest reward for ground-truth (expert) and the extractor attempts to imitate the expert by pursuing highest rewards. For challenging",
"cases, if the extractor continues selecting wrong labels, the GAN keeps expanding the margins between rewards for ground-truth labels and (wrong) extractor labels and eventually deviates the extractor from wrong labels.The main contributions of this paper can be summarized as follows: • We apply reinforcement learning framework to event extraction tasks, and the proposed framework is an end-to-end and pipelined approach that extracts entities and event triggers and determines the argument roles for detected entities.• With inverse reinforcement",
"learning propelled by GAN, we demonstrate that a dynamic reward function ensures more optimal performance in a complicated RL task.",
"In this paper, we propose an end-to-end entity and event extraction framework based on inverse reinforcement learning.",
"Experiments have demonstrated that the performance benefits from dynamic reward values estimated from discriminators in GAN, and we also demonstrate the performance of recent embedding work in the experiments.",
"In the future, besides releasing the source code, we also plan to further visualize the reward values and attempt to interpret these rewards so that researchers and event extraction system developers are able to better understand and explore the algorithm and remaining challenges.",
"Our future work also includes using cutting edge approaches such as BERT BID6 , and exploring joint model in order to alleviate impact from upstream errors in current pipelined framework."
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"We use dynamic rewards to train event extractors."
] |
[
"Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.",
"To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale.",
"We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick\", allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input.",
"Our modifications lead to models which set the new state of the art in class-conditional image synthesis.",
"When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Frechet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65.",
"Figure 1: Class-conditional samples generated by our model.The state of generative image modeling has advanced dramatically in recent years, with Generative Adversarial Networks (GANs, Goodfellow et al. (2014) ) at the forefront of efforts to generate highfidelity, diverse images with models learned directly from data.",
"GAN training is dynamic, and sensitive to nearly every aspect of its setup (from optimization parameters to model architecture), but a torrent of research has yielded empirical and theoretical insights enabling stable training in a variety of settings.",
"Despite this progress, the current state of the art in conditional ImageNet modeling (Zhang et al., 2018) achieves an Inception Score (Salimans et al., 2016) of 52.5, compared to 233 for real data.In this work, we set out to close the gap in fidelity and variety between images generated by GANs and real-world images from the ImageNet dataset.",
"We make the following three contributions towards this goal:• We demonstrate that GANs benefit dramatically from scaling, and train models with two to four times as many parameters and eight times the batch size compared to prior art.",
"We introduce two simple, general architectural changes that improve scalability, and modify a regularization scheme to improve conditioning, demonstrably boosting performance.•",
"As a side effect of our modifications, our models become amenable to the \"truncation trick,\" a simple sampling technique that allows explicit, fine-grained control of the tradeoff between sample variety and fidelity.•",
"We discover instabilities specific to large scale GANs, and characterize them empirically. Leveraging",
"insights from this analysis, we demonstrate that a combination of novel and existing techniques can reduce these instabilities, but complete training stability can only be achieved at a dramatic cost to performance.Our modifications substantially improve class-conditional GANs. When trained",
"on ImageNet at 128×128 resolution, our models (BigGANs) improve the state-of-the-art Inception Score (IS) and Fréchet Inception Distance (FID) from 52.52 and 18.65 to 166.5 and 7.4 respectively. We also successfully",
"train BigGANs on ImageNet at 256×256 and 512×512 resolution, and achieve IS and FID of 232.5 and 8.1 at 256×256 and IS and FID of 241.5 and 11.5 at 512×512. Finally, we train our",
"models on an even larger dataset -JFT-300M -and demonstrate that our design choices transfer well from ImageNet. Code and weights for",
"our pretrained generators are publicly available 1 .",
"We have demonstrated that Generative Adversarial Networks trained to model natural images of multiple categories highly benefit from scaling up, both in terms of fidelity and variety of the generated samples.",
"As a result, our models set a new level of performance among ImageNet GAN models, improving on the state of the art by a large margin.",
"We have also presented an analysis of the training behavior of large scale GANs, characterized their stability in terms of the singular values of their weights, and discussed the interplay between stability and performance.",
"In this section, we present and discuss additional investigations into the stability of our models, expanding upon the discussion in Section 4."
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"GANs benefit from scaling up."
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[
"In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation.",
"Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks.",
"Furthermore, Ben-David et al. (2010) provide an upper bound for target error when transferring the knowledge, which can be summarized as minimizing the source error and distance between marginal distributions simultaneously.",
"However, common methods based on the theory usually ignore the joint error such that samples from different classes might be mixed together when matching marginal distribution.",
"And in such case, no matter how we minimize the marginal discrepancy, the target error is not bounded due to an increasing joint error.",
"To address this problem, we propose a general upper bound taking joint error into account, such that the undesirable case can be properly penalized.",
"In addition, we utilize constrained hypothesis space to further formalize a tighter bound as well as a novel cross margin discrepancy to measure the dissimilarity between hypotheses which alleviates instability during adversarial learning.",
"Extensive empirical evidence shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation benchmarks.",
"The advent of deep convolutional neural networks (Krizhevsky et al., 2012) brings visual learning into a new era.",
"However, the performance heavily relies on the abundance of data annotated with ground-truth labels.",
"Since traditional machine learning assumes a model is trained and verified in a fixed distribution (single domain), where generalization performance is guaranteed by VC theory (N. Vapnik, 2000) , thus it cannot always be applied to real-world problem directly.",
"Take image classification task as an example, a number of factors, such as the change of light, noise, angle in which the image is pictured, and different types of sensors, can lead to a domain-shift thus harm the performance when predicting on test data.",
"Therefore, in many practical cases, we wish that a model trained in one or more source domains is also applicable to another domain.",
"As a solution, domain adaptation (DA) aims to transfer the knowledge learned from a source distribution, which is typically fully labeled into a different (but related) target distribution.",
"This work focus on the most challenging case, i.e, unsupervised domain adaptation (UDA), where no target label is available.",
"Ben-David et al. (2010) suggests that target error can be minimized by bounding the error of a model on the source data, the discrepancy between distributions of the two domains, and a small optimal joint error.",
"Owing to the strong representation power of deep neural nets, many researchers focus on learning domain-invariant features such that the discrepancy of two feature spaces can be minimized.",
"For aligning feature distributions across domains, mainly two strategies have been substantially explored.",
"The first one is bridging the distributions by matching all their statistics (Long et al., 2015; Pan et al., 2009) .",
"The second strategy is using adversarial learning (Goodfellow et al., 2014) to build a minimax game between domain discriminator and feature extractor, where a domain discriminator is trained to distinguish the source from the target while the feature extractor is learned to confuse it simultaneously (Ganin & Lempitsky, 2015; Ganin et al., 2016; Tzeng et al., 2017) .",
"In spite of the remarkable empirical results accomplished by feature distribution matching schemes, they still suffer from a major limitation: the joint distributions of feature spaces and categories are not well aligned across data domains.",
"As is reported in Ganin et al. (2016) , such methods fail to generalize in certain closely related source/target pairs, e.g., digit classification adaptation from MNIST to SVHN.",
"One potential reason is when matching marginal distributions of source and target domains, samples from different classes can be mixed together, where the joint error becomes nonnegligible since no hypothesis can classify source and target at the same time.",
"This work aims to address the above problem by incorporating joint error to formalize an optimizable upper bound such that the undesired overlap due to a wrong match can be properly penalized.",
"We evaluate our proposal on several different classification tasks.",
"In some experimental settings, our method outperforms other methods by a large margin.",
"The contributions of this work can be summarized as follows:",
"· We propose a novel upper bound taking joint error into account and theoretically prove that our proposal can reduce to several other methods under certain simplifications.",
"· We construct a constrained hypothesis space such that a much tighter bound can be obtained during optimization.",
"· We adopt a novel measurement, namely cross margin discrepancy, for the dissimilarity of two hypotheses on certain domain to alleviate the instability during adversarial learning and provide reliable performance.",
"In this work, we propose a general upper bound that takes the joint error into account.",
"Then we further pursuit a tighter bound with reasonable constraint on the hypothesis space.",
"Additionally, we adopt a novel cross domain discrepancy for dissimilarity measurement which alleviates the instability during adversarial learning.",
"Extensive empirical evidence shows that learning an invariant representation is not enough to guarantee a good generalization in the target domain, as the joint error matters especially when the domain shift is huge.",
"We believe our results take an important step towards understanding unsupervised domain adaptation, and also stimulate future work on the design of stronger adaptation algorithms that manage to align conditional distributions without using pseudo-labels from the target domain.",
"layer and a 0.5 rate of dropout is conducted.",
"Nesterov accelerated gradient is used for optimization with a mini-batch size of 32 and an initial learning rate of 10 −3 which decays exponentially.",
"As for the hyper-parameter, we test for γ = {0.1, 0.5, 0.9, 1} and η = {0, 0.5, 0.8, 0.9}.",
"For a direct comparison, we report the accuracy after 10 epochs."
