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Efficient Output Kernel Learning for Multiple Tasks | stat.ML cs.LG | The paradigm of multi-task learning is that one can achieve better
generalization by learning tasks jointly and thus exploiting the similarity
between the tasks rather than learning them independently of each other. While
previously the relationship between tasks had to be user-defined in the form of
an output kernel, recent approaches jointly learn the tasks and the output
kernel. As the output kernel is a positive semidefinite matrix, the resulting
optimization problems are not scalable in the number of tasks as an
eigendecomposition is required in each step. \mbox{Using} the theory of
positive semidefinite kernels we show in this paper that for a certain class of
regularizers on the output kernel, the constraint of being positive
semidefinite can be dropped as it is automatically satisfied for the relaxed
problem. This leads to an unconstrained dual problem which can be solved
efficiently. Experiments on several multi-task and multi-class data sets
illustrate the efficacy of our approach in terms of computational efficiency as
well as generalization performance.
| Pratik Jawanpuria and Maksim Lapin and Matthias Hein and Bernt Schiele | null | 1511.05706 | null | null |
Complex-Valued Gaussian Processes for Regression | cs.LG | In this paper we propose a novel Bayesian solution for nonlinear regression
in complex fields. Previous solutions for kernels methods usually assume a
complexification approach, where the real-valued kernel is replaced by a
complex-valued one. This approach is limited. Based on results in
complex-valued linear theory and Gaussian random processes we show that a
pseudo-kernel must be included. This is the starting point to develop the new
complex-valued formulation for Gaussian process for regression (CGPR). We face
the design of the covariance and pseudo-covariance based on a convolution
approach and for several scenarios. Just in the particular case where the
outputs are proper, the pseudo-kernel cancels. Also, the hyperparameters of the
covariance {can be learnt} maximizing the marginal likelihood using Wirtinger's
calculus and patterned complex-valued matrix derivatives. In the experiments
included, we show how CGPR successfully solve systems where real and imaginary
parts are correlated. Besides, we successfully solve the nonlinear channel
equalization problem by developing a recursive solution with basis removal. We
report remarkable improvements compared to previous solutions: a 2-4 dB
reduction of the MSE with {just a quarter} of the training samples used by
previous approaches.
| Rafael Boloix-Tortosa, Eva Arias-de-Reyna, F. Javier Payan-Somet, Juan
J. Murillo-Fuentes | 10.1109/TNNLS.2018.2805019 | 1511.05710 | null | null |
Online learning in repeated auctions | cs.GT cs.LG stat.ML | Motivated by online advertising auctions, we consider repeated Vickrey
auctions where goods of unknown value are sold sequentially and bidders only
learn (potentially noisy) information about a good's value once it is
purchased. We adopt an online learning approach with bandit feedback to model
this problem and derive bidding strategies for two models: stochastic and
adversarial. In the stochastic model, the observed values of the goods are
random variables centered around the true value of the good. In this case,
logarithmic regret is achievable when competing against well behaved
adversaries. In the adversarial model, the goods need not be identical and we
simply compare our performance against that of the best fixed bid in hindsight.
We show that sublinear regret is also achievable in this case and prove
matching minimax lower bounds. To our knowledge, this is the first complete set
of strategies for bidders participating in auctions of this type.
| Jonathan Weed, Vianney Perchet, Philippe Rigollet | null | 1511.05720 | null | null |
Sparse learning of maximum likelihood model for optimization of complex
loss function | cs.LG | Traditional machine learning methods usually minimize a simple loss function
to learn a predictive model, and then use a complex performance measure to
measure the prediction performance. However, minimizing a simple loss function
cannot guarantee that an optimal performance. In this paper, we study the
problem of optimizing the complex performance measure directly to obtain a
predictive model. We proposed to construct a maximum likelihood model for this
problem, and to learn the model parameter, we minimize a com- plex loss
function corresponding to the desired complex performance measure. To optimize
the loss function, we approximate the upper bound of the complex loss. We also
propose impose the sparsity to the model parameter to obtain a sparse model. An
objective is constructed by combining the upper bound of the loss function and
the sparsity of the model parameter, and we develop an iterative algorithm to
minimize it by using the fast iterative shrinkage- thresholding algorithm
framework. The experiments on optimization on three different complex
performance measures, including F-score, receiver operating characteristic
curve, and recall precision curve break even point, over three real-world
applications, aircraft event recognition of civil aviation safety, in- trusion
detection in wireless mesh networks, and image classification, show the
advantages of the proposed method over state-of-the-art methods.
| Ning Zhang and Prathamesh Chandrasekar | null | 1511.05743 | null | null |
Image Question Answering using Convolutional Neural Network with Dynamic
Parameter Prediction | cs.CV cs.CL cs.LG | We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent unit (GRU) taking a question as its input and a
fully-connected layer generating a set of candidate weights as its output.
However, it is challenging to construct a parameter prediction network for a
large number of parameters in the fully-connected dynamic parameter layer of
the CNN. We reduce the complexity of this problem by incorporating a hashing
technique, where the candidate weights given by the parameter prediction
network are selected using a predefined hash function to determine individual
weights in the dynamic parameter layer. The proposed network---joint network
with the CNN for ImageQA and the parameter prediction network---is trained
end-to-end through back-propagation, where its weights are initialized using a
pre-trained CNN and GRU. The proposed algorithm illustrates the
state-of-the-art performance on all available public ImageQA benchmarks.
| Hyeonwoo Noh, Paul Hongsuck Seo, Bohyung Han | null | 1511.05756 | null | null |
Metric learning approach for graph-based label propagation | cs.LG | The efficiency of graph-based semi-supervised algorithms depends on the graph
of instances on which they are applied. The instances are often in a vectorial
form before a graph linking them is built. The construction of the graph relies
on a metric over the vectorial space that help define the weight of the
connection between entities. The classic choice for this metric is usually a
distance measure or a similarity measure based on the euclidean norm. We claim
that in some cases the euclidean norm on the initial vectorial space might not
be the more appropriate to solve the task efficiently. We propose an algorithm
that aims at learning the most appropriate vectorial representation for
building a graph on which the task at hand is solved efficiently.
| Pauline Wauquier and Mikaela Keller | null | 1511.05789 | null | null |
Censoring Representations with an Adversary | cs.LG cs.AI stat.ML | In practice, there are often explicit constraints on what representations or
decisions are acceptable in an application of machine learning. For example it
may be a legal requirement that a decision must not favour a particular group.
Alternatively it can be that that representation of data must not have
identifying information. We address these two related issues by learning
flexible representations that minimize the capability of an adversarial critic.
This adversary is trying to predict the relevant sensitive variable from the
representation, and so minimizing the performance of the adversary ensures
there is little or no information in the representation about the sensitive
variable. We demonstrate this adversarial approach on two problems: making
decisions free from discrimination and removing private information from
images. We formulate the adversarial model as a minimax problem, and optimize
that minimax objective using a stochastic gradient alternate min-max optimizer.