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] | rkerLaVtDr | true | [
"joint error matters for unsupervised domain adaptation especially when the domain shift is huge"
] |
[
"A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model.",
"To address this issue, in this paper, we develop knowledge flow which moves ‘knowledge’ from multiple deep nets, referred to as teachers, to a new deep net model, called the student.",
"The structure of the teachers and the student can differ arbitrarily and they can be trained on entirely different tasks with different output spaces too.",
"Upon training with knowledge flow the student is independent of the teachers.",
"We demonstrate our approach on a variety of supervised and reinforcement learning tasks, outperforming fine-tuning and other ‘knowledge exchange’ methods.\n\n",
"Research communities have amassed a sizable number of deep net architectures for different tasks, and new ones are added almost daily.",
"Some of those architectures are trained from scratch while others are fine-tuned, i.e., before training, their weights are initialized using a structurally similar deep net which was trained on different data.Beyond fine-tuning, particularly in reinforcement learning, teachers have also been considered in one way or another by BID23 ; BID6 ; BID30 ; BID13 ; BID0 BID21 ; BID2 ; BID26 ; BID20 .",
"For instance, progressive neural net BID23 keeps multiple teachers during both training and inference, and learns to extract useful features from the teachers for a new target task.",
"PathNet BID6 uses genetic algorithms to choose pathways from a giant network for learning new tasks.",
"'Growing a Brain'",
"BID30 fine-tunes a neural network while growing the network's capacity (wider or deeper layers).",
"Actor-mimic BID20 pre-trains a big model on multiple source tasks, then the big model is used as a weight initialization for a new model which will be trained on a new target task.",
"Knowledge distillation BID9 distills knowledge from a large ensemble of models to a smaller student model.",
"However, all the aforementioned techniques have limitations.",
"For example, progressive neural net models BID23 grow with the number of teachers.",
"This large number of parameters limits the number of teachers a progressive neural net can handle, and largely increases the training and testing time.",
"In PathNet BID6 , searching over a big network for pathways is computationally intensive.",
"For fine-tuning based methods such as 'Growing a Brain' BID30 and actor-mimic BID20 , only one pretrained model can be used at a time.",
"Hence, their performance heavily relies on the chosen pretrained model.To address these shortcomings, we develop knowledge flow which moves 'knowledge' of multiple teachers when training a student.",
"Irrespective of how many teachers we use, the student is guaranteed to become independent at the final stage of training and the size of the resulting student net remains constant.",
"In addition, our framework makes no restrictions on the deep net size of the teacher and student, which provides flexibility in choosing teacher models.",
"Importantly, our approach is applicable to a variety of tasks from reinforcement learning to fully-supervised training.We evaluate knowledge flow on a variety of tasks from reinforcement learning to fully-supervised learning.",
"In particular, we follow BID23 ; BID6 and compare on the same ∞ k=0 γ k r t+k , where γ is the discount factor.",
"The expected future reward when observing state x and when following policy π θπ is defined as V π θπ (x t ) = E τ ∼π θπ [R t |x t ], where τ = {(x t , a t , r t ), (x t+1 , a t+1 , r t+1 ), .",
". .} is a trajectory generated by following π θπ from state x t .The goal of reinforcement learning is to find a policy that maximizes the expected future reward from each state x t . Without loss of generality, in this paper, we follow the asynchronous advantage actor-critic (A3C) formulation BID17 . In A3C, the policy mapping π θπ (x) = arg max a∈Aπθπ (a|x) is obtained from a probability distribution over states, wherê π θπ (a|x) is modeled by a deep net with parameters θ π . The value function is also approximated by a deep net V θv (x), having parameters θ v .To optimize the policy parameters θ π given a state x t , a loss function based on a scaled negative log-likelihood and a negative entropy regularizer is common: DISPLAYFORM0 [− logπ θπ (a t |x t )(R t − V θv (x t )) − βH(π θπ (·|x t ))] .Hereby, R t = k−1 i=0 γ i r t+i + γ k V θv (x t+k ) is the empirical k-step return obtained when starting in state x t , and |τ | is the length of the trajectory τ generated by following π θπ . The scalar β ≥ 0 is a user-specified constant, and H(π θπ (·|x t )) is the entropy function, which encourages exploration by favoring a uniform probability distributionπ θπ (a|x). To optimize the value function V θv , it is common to use the squared loss DISPLAYFORM1 By minimizing the empirical expectation of τ π (θ π ) and τ v (θ v ), i.e., by addressing DISPLAYFORM2 alternatingly, we learn a policy and a value function that maximize expected return.",
"We developed a general knowledge flow approach that permits to train a deep net from any number of teachers.",
"We showed results for reinforcement learning and supervised learning, demonstrating improvements compared to training from scratch and to fine-tuning.",
"In the future we plan to learn when to use which teacher and how to actively swap teachers during training of a student.",
"BID9 to distill knowledge from a larger model (teacher) to a smaller model (student).",
"The student models have 50% -5% parameters of the teacher models.",
"Following their setup, we conduct experiments on MNIST, MNIST with digit '3' missing in the training set, CIFAR-100, and ImageNet.",
"For MNIST and MNIST with digit '3' missing, following KD, the teacher model is an MLP with two hidden layers of 1200 hidden units, and the student model is an MLP with two hidden layers of 800 hidden units.",
"For CIFAR-100, we use the model from Chen FORMULA2 as teacher model.",
"The student model follows the structure of the teacher, but the number of output channels of each convolutional layer is halved.",
"For ImageNet, the teacher model is a 50-layer ResNet BID8 , and the student model is a 18-layer ResNet.",
"The test error of the distilled student model are summarize in TAB4 .",
"Our framework has consistently better performance than KD, because the student model in our framework benefits not only from the output layer behavior of the teacher but also from intermediate layer representations of the teacher."
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] | BJeOioA9Y7 | true | [
"‘Knowledge Flow’ trains a deep net (student) by injecting information from multiple nets (teachers). The student is independent upon training and performs very well on learned tasks irrespective of the setting (reinforcement or supervised learning)."
] |
[
"Despite the impressive performance of deep neural networks (DNNs) on numerous learning tasks, they still exhibit uncouth behaviours.",
"One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks.",
"Such a nuisance has strengthened the line of research around developing and training noise-robust networks.",
"In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input.",
"We provide an efficient and simple approach to approximate such a regularizer for arbitrarily deep networks.",
"This is done by leveraging the analytic expression of the output mean of a shallow neural network, avoiding the need for memory and computation expensive data augmentation.",
"We conduct extensive experiments on LeNet and AlexNet on various datasets including MNIST, CIFAR10, and CIFAR100 to demonstrate the effectiveness of our proposed regularizer.",
"In particular, we show that networks that are trained with the proposed regularizer benefit from a boost in robustness against Gaussian noise to an equivalent amount of performing 3-21 folds of noisy data augmentation.",
"Moreover, we empirically show on several architectures and datasets that improving robustness against Gaussian noise, by using the new regularizer, can improve the overall robustness against 6 other types of attacks by two orders of magnitude.",
"Deep neural networks (DNNs) have emerged as generic models that can be trained to perform impressively well in a variety of learning tasks ranging from object recognition (He et al., 2016) and semantic segmentation (Long et al., 2015) to speech recognition and bioinformatics (Angermueller et al., 2016) .",
"Despite their increasing popularity, flexibility, generality, and performance, DNNs have been recently shown to be quite susceptible to small imperceptible input noise (Szegedy et al., 2014; Moosavi-Dezfooli et al., 2016; Goodfellow et al., 2015) .",
"Such analysis gives a clear indication that even state-of-the-art DNNs may lack robustness.",
"Consequently, there has been an ever-growing interest in the machine learning community to study this uncanny behaviour.",
"In particular, the work of (Goodfellow et al., 2015; Moosavi-Dezfooli et al., 2016) demonstrates that there are systematic approaches to constructing adversarial attacks that result in misclassification errors with high probability.",
"Even more peculiarly, some noise perturbations seem to be doubly agnostic (Moosavi-Dezfooli et al., 2017) , i.e. there exist deterministic perturbations that can result in misclassification errors with high probability when applied to different networks, irrespective of the input (denoted network and input agnostic).",
"Understanding this degradation in performance under adversarial attacks is of tremendous importance, especially for real-world DNN deployment, e.g. self-driving cars/drones and equipment for the visually impaired.",
"A standard and popular means to alleviate this nuisance is noisy data augmentation in training, i.e. a DNN is exposed to noisy input images during training so as to bolster its robustness during inference.",
"Several works have demonstrated that DNNs can in fact benefit from such augmentation (Moosavi-Dezfooli et al., 2016; Goodfellow et al., 2015) .",
"However, data augmentation in general might not be sufficient for two reasons.",
"(1) Particularly with high-dimensional input noise, the amount of data augmentation necessary to sufficiently capture the noise space will be very large, which will increase training time.",
"(2) Data augmentation with high energy noise can negatively impact the performance on noise-free test examples.",
"This can be explained by the fundamental trade-off between accuracy and robustness (Tsipras et al., 2018; Boopathy et al., 2019) .",
"It can also arise from the fact that augmentation forces the DNN to have the same prediction for two vastly different versions of the same input, noise-free and a substantially corrupted version.",
"Addressing the sensitivity problem of deep neural networks to adversarial perturbation is of great importance to the machine learning community.",
"However, building robust classifiers against this noises is computationally expensive, as it is generally done through the means of data augmentation.",
"We propose a generic lightweight analytic regularizer, which can be applied to any deep neural network with a ReLU activation after the first affine layer.",
"It is designed to increase the robustness of the trained models under additive Gaussian noise.",
"We demonstrate this with multiple architectures and datasets and show that it outperforms data augmentation without observing any noisy examples.",
"A EXPERIMENTAL SETUP AND DETAILS.",
"All experiments, are conducted using PyTorch version 0.4.1 Paszke et al. (2017) .",
"All hyperparameters are fixed and Table 2 we report the setup for the two optimizers.",
"In particular, we use the Adam optimizaer Kingma & Ba (2015) with β 1 = 0.9, β 2 = 0.999, = 10 −8 with amsgrad set to False.",
"The second optimizer is SGD Loshchilov & Hutter (2017) with momentum=0.9, dampening=0, with Nesterov acceleration.",
"In each experiment, we randomly split the training dataset into 10% validation and 90% training and monitor the validation loss after each epoch.",
"If validation loss did not improve for lr patience epochs, we reduce the learning rate by multiplying it by lr factor.",
"We start with an initial learning rate of lr initial.",
"The training is terminated only if the validation loss did not improve for loss patience number of epochs or if the training reached 100 epochs.",
"We report the results of the model with the best validation loss.",
"In particular, one can observe that with σ large than 0.7 the among of noise is severe even for the human level.",
"Training on such extreme noise levels will deem data augmentation to be difficult."
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] | B1xDq2EFDH | true | [
"An efficient estimate to the Gaussian first moment of DNNs as a regularizer to training robust networks."
] |
[
"Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur.",
"Existing neural architectures typically do not scale to the entire evidence, and hence, resort to selecting a single passage in the document (either via truncation or other means), and carefully searching for the answer within that passage.",
"However, in some cases, this strategy can be suboptimal, since by focusing on a specific passage, it becomes difficult to leverage multiple mentions of the same answer throughout the document.",
"In this work, we take a different approach by constructing lightweight models that are combined in a cascade to find the answer.",
"Each submodel consists only of feed-forward networks equipped with an attention mechanism, making it trivially parallelizable.",
"We show that our approach can scale to approximately an order of magnitude larger evidence documents and can aggregate information from multiple mentions of each answer candidate across the document.",
"Empirically, our approach achieves state-of-the-art performance on both the Wikipedia and web domains of the TriviaQA dataset, outperforming more complex, recurrent architectures.",
"Reading comprehension, the task of answering questions based on a set of one more documents, is a key challenge in natural language understanding.",
"While data-driven approaches for the task date back to BID11 , much of the recent progress can be attributed to new largescale datasets such as the CNN/Daily Mail Corpus BID8 , the Children's Book Test Corpus BID9 and the Stanford Question Answering Dataset (SQuAD) BID21 .",
"These datasets have driven a large body of neural approaches BID24 BID16 BID22 BID27 , inter alia) that build complex deep models typically driven by long short-term memory networks BID12 .",
"These models have given impressive results on SQuAD where the document consists of a single paragraph and the correct answer span is typically only present once.",
"However, they are computationally intensive and cannot scale to large evidence texts.",
"Such is the case in the recently released TriviaQA dataset BID14 , which provides as evidence, entire webpages or Wikipedia articles, for answering independently collected trivia-style questions.So far, progress on the TriviaQA dataset has leveraged existing approaches on the SQuAD dataset by truncating documents and focusing on the first 800 words BID14 BID18 .",
"This has the obvious limitation that the truncated document may not contain the evidence required to answer the question 1 .",
"Furthermore, in TriviaQA there is often useful evidence spread throughout the supporting documents.",
"This cannot be harnessed by approaches such as that greedily search for the best 1-2 sentences in a document.",