We demonstrate the ability to provide discriminant free representations for
standard test problems, and compare with previous state of the art methods for
fairness, showing statistically significant improvement across most cases. The
flexibility of this method is shown via a novel problem: removing annotations
from images, from unaligned training examples of annotated and unannotated
images, and with no a priori knowledge of the form of annotation provided to
the model.
| Harrison Edwards, Amos Storkey | null | 1511.05897 | null | null |
Combining Neural Networks and Log-linear Models to Improve Relation
Extraction | cs.CL cs.LG | The last decade has witnessed the success of the traditional feature-based
method on exploiting the discrete structures such as words or lexical patterns
to extract relations from text. Recently, convolutional and recurrent neural
networks has provided very effective mechanisms to capture the hidden
structures within sentences via continuous representations, thereby
significantly advancing the performance of relation extraction. The advantage
of convolutional neural networks is their capacity to generalize the
consecutive k-grams in the sentences while recurrent neural networks are
effective to encode long ranges of sentence context. This paper proposes to
combine the traditional feature-based method, the convolutional and recurrent
neural networks to simultaneously benefit from their advantages. Our systematic
evaluation of different network architectures and combination methods
demonstrates the effectiveness of this approach and results in the
state-of-the-art performance on the ACE 2005 and SemEval dataset.
| Thien Huu Nguyen and Ralph Grishman | null | 1511.05926 | null | null |
On the Global Linear Convergence of Frank-Wolfe Optimization Variants | math.OC cs.LG stat.ML | The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity
thanks in particular to its ability to nicely handle the structured constraints
appearing in machine learning applications. However, its convergence rate is
known to be slow (sublinear) when the solution lies at the boundary. A simple
less-known fix is to add the possibility to take 'away steps' during
optimization, an operation that importantly does not require a feasibility
oracle. In this paper, we highlight and clarify several variants of the
Frank-Wolfe optimization algorithm that have been successfully applied in
practice: away-steps FW, pairwise FW, fully-corrective FW and Wolfe's minimum
norm point algorithm, and prove for the first time that they all enjoy global
linear convergence, under a weaker condition than strong convexity of the
objective. The constant in the convergence rate has an elegant interpretation
as the product of the (classical) condition number of the function with a novel
geometric quantity that plays the role of a 'condition number' of the
constraint set. We provide pointers to where these algorithms have made a
difference in practice, in particular with the flow polytope, the marginal
polytope and the base polytope for submodular optimization.
| Simon Lacoste-Julien and Martin Jaggi | null | 1511.05932 | null | null |
Seeding K-Means using Method of Moments | cs.LG | K-means is one of the most widely used algorithms for clustering in Data
Mining applications, which attempts to minimize the sum of the square of the
Euclidean distance of the points in the clusters from the respective means of
the clusters. However, K-means suffers from local minima problem and is not
guaranteed to converge to the optimal cost. K-means++ tries to address the
problem by seeding the means using a distance-based sampling scheme. However,
seeding the means in K-means++ needs $O\left(K\right)$ sequential passes
through the entire dataset, and this can be very costly for large datasets.
Here we propose a method of seeding the initial means based on factorizations
of higher order moments for bounded data. Our method takes $O\left(1\right)$
passes through the entire dataset to extract the initial set of means, and its
final cost can be proven to be within $O(\sqrt{K})$ of the optimal cost. We
demonstrate the performance of our algorithm in comparison with the existing
algorithms on various benchmark datasets.
| Sayantan Dasgupta | null | 1511.05933 | null | null |
Metric Learning with Adaptive Density Discrimination | stat.ML cs.LG | Distance metric learning (DML) approaches learn a transformation to a
representation space where distance is in correspondence with a predefined
notion of similarity. While such models offer a number of compelling benefits,
it has been difficult for these to compete with modern classification
algorithms in performance and even in feature extraction.
In this work, we propose a novel approach explicitly designed to address a
number of subtle yet important issues which have stymied earlier DML
algorithms. It maintains an explicit model of the distributions of the
different classes in representation space. It then employs this knowledge to
adaptively assess similarity, and achieve local discrimination by penalizing
class distribution overlap.
We demonstrate the effectiveness of this idea on several tasks. Our approach
achieves state-of-the-art classification results on a number of fine-grained
visual recognition datasets, surpassing the standard softmax classifier and
outperforming triplet loss by a relative margin of 30-40%. In terms of
computational performance, it alleviates training inefficiencies in the
traditional triplet loss, reaching the same error in 5-30 times fewer
iterations. Beyond classification, we further validate the saliency of the
learnt representations via their attribute concentration and hierarchy recovery
properties, achieving 10-25% relative gains on the softmax classifier and
25-50% on triplet loss in these tasks.
| Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev | null | 1511.05939 | null | null |
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks | cs.LG | Leveraging large historical data in electronic health record (EHR), we
developed Doctor AI, a generic predictive model that covers observed medical
conditions and medication uses. Doctor AI is a temporal model using recurrent
neural networks (RNN) and was developed and applied to longitudinal time
stamped EHR data from 260K patients over 8 years. Encounter records (e.g.
diagnosis codes, medication codes or procedure codes) were input to RNN to
predict (all) the diagnosis and medication categories for a subsequent visit.
Doctor AI assesses the history of patients to make multilabel predictions (one
label for each diagnosis or medication category). Based on separate blind test
set evaluation, Doctor AI can perform differential diagnosis with up to 79%
recall@30, significantly higher than several baselines. Moreover, we
demonstrate great generalizability of Doctor AI by adapting the resulting
models from one institution to another without losing substantial accuracy.
| Edward Choi and Mohammad Taha Bahadori and Andy Schuetz and Walter F.
Stewart and Jimeng Sun | null | 1511.05942 | null | null |
Unitary-Group Invariant Kernels and Features from Transformed Unlabeled
Data | cs.LG cs.AI | The study of representations invariant to common transformations of the data
is important to learning. Most techniques have focused on local approximate
invariance implemented within expensive optimization frameworks lacking
explicit theoretical guarantees. In this paper, we study kernels that are
invariant to the unitary group while having theoretical guarantees in
addressing practical issues such as (1) unavailability of transformed versions
of labelled data and (2) not observing all transformations. We present a
theoretically motivated alternate approach to the invariant kernel SVM. Unlike
previous approaches to the invariant SVM, the proposed formulation solves both
issues mentioned. We also present a kernel extension of a recent technique to
extract linear unitary-group invariant features addressing both issues and
extend some guarantees regarding invariance and stability. We present
experiments on the UCI ML datasets to illustrate and validate our methods.
| Dipan K. Pal, Marios Savvides | null | 1511.05943 | null | null |
ACDC: A Structured Efficient Linear Layer | cs.LG cs.NE | The linear layer is one of the most pervasive modules in deep learning
representations. However, it requires $O(N^2)$ parameters and $O(N^2)$
operations. These costs can be prohibitive in mobile applications or prevent
scaling in many domains. Here, we introduce a deep, differentiable,
fully-connected neural network module composed of diagonal matrices of
parameters, $\mathbf{A}$ and $\mathbf{D}$, and the discrete cosine transform
$\mathbf{C}$. The core module, structured as $\mathbf{ACDC^{-1}}$, has $O(N)$
parameters and incurs $O(N log N )$ operations. We present theoretical results
showing how deep cascades of ACDC layers approximate linear layers. ACDC is,
however, a stand-alone module and can be used in combination with any other
types of module. In our experiments, we show that it can indeed be successfully
interleaved with ReLU modules in convolutional neural networks for image
recognition. Our experiments also study critical factors in the training of
these structured modules, including initialization and depth. Finally, this
paper also provides a connection between structured linear transforms used in
deep learning and the field of Fourier optics, illustrating how ACDC could in
principle be implemented with lenses and diffractive elements.
| Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas | null | 1511.05946 | null | null |
Staleness-aware Async-SGD for Distributed Deep Learning | cs.LG | Deep neural networks have been shown to achieve state-of-the-art performance
in several machine learning tasks. Stochastic Gradient Descent (SGD) is the
preferred optimization algorithm for training these networks and asynchronous
SGD (ASGD) has been widely adopted for accelerating the training of large-scale
deep networks in a distributed computing environment. However, in practice it
is quite challenging to tune the training hyperparameters (such as learning
rate) when using ASGD so as achieve convergence and linear speedup, since the
stability of the optimization algorithm is strongly influenced by the
asynchronous nature of parameter updates. In this paper, we propose a variant
of the ASGD algorithm in which the learning rate is modulated according to the
gradient staleness and provide theoretical guarantees for convergence of this
algorithm. Experimental verification is performed on commonly-used image
classification benchmarks: CIFAR10 and Imagenet to demonstrate the superior
effectiveness of the proposed approach, compared to SSGD (Synchronous SGD) and
the conventional ASGD algorithm.
| Wei Zhang, Suyog Gupta, Xiangru Lian, Ji Liu | null | 1511.05950 | null | null |
Prioritized Experience Replay | cs.LG | Experience replay lets online reinforcement learning agents remember and
reuse experiences from the past. In prior work, experience transitions were
uniformly sampled from a replay memory. However, this approach simply replays
transitions at the same frequency that they were originally experienced,
regardless of their significance. In this paper we develop a framework for
prioritizing experience, so as to replay important transitions more frequently,
and therefore learn more efficiently. We use prioritized experience replay in
Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved
human-level performance across many Atari games. DQN with prioritized
experience replay achieves a new state-of-the-art, outperforming DQN with
uniform replay on 41 out of 49 games.
| Tom Schaul, John Quan, Ioannis Antonoglou, David Silver | null | 1511.05952 | null | null |
A pilot study on the daily control capability of s-EMG prosthetic hands
by amputees | cs.LG cs.HC | Surface electromyography is a valid tool to gather muscular contraction
signals from intact and amputated subjects. Electromyographic signals can be
used to control prosthetic devices in a noninvasive way distinguishing the
movements performed by the particular EMG electrodes activity. According to the
literature, several algorithms have been used to control prosthetic hands
through s-EMG signals. The main issue is to correctly classify the signals
acquired as the movement actually performed. This work presents a study on the
Support Vector Machine's performance in a short-time period, gained using two
different feature representation (Mean Absolute Value and Waveform Length) of
the sEMG signals. In particular, we paid close attention to the repeatability
problem, that is the capability to achieve a stable and satisfactory level of
accuracy in repeated experiments. Results on a limited setting are encouraging,
as they show an average accuracy above 73% even in the worst case scenario.
| Francesca Giordaniello | null | 1511.06001 | null | null |
Studying the control of non invasive prosthetic hands over large time
spans | cs.LG cs.HC | The electromyography (EMG) signal is the electrical manifestation of a
neuromuscular activation that provides access to physiological processes which
cause the muscle to generate force and produce movement. Non invasive
prostheses use such signals detected by the electrodes placed on the user's
stump, as input to generate hand posture movements according to the intentions
of the prosthesis wearer. The aim of this pilot study is to explore the
repeatability issue, i.e. the ability to classify 17 different hand postures,
represented by EMG signal, across a time span of days by a control algorithm.
Data collection experiments lasted four days and signals were collected from
the forearm of a single subject. We find that Support Vector Machine (SVM)
classification results are high enough to guarantee a correct classification of
more than 10 postures in each moment of the considered time span.
| Mara Graziani | null | 1511.06004 | null | null |
Regret Analysis of the Finite-Horizon Gittins Index Strategy for
Multi-Armed Bandits | cs.LG math.ST stat.ML stat.TH | I analyse the frequentist regret of the famous Gittins index strategy for
multi-armed bandits with Gaussian noise and a finite horizon. Remarkably it
turns out that this approach leads to finite-time regret guarantees comparable
to those available for the popular UCB algorithm. Along the way I derive
finite-time bounds on the Gittins index that are asymptotically exact and may
be of independent interest. I also discuss some computational issues and
present experimental results suggesting that a particular version of the
Gittins index strategy is a modest improvement on existing algorithms with
finite-time regret guarantees such as UCB and Thompson sampling.
| Tor Lattimore | null | 1511.06014 | null | null |
Segmental Recurrent Neural Networks | cs.CL cs.LG | We introduce segmental recurrent neural networks (SRNNs) which define, given
an input sequence, a joint probability distribution over segmentations of the
input and labelings of the segments. Representations of the input segments
(i.e., contiguous subsequences of the input) are computed by encoding their
constituent tokens using bidirectional recurrent neural nets, and these
"segment embeddings" are used to define compatibility scores with output
labels. These local compatibility scores are integrated using a global
semi-Markov conditional random field. Both fully supervised training -- in
which segment boundaries and labels are observed -- as well as partially
supervised training -- in which segment boundaries are latent -- are
straightforward. Experiments on handwriting recognition and joint Chinese word
segmentation/POS tagging show that, compared to models that do not explicitly
represent segments such as BIO tagging schemes and connectionist temporal
classification (CTC), SRNNs obtain substantially higher accuracies.
| Lingpeng Kong, Chris Dyer, Noah A. Smith | null | 1511.06018 | null | null |
Neural Variational Inference for Text Processing | cs.CL cs.LG stat.ML | Recent advances in neural variational inference have spawned a renaissance in
deep latent variable models. In this paper we introduce a generic variational
inference framework for generative and conditional models of text. While
traditional variational methods derive an analytic approximation for the
intractable distributions over latent variables, here we construct an inference
network conditioned on the discrete text input to provide the variational
distribution. We validate this framework on two very different text modelling
applications, generative document modelling and supervised question answering.
Our neural variational document model combines a continuous stochastic document
representation with a bag-of-words generative model and achieves the lowest
reported perplexities on two standard test corpora. The neural answer selection
model employs a stochastic representation layer within an attention mechanism
to extract the semantics between a question and answer pair. On two question
answering benchmarks this model exceeds all previous published benchmarks.
| Yishu Miao, Lei Yu and Phil Blunsom | null | 1511.06038 | null | null |
What Objective Does Self-paced Learning Indeed Optimize? | cs.LG cs.CV | Self-paced learning (SPL) is a recently raised methodology designed through
simulating the learning principle of humans/animals. A variety of SPL
realization schemes have been designed for different computer vision and
pattern recognition tasks, and empirically substantiated to be effective in
these applications. However, the investigation on its theoretical insight is
still a blank. To this issue, this study attempts to provide some new
theoretical understanding under the SPL scheme. Specifically, we prove that the
solving strategy on SPL accords with a majorization minimization algorithm
implemented on a latent objective function. Furthermore, we find that the loss
function contained in this latent objective has a similar configuration with
non-convex regularized penalty (NSPR) known in statistics and machine learning.
Such connection inspires us discovering more intrinsic relationship between SPL
regimes and NSPR forms, like SCAD, LOG and EXP. The robustness insight under
SPL can then be finely explained. We also analyze the capability of SPL on its
easy loss prior embedding property, and provide an insightful interpretation to
the effectiveness mechanism under previous SPL variations. Besides, we design a
group-partial-order loss prior, which is especially useful to weakly labeled
large-scale data processing tasks. Through applying SPL with this loss prior to
the FCVID dataset, which is currently one of the biggest manually annotated
video dataset, our method achieves state-of-the-art performance beyond previous
methods, which further helps supports the proposed theoretical arguments.
| Deyu Meng and Qian Zhao and Lu Jiang | null | 1511.06049 | null | null |
SparkNet: Training Deep Networks in Spark | stat.ML cs.DC cs.LG cs.NE math.OC | Training deep networks is a time-consuming process, with networks for object
recognition often requiring multiple days to train. For this reason, leveraging
the resources of a cluster to speed up training is an important area of work.