"For example, in Fig.1 the answer does not appear in the first 800 words.",
"The first occurrence of the answer string is not sufficient to answer the question.",
"The passage starting at token 4089 does contain all of the information required to infer the answer, but this inference requires us to resolve the two complex co-referential phrases in 'In the summer of that year they got married in a church'.",
"Access to other mentions of Krasner and Pollock and the year 1945 is important to answer this question.",
"Figure 1: Example from TriviaQA in which multiple mentions contain information that is useful in inferring the answer.",
"Only the italicized phrase completely answers the question (Krasner could have married multiple times) but contains complex coreference that is beyond the scope of current natural language processing.",
"The last phrase is more easy to interpret but it misses the clue provided by the year 1945.In this paper we present a novel cascaded approach to extractive question answering ( §3) that can accumulate evidence from an order of magnitude more text than previous approaches, and which achieves state-of-the-art performance on all tasks and metrics in the TriviaQA evaluation.",
"The model is split into three levels that consist of feed-forward networks applied to an embedding of the input.",
"The first level submodels use simple bag-of-embeddings representations of the question, a candidate answer span in the document, and the words surrounding the span (the context).",
"The second level submodel uses the representation built by the first level, along with an attention mechanism BID2 that aligns question words with words in the sentence that contains the candidate span.",
"Finally, for answer candidates that are mentioned multiple times in the evidence document, the third level submodel aggregates the mention level representations from the second level to build a single answer representation.",
"At inference time, predictions are made using the output of the third level classifier only.",
"However, in training, as opposed to using a single loss, all the classifiers are trained using the multi-loss framework of BID1 , with gradients flowing down from higher to lower submodels.",
"This separation into submodels and the multi-loss objective prevents adaptation between features BID10 .",
"This is particularly important in our case where the higher level, more complex submodels could subsume the weaker, lower level models c.f. BID1 .To",
"summarize, our novel contributions are• a non-recurrent architecture enabling processing of longer evidence texts consisting of simple submodels • the aggregation of evidence from multiple mentions of answer candidates at the representation level • the use of a multi-loss objective.Our experimental results ( §4) show that all the above are essential in helping our model achieve state-of-the-art performance. Since",
"we use only feed-forward networks along with fixed length window representations of the question, answer candidate, and answer context, the vast majority of computation required by our model is trivially parallelizable, and is about 45× faster in comparison to recurrent models.",
"We presented a 3-level cascaded model for TriviaQA reading comprehension.",
"Our approach, through the use of feed-forward networks and bag-of-embeddings representations, can handle longer evidence documents and aggregated information from multiple occurrences of answer spans throughout the document.",
"We achieved state-of-the-art performance on both Wikipedia and web domains, outperforming several complex recurrent architectures."
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] | HyRnez-RW | true | [
"We propose neural cascades, a simple and trivially parallelizable approach to reading comprehension, consisting only of feed-forward nets and attention that achieves state-of-the-art performance on the TriviaQA dataset."
] |
[
"Compressed forms of deep neural networks are essential in deploying large-scale\n",
"computational models on resource-constrained devices.",
"Contrary to analogous\n",
"domains where large-scale systems are build as a hierarchical repetition of small-\n",
"scale units, the current practice in Machine Learning largely relies on models with\n",
"non-repetitive components.",
"In the spirit of molecular composition with repeating\n",
"atoms, we advance the state-of-the-art in model compression by proposing Atomic\n",
"Compression Networks (ACNs), a novel architecture that is constructed by recursive\n",
"repetition of a small set of neurons.",
"In other words, the same neurons with the\n",
"same weights are stochastically re-positioned in subsequent layers of the network.\n",
"Empirical evidence suggests that ACNs achieve compression rates of up to three\n",
"orders of magnitudes compared to fine-tuned fully-connected neural networks (88×\n",
"to 1116× reduction) with only a fractional deterioration of classification accuracy\n",
"(0.15% to 5.33%).",
"Moreover our method can yield sub-linear model complexities\n",
"and permits learning deep ACNs with less parameters than a logistic regression\n",
"with no decline in classification accuracy.",
"The universe is composed of matter, a physical substance formed by the structural constellation of a plethora of unitary elements denoted as atoms.",
"The type of an atom eventually defines the respective chemical elements, while structural bonding between atoms yields molecules (the building blocks of matter and our universe).",
"In Machine Learning a neuron is the infinitesimal nucleus of intelligence (i.e. {atom, matter} ↔ {neuron, AI}), whose structural arrangement in layers produces complex intelligence models.",
"Surprisingly, in contrast to physical matter where molecules often reuse quasi-identical atoms (i.e. repeating carbon, hydrogen, etc.), neural networks do not share the same neurons across layers.",
"Instead, the neurons are parameterized through weights which are optimized independently for every neuron in every layer.",
"Inspired by nature, we propose a new paradigm for constructing deep neural networks as a recursive repetition of a fixed set of neurons.",
"Staying faithful to the analogy we name such models as Atomic Compression Networks (ACNs).",
"Extensive experimental results show that by repeating the same set of neurons, ACNs achieve unprecedented compression in terms of the total neural network parameters, with a minimal compromise on the prediction quality.",
"Deep neural networks (DNN) achieve state-of-the-art prediction performances on several domains like computer vision Tan & Le, 2019) and natural language processing (Vaswani et al., 2017; Gehring et al., 2017) .",
"Therefore, considerable research efforts are invested in adopting DNNs for mobile, embedded, or Internet of Things (IoT) devices (Kim et al., 2015) .",
"Yet, multiple technical issues related to restricted resources, w.r.t. computation and memory, prevent their straightforward application in this particular domain Samie et al., 2016; Mehta et al., 2018) .",
"Even though prior works investigate neural compression techniques like pruning or low-rank parameter factorization, they face fragility concerns regarding the tuning of hyperparameters and network architecture, besides struggling to balance the trade-off between compression and accuracy (Cheng et al., 2017) .",
"• a novel compression paradigm for neural networks composed of repeating neurons as the atomic network components and further motivated by function composition;",
"• compression rates of up to three orders of magnitudes compared to a cross-validated fullyconnected network on nine real-world vector datasets;",
"• first work to achieve sub-linear model complexities measured in the number of trained parameters compared to connected architectures on several computer vision tasks.",
"2 RELATED WORK",
"In this paper we presented Atomic Compression Networks (ACN), a new network architecture which recursively reuses neurons throughout the model.",
"We evaluate our model on nine vector and three image datasets where we achieve promising results regarding the compression rate and the loss in model accuracy.",
"In general ACNs achieve much tinier models with only a small to moderate decrease of accuracy compared to six other baselines.",
"For future work we plan to include skip connections in the architecture and to extend the idea to CNNs and the sharing of kernel parameters as well as for the FC layers.",
"Another interesting path of research is the combination of the ACN scheme with NAS methods to further optimize the efficiency and performance of the created architectures."
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] | S1xO4xHFvB | true | [
"We advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons."
] |
[
"Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.",
"However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possible futures.",
"Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally as in the case of pixel-level autoregressive models, or do not directly optimize the likelihood of the data.",
"To our knowledge, our work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions.",
"We describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative models offer a viable and competitive approach to generative modeling of video.",
"Exponential progress in the capabilities of computational hardware, paired with a relentless effort towards greater insights and better methods, has pushed the field of machine learning from relative obscurity into the mainstream.",
"Progress in the field has translated to improvements in various capabilities, such as classification of images (Krizhevsky et al., 2012) , machine translation (Vaswani et al., 2017) and super-human game-playing agents (Mnih et al., 2013; Silver et al., 2017) , among others.",
"However, the application of machine learning technology has been largely constrained to situations where large amounts of supervision is available, such as in image classification or machine translation, or where highly accurate simulations of the environment are available to the learning agent, such as in game-playing agents.",
"An appealing alternative to supervised learning is to utilize large unlabeled datasets, combined with predictive generative models.",
"In order for a complex generative model to be able to effectively predict future events, it must build up an internal representation of the world.",
"For example, a predictive generative model that can predict future frames in a video would need to model complex real-world phenomena, such as physical interactions.",
"This provides an appealing mechanism for building models that have a rich understanding of the physical world, without any labeled examples.",
"Videos of real-world interactions are plentiful and readily available, and a large generative model can be trained on large unlabeled datasets containing many video sequences, thereby learning about a wide range of real-world phenoma.",
"Such a model could be useful for learning representations for further downstream tasks (Mathieu et al., 2016) , or could even be used directly in applications where predicting the future enables effective decision making and control, such as robotics (Finn et al., 2016) .",
"A central challenge in video prediction is that the future is highly uncertain: a short sequence of observations of the present can imply many possible futures.",
"Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally (as in the case of pixel-level autoregressive models), or do not directly optimize the likelihood of the data.",
"In this paper, we study the problem of stochastic prediction, focusing specifically on the case of conditional video prediction: synthesizing raw RGB video frames conditioned on a short context of past observations (Ranzato et al., 2014; Srivastava et al., 2015; Vondrick et al., 2015; Xingjian et al., 2015; Boots et al., 2014) .",
"Specifically, we propose a new class of video prediction models that can provide exact likelihoods, generate diverse stochastic futures, and accurately synthesize realistic and high-quality video frames.",
"The main idea behind our approach is to extend flow-based generative models (Dinh et al., 2014; into the setting of conditional video prediction.",
"To our knowledge, flow-based models have been applied only to generation of non-temporal data, such as images (Kingma & Dhariwal, 2018) , and to audio sequences (Prenger et al., 2018) .",
"Conditional generation of videos presents its own unique challenges: the high dimensionality of video sequences makes them difficult to model as individual datapoints.",
"Instead, we learn a latent dynamical system model that predicts future values of the flow model's latent state.",
"This induces Markovian dynamics on the latent state of the system, replacing the standard unconditional prior distribution.",
"We further describe a practically applicable architecture for flow-based video prediction models, inspired by the Glow model for image generation (Kingma & Dhariwal, 2018) , which we call VideoFlow.",
"Our empirical results show that VideoFlow achieves results that are competitive with the state-ofthe-art in stochastic video prediction on the action-free BAIR dataset, with quantitative results that rival the best VAE-based models.",
"VideoFlow also produces excellent qualitative results, and avoids many of the common artifacts of models that use pixel-level mean-squared-error for training (e.g., blurry predictions), without the challenges associated with training adversarial models.",
"Compared to models based on pixel-level autoregressive prediction, VideoFlow achieves substantially faster test-time image synthesis 1 , making it much more practical for applications that require real-time prediction, such as robotic control .",
"Finally, since VideoFlow directly optimizes the likelihood of training videos, without relying on a variational lower bound, we can evaluate its performance directly in terms of likelihood values.",
"We describe a practically applicable architecture for flow-based video prediction models, inspired by the Glow model for image generation Kingma & Dhariwal (2018) , which we call VideoFlow.",
"We introduce a latent dynamical system model that predicts future values of the flow model's latent state replacing the standard unconditional prior distribution.",
"Our empirical results show that VideoFlow achieves results that are competitive with the state-of-the-art VAE models in stochastic video prediction.",
"Finally, our model optimizes log-likelihood directly making it easy to evaluate while achieving faster synthesis compared to pixel-level autoregressive video models, making our model suitable for practical purposes.",
"In future work, we plan to incorporate memory in VideoFlow to model arbitrary long-range dependencies and apply the model to challenging downstream tasks."
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] | rJgUfTEYvH | true | [
"We demonstrate that flow-based generative models offer a viable and competitive approach to generative modeling of video."
] |
[
"Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently.",
"Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics.",
"Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects.",
"A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function.",
"Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency.",
"We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well.",
"We verify our understanding through comparing\n",
"this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.",
"As a successful learning algorithm, stochastic gradient descent (SGD) was originally adopted for dealing with the computational bottleneck of training neural networks with large-scale datasets BID0 .",
"Its empirical efficiency and effectiveness have attracted lots of attention.",
"And thus, SGD and its variants have become standard workhorse for learning deep models.",
"Besides the aspect of empirical efficiency, recently, researchers started to analyze the optimization behaviors of SGD and its impacts on generalization.The optimization properties of SGD have been studied from various perspectives.",
"The convergence behaviors of SGD for simple one hidden layer neural networks were investigated in BID13 BID1 .",
"In non-convex settings, the characterization of how SGD escapes from stationary points, including saddle points and local minima, was analyzed in BID3 BID10 BID8 .On",
"the other hand, in the context of deep learning, researchers realized that the noise introduced by SGD impacts the generalization, thanks to the research on the phenomenon that training with a large batch could cause a significant drop of test accuracy BID11 . Particularly",
", several works attempted to investigate how the magnitude of the noise influences the generalization during the process of SGD optimization, including the batch size and learning rate BID7 BID5 BID2 BID9 . Another line",
"of research interpreted SGD from a Bayesian perspective. In BID14 BID2",
", SGD was interpreted as performing variational inference, where certain entropic regularization involves to prevent overfitting. And the work",
"BID21 tried to provide an understanding based on model evidence. These explanations",
"are compatible with the flat/sharp minima argument BID6 BID11 , since Bayesian inference tends to targeting the region with large probability mass, corresponding to the flat minima.However, when analyzing the optimization behavior and regularization effects of SGD, most of existing works only assume the noise covariance of SGD is constant or upper bounded by some constant, and what role the noise structure of stochastic gradient plays in optimization and generalization was rarely discussed in literature.In this work, we theoretically study a general form of gradient-based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. By investigating this",
"general dynamics, we analyze how the noise structure of SGD influences the escaping behavior from minima and its regularization effects. Several novel theoretical",
"results and empirical justifications are made.1. We derive a key indicator",
"to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator,",
"two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency;2. We further justify that",
"SGD in the context of deep neural networks satisfies these two conditions, and thus provide a plausible explanation why SGD can escape from sharp minima more efficiently, converging to flat minima with a higher probability. Moreover, these flat minima",
"typically generalize well according to various works BID6 BID11 BID16 BID22 . We also show that Langevin",
"dynamics with well tuned isotropic noise cannot beat SGD, which further confirms the importance of noise structure of SGD; 3. A large number of experiments",
"are designed systematically to justify our understanding on the behavior of the anisotropic diffusion of SGD. We compare SGD with full gradient",
"descent with different types of diffusion noise, including isotropic and positiondependent/independent noise. All these comparisons demonstrate",
"the effectiveness of anisotropic diffusion for good generalization in training deep networks.The remaining of the paper is organized as follows. In Section 2, we introduce the background",
"of SGD and a general form of optimization dynamics of interest. We then theoretically study the behaviors",
"of escaping from minima in Ornstein-Uhlenbeck process in Section 3, and establish two conditions for characterizing the noise structure that affects the escaping efficiency. In Section 4, we show that the noise of SGD",
"in the context of deep learning meets the two conditions, and thus explains its superior efficiency of escaping from sharp minima over other dynamics with isotropic noise. Various experiments are conducted for verifying",
"our understanding in Section 5, and we conclude the paper in Section 6.",
"We theoretically investigate a general optimization dynamics with unbiased noise, which unifies various existing optimization methods, including SGD.",
"We provide some novel results on the behaviors of escaping from minima and its regularization effects.",
"A novel indicator is derived for characterizing the escaping efficiency.",
"Based on this indicator, two conditions are constructed for showing what type of noise structure is superior to isotropic noise in term of escaping.",
"We then analyze the noise structure of SGD in deep learning and find that it indeed satisfies the two conditions, thus explaining the widely know observation that SGD can escape from sharp minima efficiently toward flat minina that generalize well.",
"Various experimental evidence supports our arguments on the behavior of SGD and its effects on generalization.",
"Our study also shows that isotropic noise helps little for escaping from sharp minima, due to the highly anisotropic nature of landscape.",
"This indicates that it is not sufficient to analyze SGD by treating it as an isotropic diffusion over landscape (Zhang et al., 2017; BID15 . A better understanding of this out-of-equilibrium behavior BID2 ) is on demand.Taking expectation with respect to the distribution of θ t , DISPLAYFORM0 for the expectation of Brownian motion is zero.",
"Thus the solution of EY t is, DISPLAYFORM1"
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] | H1M7soActX | true | [
"We provide theoretical and empirical analysis on the role of anisotropic noise introduced by stochastic gradient on escaping from minima."