However, widely-popular batch-processing computational frameworks like
MapReduce and Spark were not designed to support the asynchronous and
communication-intensive workloads of existing distributed deep learning
systems. We introduce SparkNet, a framework for training deep networks in
Spark. Our implementation includes a convenient interface for reading data from
Spark RDDs, a Scala interface to the Caffe deep learning framework, and a
lightweight multi-dimensional tensor library. Using a simple parallelization
scheme for stochastic gradient descent, SparkNet scales well with the cluster
size and tolerates very high-latency communication. Furthermore, it is easy to
deploy and use with no parameter tuning, and it is compatible with existing
Caffe models. We quantify the dependence of the speedup obtained by SparkNet on
the number of machines, the communication frequency, and the cluster's
communication overhead, and we benchmark our system's performance on the
ImageNet dataset.
| Philipp Moritz, Robert Nishihara, Ion Stoica, Michael I. Jordan | null | 1511.06051 | null | null |
A Novel Approach for Phase Identification in Smart Grids Using Graph
Theory and Principal Component Analysis | cs.LG stat.AP stat.ML | Consumers with low demand, like households, are generally supplied
single-phase power by connecting their service mains to one of the phases of a
distribution transformer. The distribution companies face the problem of
keeping a record of consumer connectivity to a phase due to uninformed changes
that happen. The exact phase connectivity information is important for the
efficient operation and control of distribution system. We propose a new data
driven approach to the problem based on Principal Component Analysis (PCA) and
its Graph Theoretic interpretations, using energy measurements in equally timed
short intervals, generated from smart meters. We propose an algorithm for
inferring phase connectivity from noisy measurements. The algorithm is
demonstrated using simulated data for phase connectivities in distribution
networks.
| P Satya Jayadev, Aravind Rajeswaran, Nirav P Bhatt, Ramkrishna
Pasumarthy | null | 1511.06063 | null | null |
Deep Learning for Tactile Understanding From Visual and Haptic Data | cs.RO cs.CV cs.LG | Robots which interact with the physical world will benefit from a
fine-grained tactile understanding of objects and surfaces. Additionally, for
certain tasks, robots may need to know the haptic properties of an object
before touching it. To enable better tactile understanding for robots, we
propose a method of classifying surfaces with haptic adjectives (e.g.,
compressible or smooth) from both visual and physical interaction data. Humans
typically combine visual predictions and feedback from physical interactions to
accurately predict haptic properties and interact with the world. Inspired by
this cognitive pattern, we propose and explore a purely visual haptic
prediction model. Purely visual models enable a robot to "feel" without
physical interaction. Furthermore, we demonstrate that using both visual and
physical interaction signals together yields more accurate haptic
classification. Our models take advantage of recent advances in deep neural
networks by employing a unified approach to learning features for physical
interaction and visual observations. Even though we employ little domain
specific knowledge, our model still achieves better results than methods based
on hand-designed features.
| Yang Gao, Lisa Anne Hendricks, Katherine J. Kuchenbecker, Trevor
Darrell | null | 1511.06065 | null | null |
Transfer Learning for Speech and Language Processing | cs.CL cs.LG | Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.
| Dong Wang and Thomas Fang Zheng | null | 1511.06066 | null | null |
Convolutional neural networks with low-rank regularization | cs.LG cs.CV stat.ML | Large CNNs have delivered impressive performance in various computer vision
applications. But the storage and computation requirements make it problematic
for deploying these models on mobile devices. Recently, tensor decompositions
have been used for speeding up CNNs. In this paper, we further develop the
tensor decomposition technique. We propose a new algorithm for computing the
low-rank tensor decomposition for removing the redundancy in the convolution
kernels. The algorithm finds the exact global optimizer of the decomposition
and is more effective than iterative methods. Based on the decomposition, we
further propose a new method for training low-rank constrained CNNs from
scratch. Interestingly, while achieving a significant speedup, sometimes the
low-rank constrained CNNs delivers significantly better performance than their
non-constrained counterparts. On the CIFAR-10 dataset, the proposed low-rank
NIN model achieves $91.31\%$ accuracy (without data augmentation), which also
improves upon state-of-the-art result. We evaluated the proposed method on
CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet,
NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is
reduced by half while the performance is still comparable. Empirical success
suggests that low-rank tensor decompositions can be a very useful tool for
speeding up large CNNs.
| Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, Weinan E | null | 1511.06067 | null | null |
Reducing Overfitting in Deep Networks by Decorrelating Representations | cs.LG stat.ML | One major challenge in training Deep Neural Networks is preventing
overfitting. Many techniques such as data augmentation and novel regularizers
such as Dropout have been proposed to prevent overfitting without requiring a
massive amount of training data. In this work, we propose a new regularizer
called DeCov which leads to significantly reduced overfitting (as indicated by
the difference between train and val performance), and better generalization.
Our regularizer encourages diverse or non-redundant representations in Deep
Neural Networks by minimizing the cross-covariance of hidden activations. This
simple intuition has been explored in a number of past works but surprisingly
has never been applied as a regularizer in supervised learning. Experiments
across a range of datasets and network architectures show that this loss always
reduces overfitting while almost always maintaining or increasing
generalization performance and often improving performance over Dropout.
| Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, Dhruv
Batra | null | 1511.06068 | null | null |
Mediated Experts for Deep Convolutional Networks | cs.LG cs.NE | We present a new supervised architecture termed Mediated Mixture-of-Experts
(MMoE) that allows us to improve classification accuracy of Deep Convolutional
Networks (DCN). Our architecture achieves this with the help of expert
networks: A network is trained on a disjoint subset of a given dataset and then
run in parallel to other experts during deployment. A mediator is employed if
experts contradict each other. This allows our framework to naturally support
incremental learning, as adding new classes requires (re-)training of the new
expert only. We also propose two measures to control computational complexity:
An early-stopping mechanism halts experts that have low confidence in their
prediction. The system allows to trade-off accuracy and complexity without
further retraining. We also suggest to share low-level convolutional layers
between experts in an effort to avoid computation of a near-duplicate feature
set. We evaluate our system on a popular dataset and report improved accuracy
compared to a single model of same configuration.
| Sebastian Agethen, Winston H. Hsu | null | 1511.06072 | null | null |
Learning Deep Structure-Preserving Image-Text Embeddings | cs.CV cs.CL cs.LG | This paper proposes a method for learning joint embeddings of images and text
using a two-branch neural network with multiple layers of linear projections
followed by nonlinearities. The network is trained using a large margin
objective that combines cross-view ranking constraints with within-view
neighborhood structure preservation constraints inspired by metric learning
literature. Extensive experiments show that our approach gains significant
improvements in accuracy for image-to-text and text-to-image retrieval. Our
method achieves new state-of-the-art results on the Flickr30K and MSCOCO
image-sentence datasets and shows promise on the new task of phrase
localization on the Flickr30K Entities dataset.
| Liwei Wang, Yin Li, Svetlana Lazebnik | null | 1511.06078 | null | null |
Variable Rate Image Compression with Recurrent Neural Networks | cs.CV cs.LG cs.NE | A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of the initial page load process to improve
apparent page responsiveness. Increasing thumbnail compression beyond the
capabilities of existing codecs is therefore a current research focus, as any
byte savings will significantly enhance the experience of mobile device users.
Toward this end, we propose a general framework for variable-rate image
compression and a novel architecture based on convolutional and deconvolutional
LSTM recurrent networks. Our models address the main issues that have prevented
autoencoder neural networks from competing with existing image compression
algorithms: (1) our networks only need to be trained once (not per-image),
regardless of input image dimensions and the desired compression rate; (2) our
networks are progressive, meaning that the more bits are sent, the more
accurate the image reconstruction; and (3) the proposed architecture is at
least as efficient as a standard purpose-trained autoencoder for a given number
of bits. On a large-scale benchmark of 32$\times$32 thumbnails, our LSTM-based
approaches provide better visual quality than (headerless) JPEG, JPEG2000 and
WebP, with a storage size that is reduced by 10% or more.
| George Toderici, Sean M. O'Malley, Sung Jin Hwang, Damien Vincent,
David Minnen, Shumeet Baluja, Michele Covell, Rahul Sukthankar | null | 1511.06085 | null | null |
Principled Parallel Mean-Field Inference for Discrete Random Fields | cs.CV cs.LG | Mean-field variational inference is one of the most popular approaches to
inference in discrete random fields. Standard mean-field optimization is based
on coordinate descent and in many situations can be impractical. Thus, in
practice, various parallel techniques are used, which either rely on ad-hoc
smoothing with heuristically set parameters, or put strong constraints on the
type of models. In this paper, we propose a novel proximal gradient-based
approach to optimizing the variational objective. It is naturally
parallelizable and easy to implement. We prove its convergence, and then
demonstrate that, in practice, it yields faster convergence and often finds
better optima than more traditional mean-field optimization techniques.