] |
[
"Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning.",
"In this paper, we show how to make more effective use of the model by exploiting its differentiability.",
"We construct a policy optimization algorithm that uses the pathwise derivative of the learned model and policy across future timesteps.",
"Instabilities of learning across many timesteps are prevented by using a terminal value function, learning the policy in an actor-critic fashion.",
"Furthermore, we present a derivation on the monotonic improvement of our objective in terms of the gradient error in the model and value function.",
"We show that our approach",
"(i) is consistently more sample efficient than existing state-of-the-art model-based algorithms,",
"(ii) matches the asymptotic performance of model-free algorithms, and",
"(iii) scales to long horizons, a regime where typically past model-based approaches have struggled.",
"Model-based reinforcement learning (RL) offers the potential to be a general-purpose tool for learning complex policies while being sample efficient.",
"When learning in real-world physical systems, data collection can be an arduous process.",
"Contrary to model-free methods, model-based approaches are appealing due to their comparatively fast learning.",
"By first learning the dynamics of the system in a supervised learning way, it can exploit off-policy data.",
"Then, model-based methods use the model to derive controllers from it either parametric controllers (Luo et al., 2019; Buckman et al., 2018; Janner et al., 2019) or non-parametric controllers (Nagabandi et al., 2017; Chua et al., 2018) .",
"Current model-based methods learn with an order of magnitude less data than their model-free counterparts while achieving the same asymptotic convergence.",
"Tools like ensembles, probabilistic models, planning over shorter horizons, and meta-learning have been used to achieved such performance (Kurutach et al., 2018; Chua et al., 2018; .",
"However, the model usage in all of these methods is the same: simple data augmentation.",
"They use the learned model as a black-box simulator generating samples from it.",
"In high-dimensional environments or environments that require longer planning, substantial sampling is needed to provide meaningful signal for the policy.",
"Can we further exploit our learned models?",
"In this work, we propose to estimate the policy gradient by backpropagating its gradient through the model using the pathwise derivative estimator.",
"Since the learned model is differentiable, one can link together the model, reward function, and policy to obtain an analytic expression for the gradient of the returns with respect to the policy.",
"By computing the gradient in this manner, we obtain an expressive signal that allows rapid policy learning.",
"We avoid the instabilities that often result from back-propagating through long horizons by using a terminal Q-function.",
"This scheme fully exploits the learned model without harming the learning stability seen in previous approaches (Kurutach et al., 2018; .",
"The horizon at which we apply the terminal Q-function acts as a hyperparameter between model-free (when fully relying on the Q-function) and model-based (when using a longer horizon) of our algorithm.",
"The main contribution of this work is a model-based method that significantly reduces the sample complexity compared to state-of-the-art model-based algorithms (Janner et al., 2019; Buckman et al., 2018) .",
"For instance, we achieve a 10k return in the half-cheetah environment in just 50 trajectories.",
"We theoretically justify our optimization objective and derive the monotonic improvement of our learned policy in terms of the Q-function and the model error.",
"Furtermore, we experimentally analyze the theoretical derivations.",
"Finally, we pinpoint the importance of our objective by ablating all the components of our algorithm.",
"The results are reported in four model-based benchmarking environments Todorov et al., 2012) .",
"The low sample complexity and high performance of our method carry high promise towards learning directly on real robots.",
"In this work, we present model-augmented actor-critic, MAAC, a reinforcement learning algorithm that makes use of a learned model by using the pathwise derivative across future timesteps.",
"We prevent instabilities arisen from backpropagation through time by the means of a terminal value function.",
"The objective is theoretically analyzed in terms of the model and value error, and we derive a policy improvement expression with respect to those terms.",
"Our algorithm that builds on top of MAAC is able to achieve superior performance and sample efficiency than state-of-the-art model-based and model-free reinforcement learning algorithms.",
"For future work, it would be enticing to deploy the presented algorithm on a real-robotic agent.",
"Then, the error in the gradient in the previous term is bounded by",
"In order to bound the model term we need first to bound the rewards since",
"Similar to the previous bounds, we can bound now each reward term by",
"With this result we can bound the total error in models",
"Then, the gradient error has the form",
"A.2",
"PROOF OF LEMMA 4.2",
"The total variation distance can be bounded by the KL-divergence using the Pinsker's inequality",
"Then if we assume third order smoothness on our policy, by the Fisher information metric theorem then",
"Given that θ −θ 2 = α ∇ θ J π − ∇ θĴπ 2 , for a small enough step the following inequality holds Given the bound on the total variation distance, we can now make use of the monotonic improvement theorem to establish an improvement bound in terms of the gradient error.",
"Let J π (θ) and J π (θ) be the expected return of the policy π θ and πθ under the true dynamics.",
"Let ρ andρ be the discounted state marginal for the policy π θ and πθ, respectively Then, combining the results from Lemma 4.2 we obtain the desired bound."
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"Policy gradient through backpropagation through time using learned models and Q-functions. SOTA results in reinforcement learning benchmark environments."