Moreover, our method is less sensitive to the choice of parameters.
| Pierre Baqu\'e, Timur Bagautdinov, Fran\c{c}ois Fleuret and Pascal Fua | null | 1511.06103 | null | null |
Semi-supervised Learning for Convolutional Neural Networks via Online
Graph Construction | cs.NE cs.CV cs.LG | The recent promising achievements of deep learning rely on the large amount
of labeled data. Considering the abundance of data on the web, most of them do
not have labels at all. Therefore, it is important to improve generalization
performance using unlabeled data on supervised tasks with few labeled
instances. In this work, we revisit graph-based semi-supervised learning
algorithms and propose an online graph construction technique which suits deep
convolutional neural network better. We consider an EM-like algorithm for
semi-supervised learning on deep neural networks: In forward pass, the graph is
constructed based on the network output, and the graph is then used for loss
calculation to help update the network by back propagation in the backward
pass. We demonstrate the strength of our online approach compared to the
conventional ones whose graph is constructed on static but not robust enough
feature representations beforehand.
| Sheng-Yi Bai, Sebastian Agethen, Ting-Hsuan Chao, Winston Hsu | null | 1511.06104 | null | null |
Multi-task Sequence to Sequence Learning | cs.LG cs.CL stat.ML | Sequence to sequence learning has recently emerged as a new paradigm in
supervised learning. To date, most of its applications focused on only one task
and not much work explored this framework for multiple tasks. This paper
examines three multi-task learning (MTL) settings for sequence to sequence
models: (a) the oneto-many setting - where the encoder is shared between
several tasks such as machine translation and syntactic parsing, (b) the
many-to-one setting - useful when only the decoder can be shared, as in the
case of translation and image caption generation, and (c) the many-to-many
setting - where multiple encoders and decoders are shared, which is the case
with unsupervised objectives and translation. Our results show that training on
a small amount of parsing and image caption data can improve the translation
quality between English and German by up to 1.5 BLEU points over strong
single-task baselines on the WMT benchmarks. Furthermore, we have established a
new state-of-the-art result in constituent parsing with 93.0 F1. Lastly, we
reveal interesting properties of the two unsupervised learning objectives,
autoencoder and skip-thought, in the MTL context: autoencoder helps less in
terms of perplexities but more on BLEU scores compared to skip-thought.
| Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz
Kaiser | null | 1511.06114 | null | null |
Coreset-Based Adaptive Tracking | cs.CV cs.LG | We propose a method for learning from streaming visual data using a compact,
constant size representation of all the data that was seen until a given
moment. Specifically, we construct a 'coreset' representation of streaming data
using a parallelized algorithm, which is an approximation of a set with
relation to the squared distances between this set and all other points in its
ambient space. We learn an adaptive object appearance model from the coreset
tree in constant time and logarithmic space and use it for object tracking by
detection. Our method obtains excellent results for object tracking on three
standard datasets over more than 100 videos. The ability to summarize data
efficiently makes our method ideally suited for tracking in long videos in
presence of space and time constraints. We demonstrate this ability by
outperforming a variety of algorithms on the TLD dataset with 2685 frames on
average. This coreset based learning approach can be applied for both real-time
learning of small, varied data and fast learning of big data.
| Abhimanyu Dubey, Nikhil Naik, Dan Raviv, Rahul Sukthankar and Ramesh
Raskar | null | 1511.06147 | null | null |
Adjustable Bounded Rectifiers: Towards Deep Binary Representations | cs.LG stat.ML | Binary representation is desirable for its memory efficiency, computation
speed and robustness. In this paper, we propose adjustable bounded rectifiers
to learn binary representations for deep neural networks. While hard
constraining representations across layers to be binary makes training
unreasonably difficult, we softly encourage activations to diverge from real
values to binary by approximating step functions. Our final representation is
completely binary. We test our approach on MNIST, CIFAR10, and ILSVRC2012
dataset, and systematically study the training dynamics of the binarization
process. Our approach can binarize the last layer representation without loss
of performance and binarize all the layers with reasonably small degradations.
The memory space that it saves may allow more sophisticated models to be
deployed, thus compensating the loss. To the best of our knowledge, this is the
first work to report results on current deep network architectures using
complete binary middle representations. Given the learned representations, we
find that the firing or inhibition of a binary neuron is usually associated
with a meaningful interpretation across different classes. This suggests that
the semantic structure of a neural network may be manifested through a guided
binarization process.
| Zhirong Wu, Dahua Lin, Xiaoou Tang | null | 1511.06201 | null | null |
Diffusion Representations | stat.ML cs.LG math.SP | Diffusion Maps framework is a kernel based method for manifold learning and
data analysis that defines diffusion similarities by imposing a Markovian
process on the given dataset. Analysis by this process uncovers the intrinsic
geometric structures in the data. Recently, it was suggested to replace the
standard kernel by a measure-based kernel that incorporates information about
the density of the data. Thus, the manifold assumption is replaced by a more
general measure-based assumption.
The measure-based diffusion kernel incorporates two separate independent
representations. The first determines a measure that correlates with a density
that represents normal behaviors and patterns in the data. The second consists
of the analyzed multidimensional data points.
In this paper, we present a representation framework for data analysis of
datasets that is based on a closed-form decomposition of the measure-based
kernel. The proposed representation preserves pairwise diffusion distances that
does not depend on the data size while being invariant to scale. For a
stationary data, no out-of-sample extension is needed for embedding newly
arrived data points in the representation space. Several aspects of the
presented methodology are demonstrated on analytically generated data.
| Moshe Salhov and Amit Bermanis and Guy Wolf and Amir Averbuch | null | 1511.06208 | null | null |
Knowledge Base Population using Semantic Label Propagation | cs.CL cs.LG | A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.
| Lucas Sterckx and Thomas Demeester and Johannes Deleu and Chris
Develder | null | 1511.06219 | null | null |
Towards Open Set Deep Networks | cs.CV cs.LG | Deep networks have produced significant gains for various visual recognition
problems, leading to high impact academic and commercial applications. Recent
work in deep networks highlighted that it is easy to generate images that
humans would never classify as a particular object class, yet networks classify
such images high confidence as that given class - deep network are easily
fooled with images humans do not consider meaningful. The closed set nature of
deep networks forces them to choose from one of the known classes leading to
such artifacts. Recognition in the real world is open set, i.e. the recognition
system should reject unknown/unseen classes at test time. We present a
methodology to adapt deep networks for open set recognition, by introducing a
new model layer, OpenMax, which estimates the probability of an input being
from an unknown class. A key element of estimating the unknown probability is
adapting Meta-Recognition concepts to the activation patterns in the
penultimate layer of the network. OpenMax allows rejection of "fooling" and
unrelated open set images presented to the system; OpenMax greatly reduces the
number of obvious errors made by a deep network. We prove that the OpenMax
concept provides bounded open space risk, thereby formally providing an open
set recognition solution. We evaluate the resulting open set deep networks
using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation
data, and thousands of fooling and open set images. The proposed OpenMax model
significantly outperforms open set recognition accuracy of basic deep networks
as well as deep networks with thresholding of SoftMax probabilities.
| Abhijit Bendale, Terrance Boult | null | 1511.06233 | null | null |
Multimodal sparse representation learning and applications | cs.LG cs.CV stat.ML | Unsupervised methods have proven effective for discriminative tasks in a
single-modality scenario. In this paper, we present a multimodal framework for
learning sparse representations that can capture semantic correlation between
modalities. The framework can model relationships at a higher level by forcing
the shared sparse representation. In particular, we propose the use of joint
dictionary learning technique for sparse coding and formulate the joint
representation for concision, cross-modal representations (in case of a missing
modality), and union of the cross-modal representations. Given the accelerated
growth of multimodal data posted on the Web such as YouTube, Wikipedia, and
Twitter, learning good multimodal features is becoming increasingly important.