] |
[
"Meta-learning algorithms learn to acquire new tasks more quickly from past experience.",
"In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks.",
"The performance of meta-learning algorithms depends on the tasks available for meta-training: in the same way that supervised learning generalizes best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks.",
"In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design.",
"If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated.",
"In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning.",
"We motivate and describe a general recipe for unsupervised meta-reinforcement learning, and present an instantiation of this approach.",
"Our conceptual and theoretical contributions consist of formulating the unsupervised meta-reinforcement learning problem and describing how task proposals based on mutual information can in principle be used to train optimal meta-learners.",
"Our experimental results indicate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design and significantly exceeds the performance of learning from scratch.",
"Reusing past experience for faster learning of new tasks is a key challenge for machine learning.",
"Meta-learning methods achieve this by using past experience to explicitly optimize for rapid adaptation (Mishra et al., 2017; Snell et al., 2017; Schmidhuber, 1987; Finn et al., 2017a; Wang et al., 2016; Al-Shedivat et al., 2017) .",
"In the context of reinforcement learning (RL), meta-reinforcement learning (meta-RL) algorithms can learn to solve new RL tasks more quickly through experience on past tasks (Duan et al., 2016b; Finn et al., 2017a) .",
"Typical meta-RL algorithms assume the ability to sample from a pre-specified task distribution, and these algorithms learn to solve new tasks drawn from this distribution very quickly.",
"However, specifying a task distribution is tedious and requires a significant amount of supervision (Finn et al., 2017b; Duan et al., 2016b ) that may be difficult to provide for large, real-world problem settings.",
"The performance of meta-learning algorithms critically depends on the meta-training task distribution, and meta-learning algorithms generalize best to new tasks which are drawn from the same distribution as the meta-training tasks .",
"In effect, meta-RL offloads much of the design burden from algorithm design to designing a sufficiently broad and relevant distribution of meta-training tasks.",
"While this offloading helps in acquiring representations for fast adaptation to the specified task distribution, specifying this is often tedious and challenging.",
"A natural question is whether we can do away with manual task design and develop meta-RL algorithms that learn only from unsupervised environment interaction.",
"In this paper, we take an initial step toward the formalization and design of such methods.",
"Our goal is to automate the meta-training process by removing the need for hand-designed metatraining tasks.",
"To that end, we introduce unsupervised meta-RL: meta-learning from a task distribution that is acquired automatically, rather than requiring manual design of the meta-training tasks.",
"Unsupervised meta-RL methods must solve two difficult problems together: meta-RL with broad task distributions, and unsupervised exploration for proposing a wide variety of tasks for meta-learning.",
"Since the assumptions of our method differ fundamentally from prior meta-RL methods (we do not assume access to hand-specified meta-training tasks that use human-specified reward functions), the best points of comparison for our approach are learning meta-test tasks entirely from scratch with conventional RL algorithms.",
"Our method can also be thought of as automatically acquiring an environment-specific learning procedure for deep neural network policies, somewhat related to data-driven initialization procedures explored in supervised learning (Krähenbühl et al., 2015; Hsu et al., 2018) .",
"The primary contributions of our work are to propose a framework for unsupervised meta-RL; to sketch out a family of unsupervised meta-RL algorithms; to provide a theoretical derivation that allows us to reason about the optimality of unsupervised meta-RL methods in terms of mutual information objectives; and to describe an instantiation of an algorithm from this family that builds on a recently proposed procedure for unsupervised exploration and modelagnostic meta-learning (MAML) (Finn et al., 2017a) .",
"In addition to our theoretical derivations, we provide an empirical evaluation that studies the performance of two variants of our approach on simulated control tasks.",
"Our experimental evaluation shows that, for a variety of tasks, unsupervised meta-RL can effectively acquire RL procedures that perform significantly better than standard RL methods that learn from scratch, without requiring additional task knowledge.",
"We presented an unsupervised approach to meta-RL, where meta-learning is used to acquire an efficient RL procedure without requiring hand-specified task distributions for meta-training.",
"This approach accelerates RL without relying on the manual supervision required for conventional metalearning algorithms.",
"We provide a theoretical derivation that argues that task proposals based on mutual information maximization can provide for a minimum worst-case regret meta-learner, under certain assumptions.",
"We then instantiate an approximation to the theoretically-motivated method by building on recently developed unsupervised task proposal and meta-learning algorithms.",
"Our experiments indicate that unsupervised meta-RL can accelerate learning on a range of tasks, outperforming learning from scratch and often matching the performance of meta-learning from hand-specified task distributions.",
"As our work is the first foray into unsupervised meta-RL, our approach opens a number of questions about unsupervised meta-learning algorithms.",
"One limitation of our analysis is that it only considers deterministic dynamics, and only considers task distributions where posterior sampling is optimal.",
"Extending our analysis to stochastic dynamics and more realistic task distributions may allow unsupervised meta-RL to acquire learning algorithms that can explore and adapt more intelligently, and more effectively solve real-world tasks."
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"Meta-learning on self-proposed task distributions to speed up reinforcement learning without human specified task distributions "
] |
[
"We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks.",
"The basic approach is to learn a model patch - a small set of parameters - that will specialize to each task, instead of fine-tuning the last layer or the entire network.",
"For instance, we show that learning a set of scales and biases is sufficient to convert a pretrained network to perform well on qualitatively different problems (e.g. converting a Single Shot MultiBox Detection (SSD) model into a 1000-class image classification model while reusing 98% of parameters of the SSD feature extractor).",
"Similarly, we show that re-learning existing low-parameter layers (such as depth-wise convolutions) while keeping the rest of the network frozen also improves transfer-learning accuracy significantly.",
"Our approach allows both simultaneous (multi-task) as well as sequential transfer learning.",
"In several multi-task learning problems, despite using much fewer parameters than traditional logits-only fine-tuning, we match single-task performance. \n",
"Deep neural networks have revolutionized many areas of machine intelligence and are now used for many vision tasks that even few years ago were considered nearly impenetrable BID15 BID26 .",
"Advances in neural networks and hardware is resulting in much of the computation being shifted to consumer devices, delivering faster response, and better security and privacy guarantees BID11 BID8 .As",
"the space of deep learning applications expands and starts to personalize, there is a growing need for the ability to quickly build and customize models. While",
"model sizes have dropped dramatically from >50M parameters of the pioneering work of AlexNet BID15 and VGG BID26 to <5M of the recent Mobilenet BID25 BID8 and ShuffleNet BID30 BID19 , the accuracy of models has been improving. However",
", delivering, maintaining and updating hundreds of models on the embedded device is still a significant expense in terms of bandwidth, energy and storage costs.While there still might be space for improvement in designing smaller models, in this paper we explore a different angle: we would like to be able to build models that require only a few parameters to be trained in order to be re-purposed to a different task, with minimal loss in accuracy compared to a model trained from scratch. While",
"there is ample existing work on compressing models and learning as few weights as possible BID24 BID25 BID8 to solve a single task, to the best of our awareness, there is no prior work that tries to minimize the number of model parameters when solving many tasks together.Our contribution is a novel learning paradigm in which each task carries its own model patcha small set of parameters -that, along with a shared set of parameters constitutes the model for that task (for a visual description of the idea, see FIG0 , left side). We put",
"this idea to use in two scenarios: a) in",
"transfer learning, by fine-tuning only the model patch for new tasks, and b) in",
"multi-task learning, where each task performs gradient updates to both its own model patch, and the shared parameters. In our",
"experiments (Section 5), the largest patch that we used is smaller than 10% of the size of the entire model. We now",
"describe our contribution in detail.Transfer learning We demonstrate that, by fine-tuning less than 35K parameters in MobilenetV2 BID25 and InceptionV3 , our method leads to significant accuracy improvements over fine-tuning only the last layer (102K-1.2M parameters, depending on the number of classes) on multiple transfer learning tasks. When combined",
"with fine-tuning the last layer, we train less than 10% of the model's parameters in total.We also show the effectiveness of our method over last-layer-based fine-tuning on transfer learning between completely different problems, namely COCO-trained SSD model to classification over ImageNet BID4 .Multi-task learning",
"We explore a multi-task learning paradigm wherein multiple models that share most of the parameters are trained simultaneously (see FIG0 , right side). Each model has a task-specific",
"model patch. Training is done in a distributed",
"manner; each task is assigned a subset of available workers that send independent gradient updates to both shared and task-specific parameters using standard optimization algorithms. Our results show that simultaneously",
"training two such MobilenetV2 BID25 ) models on ImageNet BID4 ) and Places-365 reach accuracies comparable to, and sometimes higher than individually trained models."