We show that the shared representations enabled by our framework substantially
improve the classification performance under both unimodal and multimodal
settings. We further show how deep architectures built on the proposed
framework are effective for the case of highly nonlinear correlations between
modalities. The effectiveness of our approach is demonstrated experimentally in
image denoising, multimedia event detection and retrieval on the TRECVID
dataset (audio-video), category classification on the Wikipedia dataset
(image-text), and sentiment classification on PhotoTweet (image-text).
| Miriam Cha, Youngjune Gwon, H.T. Kung | null | 1511.06238 | null | null |
Convolutional Clustering for Unsupervised Learning | cs.LG cs.CV | The task of labeling data for training deep neural networks is daunting and
tedious, requiring millions of labels to achieve the current state-of-the-art
results. Such reliance on large amounts of labeled data can be relaxed by
exploiting hierarchical features via unsupervised learning techniques. In this
work, we propose to train a deep convolutional network based on an enhanced
version of the k-means clustering algorithm, which reduces the number of
correlated parameters in the form of similar filters, and thus increases test
categorization accuracy. We call our algorithm convolutional k-means
clustering. We further show that learning the connection between the layers of
a deep convolutional neural network improves its ability to be trained on a
smaller amount of labeled data. Our experiments show that the proposed
algorithm outperforms other techniques that learn filters unsupervised.
Specifically, we obtained a test accuracy of 74.1% on STL-10 and a test error
of 0.5% on MNIST.
| Aysegul Dundar, Jonghoon Jin and Eugenio Culurciello | null | 1511.06241 | null | null |
Predicting online user behaviour using deep learning algorithms | cs.LG stat.ML | We propose a robust classifier to predict buying intentions based on user
behaviour within a large e-commerce website. In this work we compare
traditional machine learning techniques with the most advanced deep learning
approaches. We show that both Deep Belief Networks and Stacked Denoising
auto-Encoders achieved a substantial improvement by extracting features from
high dimensional data during the pre-train phase. They prove also to be more
convenient to deal with severe class imbalance.
| Armando Vieira | null | 1511.06247 | null | null |
Stochastic modified equations and adaptive stochastic gradient
algorithms | cs.LG stat.ML | We develop the method of stochastic modified equations (SME), in which
stochastic gradient algorithms are approximated in the weak sense by
continuous-time stochastic differential equations. We exploit the continuous
formulation together with optimal control theory to derive novel adaptive
hyper-parameter adjustment policies. Our algorithms have competitive
performance with the added benefit of being robust to varying models and
datasets. This provides a general methodology for the analysis and design of
stochastic gradient algorithms.
| Qianxiao Li, Cheng Tai, Weinan E | null | 1511.06251 | null | null |
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding
For Image & Text Retrieval | cs.LG | Joint modeling of language and vision has been drawing increasing interest. A
multimodal data representation allowing for bidirectional retrieval of images
by sentences and vice versa is a key aspect. In this paper we present three
contributions in canonical correlation analysis (CCA) based multimodal
retrieval. Firstly, we show that an asymmetric weighting of the canonical
weights, while achieving a cross view mapping from the search to the query
space, improves the retrieval performance. Secondly, we devise a
computationally efficient model selection, crucial to generalization and
stability, in the framework of the Bj\"ork Golub algorithm for regularized CCA
via spectral filtering. Finally, we introduce a Hierarchical Kernel Sentence
Embedding (HKSE) that approximates Kernel CCA for a special similarity kernel
between distribution of words embedded in a vector space. State of the art
results are obtained on MSCOCO and Flickr benchmarks when these three
techniques are used in conjunction.
| Youssef Mroueh, Etienne Marcheret, Vaibhava Goel | null | 1511.06267 | null | null |
Faster method for Deep Belief Network based Object classification using
DWT | cs.CV cs.LG | A Deep Belief Network (DBN) requires large, multiple hidden layers with high
number of hidden units to learn good features from the raw pixels of large
images. This implies more training time as well as computational complexity. By
integrating DBN with Discrete Wavelet Transform (DWT), both training time and
computational complexity can be reduced. The low resolution images obtained
after application of DWT are used to train multiple DBNs. The results obtained
from these DBNs are combined using a weighted voting algorithm. The performance
of this method is found to be competent and faster in comparison with that of
traditional DBNs.
| Saurabh Sihag and Pranab Kumar Dutta | null | 1511.06276 | null | null |
Neural Programmer-Interpreters | cs.LG cs.NE | We propose the neural programmer-interpreter (NPI): a recurrent and
compositional neural network that learns to represent and execute programs. NPI
has three learnable components: a task-agnostic recurrent core, a persistent
key-value program memory, and domain-specific encoders that enable a single NPI
to operate in multiple perceptually diverse environments with distinct
affordances. By learning to compose lower-level programs to express
higher-level programs, NPI reduces sample complexity and increases
generalization ability compared to sequence-to-sequence LSTMs. The program
memory allows efficient learning of additional tasks by building on existing
programs. NPI can also harness the environment (e.g. a scratch pad with
read-write pointers) to cache intermediate results of computation, lessening
the long-term memory burden on recurrent hidden units. In this work we train
the NPI with fully-supervised execution traces; each program has example
sequences of calls to the immediate subprograms conditioned on the input.
Rather than training on a huge number of relatively weak labels, NPI learns
from a small number of rich examples. We demonstrate the capability of our
model to learn several types of compositional programs: addition, sorting, and
canonicalizing 3D models. Furthermore, a single NPI learns to execute these
programs and all 21 associated subprograms.
| Scott Reed and Nando de Freitas | null | 1511.06279 | null | null |
Density Modeling of Images using a Generalized Normalization
Transformation | cs.LG cs.CV | We introduce a parametric nonlinear transformation that is well-suited for
Gaussianizing data from natural images. The data are linearly transformed, and
each component is then normalized by a pooled activity measure, computed by
exponentiating a weighted sum of rectified and exponentiated components and a
constant. We optimize the parameters of the full transformation (linear
transform, exponents, weights, constant) over a database of natural images,
directly minimizing the negentropy of the responses. The optimized
transformation substantially Gaussianizes the data, achieving a significantly
smaller mutual information between transformed components than alternative
methods including ICA and radial Gaussianization. The transformation is
differentiable and can be efficiently inverted, and thus induces a density
model on images. We show that samples of this model are visually similar to
samples of natural image patches. We demonstrate the use of the model as a
prior probability density that can be used to remove additive noise. Finally,
we show that the transformation can be cascaded, with each layer optimized
using the same Gaussianization objective, thus offering an unsupervised method
of optimizing a deep network architecture.
| Johannes Ball\'e and Valero Laparra and Eero P. Simoncelli | null | 1511.06281 | null | null |
Foveation-based Mechanisms Alleviate Adversarial Examples | cs.LG cs.CV | We show that adversarial examples, i.e., the visually imperceptible
perturbations that result in Convolutional Neural Networks (CNNs) fail, can be
alleviated with a mechanism based on foveations---applying the CNN in different
image regions. To see this, first, we report results in ImageNet that lead to a
revision of the hypothesis that adversarial perturbations are a consequence of
CNNs acting as a linear classifier: CNNs act locally linearly to changes in the
image regions with objects recognized by the CNN, and in other regions the CNN
may act non-linearly. Then, we corroborate that when the neural responses are
linear, applying the foveation mechanism to the adversarial example tends to
significantly reduce the effect of the perturbation. This is because,
hypothetically, the CNNs for ImageNet are robust to changes of scale and
translation of the object produced by the foveation, but this property does not
generalize to transformations of the perturbation. As a result, the accuracy
after a foveation is almost the same as the accuracy of the CNN without the
adversarial perturbation, even if the adversarial perturbation is calculated
taking into account a foveation.
| Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, Qi Zhao | null | 1511.06292 | null | null |
Policy Distillation | cs.LG | Policies for complex visual tasks have been successfully learned with deep
reinforcement learning, using an approach called deep Q-networks (DQN), but
relatively large (task-specific) networks and extensive training are needed to
achieve good performance. In this work, we present a novel method called policy
distillation that can be used to extract the policy of a reinforcement learning
agent and train a new network that performs at the expert level while being
dramatically smaller and more efficient. Furthermore, the same method can be
used to consolidate multiple task-specific policies into a single policy. We
demonstrate these claims using the Atari domain and show that the multi-task
distilled agent outperforms the single-task teachers as well as a
jointly-trained DQN agent.
| Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume
Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray
Kavukcuoglu, Raia Hadsell | null | 1511.06295 | null | null |
Conditional Computation in Neural Networks for faster models | cs.LG | Deep learning has become the state-of-art tool in many applications, but the
evaluation and training of deep models can be time-consuming and
computationally expensive. The conditional computation approach has been
proposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013). It
operates by selectively activating only parts of the network at a time. In this
paper, we use reinforcement learning as a tool to optimize conditional
computation policies. More specifically, we cast the problem of learning
activation-dependent policies for dropping out blocks of units as a
reinforcement learning problem. We propose a learning scheme motivated by
computation speed, capturing the idea of wanting to have parsimonious
activations while maintaining prediction accuracy. We apply a policy gradient
algorithm for learning policies that optimize this loss function and propose a
regularization mechanism that encourages diversification of the dropout policy.
We present encouraging empirical results showing that this approach improves
the speed of computation without impacting the quality of the approximation.
| Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau and Doina Precup | null | 1511.06297 | null | null |
Alternative structures for character-level RNNs | cs.LG cs.CL | Recurrent neural networks are convenient and efficient models for language
modeling. However, when applied on the level of characters instead of words,
they suffer from several problems. In order to successfully model long-term
dependencies, the hidden representation needs to be large. This in turn implies
higher computational costs, which can become prohibitive in practice. We
propose two alternative structural modifications to the classical RNN model.
The first one consists on conditioning the character level representation on
the previous word representation. The other one uses the character history to
condition the output probability. We evaluate the performance of the two
proposed modifications on challenging, multi-lingual real world data.
| Piotr Bojanowski and Armand Joulin and Tomas Mikolov | null | 1511.06303 | null | null |
Robust Convolutional Neural Networks under Adversarial Noise | cs.LG cs.CV | Recent studies have shown that Convolutional Neural Networks (CNNs) are
vulnerable to a small perturbation of input called "adversarial examples". In
this work, we propose a new feedforward CNN that improves robustness in the
presence of adversarial noise. Our model uses stochastic additive noise added
to the input image and to the CNN models. The proposed model operates in
conjunction with a CNN trained with either standard or adversarial objective
function. In particular, convolution, max-pooling, and ReLU layers are modified
to benefit from the noise model. Our feedforward model is parameterized by only
a mean and variance per pixel which simplifies computations and makes our
method scalable to a deep architecture. From CIFAR-10 and ImageNet test, the
proposed model outperforms other methods and the improvement is more evident
for difficult classification tasks or stronger adversarial noise.
| Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello | null | 1511.06306 | null | null |
Spatio-temporal video autoencoder with differentiable memory | cs.LG cs.CV | We describe a new spatio-temporal video autoencoder, based on a classic
spatial image autoencoder and a novel nested temporal autoencoder. The temporal
encoder is represented by a differentiable visual memory composed of
convolutional long short-term memory (LSTM) cells that integrate changes over
time. Here we target motion changes and use as temporal decoder a robust
optical flow prediction module together with an image sampler serving as
built-in feedback loop. The architecture is end-to-end differentiable. At each
time step, the system receives as input a video frame, predicts the optical
flow based on the current observation and the LSTM memory state as a dense
transformation map, and applies it to the current frame to generate the next
frame. By minimising the reconstruction error between the predicted next frame
and the corresponding ground truth next frame, we train the whole system to
extract features useful for motion estimation without any supervision effort.
We present one direct application of the proposed framework in
weakly-supervised semantic segmentation of videos through label propagation
using optical flow.
| Viorica Patraucean, Ankur Handa, Roberto Cipolla | null | 1511.06309 | null | null |
Why M Heads are Better than One: Training a Diverse Ensemble of Deep
Networks | cs.CV cs.LG cs.NE | Convolutional Neural Networks have achieved state-of-the-art performance on a
wide range of tasks. Most benchmarks are led by ensembles of these powerful
learners, but ensembling is typically treated as a post-hoc procedure
implemented by averaging independently trained models with model variation
induced by bagging or random initialization. In this paper, we rigorously treat
ensembling as a first-class problem to explicitly address the question: what
are the best strategies to create an ensemble? We first compare a large number
of ensembling strategies, and then propose and evaluate novel strategies, such
as parameter sharing (through a new family of models we call TreeNets) as well
as training under ensemble-aware and diversity-encouraging losses. We
demonstrate that TreeNets can improve ensemble performance and that diverse
ensembles can be trained end-to-end under a unified loss, achieving
significantly higher "oracle" accuracies than classical ensembles.
| Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall,
and Dhruv Batra | null | 1511.06314 | null | null |
Neural network-based clustering using pairwise constraints | cs.LG stat.ML | This paper presents a neural network-based end-to-end clustering framework.
We design a novel strategy to utilize the contrastive criteria for pushing
data-forming clusters directly from raw data, in addition to learning a feature
embedding suitable for such clustering. The network is trained with weak
labels, specifically partial pairwise relationships between data instances. The
cluster assignments and their probabilities are then obtained at the output
layer by feed-forwarding the data. The framework has the interesting
characteristic that no cluster centers need to be explicitly specified, thus
the resulting cluster distribution is purely data-driven and no distance
metrics need to be predefined. The experiments show that the proposed approach
beats the conventional two-stage method (feature embedding with k-means) by a
significant margin. It also compares favorably to the performance of the
standard cross entropy loss for classification. Robustness analysis also shows
that the method is largely insensitive to the number of clusters. Specifically,
we show that the number of dominant clusters is close to the true number of
clusters even when a large k is used for clustering.
| Yen-Chang Hsu, Zsolt Kira | null | 1511.06321 | null | null |
Manifold Regularized Discriminative Neural Networks | cs.LG | Unregularized deep neural networks (DNNs) can be easily overfit with a
limited sample size. We argue that this is mostly due to the disriminative
nature of DNNs which directly model the conditional probability (or score) of
labels given the input. The ignorance of input distribution makes DNNs
difficult to generalize to unseen data. Recent advances in regularization
techniques, such as pretraining and dropout, indicate that modeling input data
distribution (either explicitly or implicitly) greatly improves the
generalization ability of a DNN. In this work, we explore the manifold
hypothesis which assumes that instances within the same class lie in a smooth
manifold. We accordingly propose two simple regularizers to a standard
discriminative DNN. The first one, named Label-Aware Manifold Regularization,
assumes the availability of labels and penalizes large norms of the loss
function w.r.t. data points. The second one, named Label-Independent Manifold
Regularization, does not use label information and instead penalizes the
Frobenius norm of the Jacobian matrix of prediction scores w.r.t. data points,
which makes semi-supervised learning possible. We perform extensive control
experiments on fully supervised and semi-supervised tasks using the MNIST,
CIFAR10 and SVHN datasets and achieve excellent results.
| Shuangfei Zhai, Zhongfei Zhang | null | 1511.06328 | null | null |
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its
Application to Inverse Problems | cs.LG | The sparsity of signals in a transform domain or dictionary has been
exploited in applications such as compression, denoising and inverse problems.