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] | BJxvEh0cFQ | true | [
"A novel and practically effective method to adapt pretrained neural networks to new tasks by retraining a minimal (e.g., less than 2%) number of parameters"
] |
[
"Adversarial examples have somewhat disrupted the enormous success of machine learning (ML) and are causing concern with regards to its trustworthiness: A small perturbation of an input results in an arbitrary failure of an otherwise seemingly well-trained ML system.",
"While studies are being conducted to discover the intrinsic properties of adversarial examples, such as their transferability and universality, there is insufficient theoretic analysis to help understand the phenomenon in a way that can influence the design process of ML experiments.",
"In this paper, we deduce an information-theoretic model which explains adversarial attacks universally as the abuse of feature redundancies in ML algorithms.",
"We prove that feature redundancy is a necessary condition for the existence of adversarial examples.",
"Our model helps to explain the major questions raised in many anecdotal studies on adversarial examples.",
"Our theory is backed up by empirical measurements of the information content of benign and adversarial examples on both image and text datasets.",
"Our measurements show that typical adversarial examples introduce just enough redundancy to overflow the decision making of a machine learner trained on corresponding benign examples.",
"We conclude with actionable recommendations to improve the robustness of machine learners against adversarial examples.",
"Deep neural networks (DNNs) have been widely applied to various applications and achieved great successes BID5 BID36 BID16 .",
"This is mostly due to their versatility: DNNs are able to be trained to fit a target function.",
"Therefore, it raises great concerns given the discovery that DNNs are vulnerable to adversarial examples.",
"These are carefully crafted inputs, which are often seemingly normal within the variance of the training data but can fool a well-trained model with high attack success rate BID14 .",
"Adversarial examples can be generated for various types of data, including images, text, audio, and software BID4 BID6 , and for different ML models, such as classifiers, segmentation models, object detectors, and reinforcement learning systems BID20 BID17 .",
"Moreover, adversarial examples are transferable BID38 BID23 )-if we generate adversarial perturbation against one model for a given input, the same perturbation will have high probability to be able to attack other models trained on similar data, regardless how different the models are.",
"Last but not the least, adversarial examples cannot only be synthesized in the digital world but also in the physical world BID7 BID21 , which has caused great real-world security concerns.Given such subtle, yet universally powerful attacks against ML models, several defensive methods have been proposed.",
"For example, ; BID9 pre-process inputs to eliminate certain perturbations.",
"Other work BID1 suggest to push the adversarial instance into random directions so they hopefully escape a local minimum and fall back to the correct class.",
"The authors are aware of ongoing work to establish metrics to distinguish adversarial examples from benign ones so that one can filter out adversarial examples before they are used by ML models.",
"However, so far, all defense and detection methods have shown to be adaptively attackable.",
"Therefore, intelligent attacks against intelligent defenses become an arms race.",
"Defending against adversarial examples remains an open problem.In this paper, we propose and validate a theoretical model that can be used to create an actionable understanding of adversarial perturbations.",
"Based upon the model, we give recommendations to modify the design process of ML experiments such that the effect of adversarial attacks is mitigated.",
"We illustrate adversarial examples using an example of a simple perceptron network that learns the Boolean equal operator and then generalize the example into a universal model of classification based on Shannon's theory of communication.",
"We further explain how adversarial examples fit the thermodynamics of computation.",
"We prove a necessary condition for the existence of adversarial examples.",
"In summary, the contributions of the paper are listed below:• a model for adversarial examples consistent with related work, physics and information theory;• a proof that using redundant features is a necessary condition for the vulnerability of ML models to adversarial examples;• extensive experiments that showcase the relationship between data redundancy and adversarial examples• actionable recommendations for the ML process to mitigate adversarial attacks.",
"Our theoretical and empirical results presented in this paper consistently show that adversarial examples are enabled by irrelevant input that the networks was not trained to suppress.",
"In fact, a single bit of redundancy can be exploited to cause the ML models to make arbitrary mistakes.",
"Moreover, redundancy exploited against one model can also affect the decision of another model trained on the same data as that other model learned to only cope with the same amount of redundancy (transferability-based attack).",
"Unfortunately, unlike the academic example in Section 3.1, we almost never know how many variables we actually need.",
"For image classification, for example, the current assumption is that each pixel serves as input and it is well known that this is feeding the network redundant information e.g., nobody would assume that the upper-most left-most pixel contributes to an object recognition result when the object is usually centered in the image.Nevertheless, the highest priority actionable recommendation has to be to reduce redundancies.",
"Before deep learning, manually-crafted features reduced redundancies assumed by humans before the data entered the ML system.",
"This practice has been abandoned with the introduction of deep learning, explaining the temporal correlation with the discovery of adversarial examples.",
"Short of going back to manual feature extraction, automatic techniques can be used to reduce redundancy.",
"Obviously, adaptive techniques, like auto encoders, will be susceptible to their own adversarial attacks.",
"However, consistent with our experiments in Section 4.2, and dependent on the input domain, we recommend to use lossy compression.",
"Similar results using quantization have been reported for MP3 and audio compression BID12 as well as molecular dynamics BID22 .",
"In general, we recommend a training procedure where input data is increasingly quantized while training accuracy is measured.",
"The point where the highest quantization is achieved at limited loss in accuracy, is the point where most of the noise and least of the content is lost.",
"This should be the point with least redundancies and therefore the operation point least susceptible to adversarial attacks.",
"In terms of detecting adversarial examples, we showed in Section 4 that estimating the complexity of the input using surrogate methods, such as different compression techniques, can serve as a prefilter to detect adversarial attacks.",
"We will dedicate future work to this topic.",
"Ultimately, however, the only way to practically guarantee adversarial attacks cannot happen is to present every possible input to the machine learner and train to 100% accuracy, which contradicts the idea of generalization in ML itself.",
"There is no free lunch.",
"A PROOF OF THEOREM 1Proof.",
"Let X be the set of admissible data points and X denote the set of adversarial examples,We prove this theorem by constructing a sufficient statistic T (X) that has lower entropy than T (X).",
"Consider DISPLAYFORM0 where x is an arbitrary benign example in the data space.",
"Then, for all x ∈ X , g(T (x)) = g(T (x )).",
"It follows that T (x) = T (x ), ∀x ∈ X .",
"On the other hand, T (x) = T (x) by construction.Let the probability density of T (X) be denoted by p(t), where t ∈ T (X ), and the probability density of T (X) be denoted by q(t) where t ∈ T (X \\ X ).",
"Then, q(t) = p(t) + w(t) for t ∈ T (X \\ X ), where w(t) corresponds to the part of benign example probability that is formed by enforcing an originally adversarial example' feature to be equal to the feature of an arbitrary benign example according to (2).",
"Furthermore, t∈T (X \\X ) w(t) = t∈T (X ) p(t).",
"We now compare the entropy of T (X) and T (X): DISPLAYFORM1 It is evident that U 1 ≥ 0.",
"Note that for any p(t), there always exists a configuration of w(t) such that U 2 ≥ 0.",
"For instance, let t * = arg max t∈T (X \\X ) p(t).",
"Then, we can let w(t * ) = t∈T (X ) p(t) and w(t) = 0 for t = t * .",
"With this configuration of w(t), U 2 = (p(t * ) + w(t * )) log((p(t * ) + w(t * )) − p(t * ) log p(t * ) (6) Due to the fact that x log x is a monotonically increasing function, U 2 ≥ 0.To sum up, both U 1 and U 2 are non-negative; as a result, H(T (X)) > H(T (X)) (7) Thus, we constructed a sufficient statistic T (·) that achieves lower entropy than T (·), which, in turn, indicates that T (X) is not a minimal sufficient statistic."
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"A new theoretical explanation for the existence of adversarial examples"
] |
Subsets and Splits