More recently, data-driven adaptation of synthesis dictionaries has shown
promise compared to analytical dictionary models. However, dictionary learning
problems are typically non-convex and NP-hard, and the usual alternating
minimization approaches for these problems are often computationally expensive,
with the computations dominated by the NP-hard synthesis sparse coding step.
This paper exploits the ideas that drive algorithms such as K-SVD, and
investigates in detail efficient methods for aggregate sparsity penalized
dictionary learning by first approximating the data with a sum of sparse
rank-one matrices (outer products) and then using a block coordinate descent
approach to estimate the unknowns. The resulting block coordinate descent
algorithms involve efficient closed-form solutions. Furthermore, we consider
the problem of dictionary-blind image reconstruction, and propose novel and
efficient algorithms for adaptive image reconstruction using block coordinate
descent and sum of outer products methodologies. We provide a convergence study
of the algorithms for dictionary learning and dictionary-blind image
reconstruction. Our numerical experiments show the promising performance and
speed-ups provided by the proposed methods over previous schemes in sparse data
representation and compressed sensing-based image reconstruction.
| Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler | 10.1109/TCI.2017.2697206 | 1511.06333 | null | null |
Unsupervised Deep Embedding for Clustering Analysis | cs.LG cs.CV | Clustering is central to many data-driven application domains and has been
studied extensively in terms of distance functions and grouping algorithms.
Relatively little work has focused on learning representations for clustering.
In this paper, we propose Deep Embedded Clustering (DEC), a method that
simultaneously learns feature representations and cluster assignments using
deep neural networks. DEC learns a mapping from the data space to a
lower-dimensional feature space in which it iteratively optimizes a clustering
objective. Our experimental evaluations on image and text corpora show
significant improvement over state-of-the-art methods.
| Junyuan Xie, Ross Girshick, Ali Farhadi | null | 1511.06335 | null | null |
Robust Classification by Pre-conditioned LASSO and Transductive
Diffusion Component Analysis | cs.LG cs.CV math.ST stat.ML stat.TH | Modern machine learning-based recognition approaches require large-scale
datasets with large number of labelled training images. However, such datasets
are inherently difficult and costly to collect and annotate. Hence there is a
great and growing interest in automatic dataset collection methods that can
leverage the web. % which are collected % in a cheap, efficient and yet
unreliable way. Collecting datasets in this way, however, requires robust and
efficient ways for detecting and excluding outliers that are common and
prevalent. % Outliers are thus a % prominent treat of using these dataset. So
far, there have been a limited effort in machine learning community to directly
detect outliers for robust classification. Inspired by the recent work on
Pre-conditioned LASSO, this paper formulates the outlier detection task using
Pre-conditioned LASSO and employs \red{unsupervised} transductive diffusion
component analysis to both integrate the topological structure of the data
manifold, from labeled and unlabeled instances, and reduce the feature
dimensionality. Synthetic experiments as well as results on two real-world
classification tasks show that our framework can robustly detect the outliers
and improve classification.
| Yanwei Fu and De-An Huang and Leonid Sigal | null | 1511.06340 | null | null |
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning | cs.LG | The ability to act in multiple environments and transfer previous knowledge
to new situations can be considered a critical aspect of any intelligent agent.
Towards this goal, we define a novel method of multitask and transfer learning
that enables an autonomous agent to learn how to behave in multiple tasks
simultaneously, and then generalize its knowledge to new domains. This method,
termed "Actor-Mimic", exploits the use of deep reinforcement learning and model
compression techniques to train a single policy network that learns how to act
in a set of distinct tasks by using the guidance of several expert teachers. We
then show that the representations learnt by the deep policy network are
capable of generalizing to new tasks with no prior expert guidance, speeding up
learning in novel environments. Although our method can in general be applied
to a wide range of problems, we use Atari games as a testing environment to
demonstrate these methods.
| Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov | null | 1511.06342 | null | null |
Online Batch Selection for Faster Training of Neural Networks | cs.LG cs.NE math.OC | Deep neural networks are commonly trained using stochastic non-convex
optimization procedures, which are driven by gradient information estimated on
fractions (batches) of the dataset. While it is commonly accepted that batch
size is an important parameter for offline tuning, the benefits of online
selection of batches remain poorly understood. We investigate online batch
selection strategies for two state-of-the-art methods of stochastic
gradient-based optimization, AdaDelta and Adam. As the loss function to be
minimized for the whole dataset is an aggregation of loss functions of
individual datapoints, intuitively, datapoints with the greatest loss should be
considered (selected in a batch) more frequently. However, the limitations of
this intuition and the proper control of the selection pressure over time are
open questions. We propose a simple strategy where all datapoints are ranked
w.r.t. their latest known loss value and the probability to be selected decays
exponentially as a function of rank. Our experimental results on the MNIST
dataset suggest that selecting batches speeds up both AdaDelta and Adam by a
factor of about 5.
| Ilya Loshchilov and Frank Hutter | null | 1511.06343 | null | null |
How much data is needed to train a medical image deep learning system to
achieve necessary high accuracy? | cs.LG cs.CV cs.NE | The use of Convolutional Neural Networks (CNN) in natural image
classification systems has produced very impressive results. Combined with the
inherent nature of medical images that make them ideal for deep-learning,
further application of such systems to medical image classification holds much
promise. However, the usefulness and potential impact of such a system can be
completely negated if it does not reach a target accuracy. In this paper, we
present a study on determining the optimum size of the training data set
necessary to achieve high classification accuracy with low variance in medical
image classification systems. The CNN was applied to classify axial Computed
Tomography (CT) images into six anatomical classes. We trained the CNN using
six different sizes of training data set (5, 10, 20, 50, 100, and 200) and then
tested the resulting system with a total of 6000 CT images. All images were
acquired from the Massachusetts General Hospital (MGH) Picture Archiving and
Communication System (PACS). Using this data, we employ the learning curve
approach to predict classification accuracy at a given training sample size.
Our research will present a general methodology for determining the training
data set size necessary to achieve a certain target classification accuracy
that can be easily applied to other problems within such systems.
| Junghwan Cho, Kyewook Lee, Ellie Shin, Garry Choy, Synho Do | null | 1511.06348 | null | null |
Generating Sentences from a Continuous Space | cs.LG cs.CL | The standard recurrent neural network language model (RNNLM) generates
sentences one word at a time and does not work from an explicit global sentence
representation. In this work, we introduce and study an RNN-based variational
autoencoder generative model that incorporates distributed latent
representations of entire sentences. This factorization allows it to explicitly
model holistic properties of sentences such as style, topic, and high-level
syntactic features. Samples from the prior over these sentence representations
remarkably produce diverse and well-formed sentences through simple
deterministic decoding. By examining paths through this latent space, we are
able to generate coherent novel sentences that interpolate between known
sentences. We present techniques for solving the difficult learning problem
presented by this model, demonstrate its effectiveness in imputing missing
words, explore many interesting properties of the model's latent sentence
space, and present negative results on the use of the model in language
modeling.
| Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal
Jozefowicz, Samy Bengio | null | 1511.06349 | null | null |
Structured Prediction Energy Networks | cs.LG stat.ML | We introduce structured prediction energy networks (SPENs), a flexible
framework for structured prediction. A deep architecture is used to define an
energy function of candidate labels, and then predictions are produced by using
back-propagation to iteratively optimize the energy with respect to the labels.
This deep architecture captures dependencies between labels that would lead to
intractable graphical models, and performs structure learning by automatically
learning discriminative features of the structured output. One natural
application of our technique is multi-label classification, which traditionally
has required strict prior assumptions about the interactions between labels to
ensure tractable learning and prediction. We are able to apply SPENs to
multi-label problems with substantially larger label sets than previous
applications of structured prediction, while modeling high-order interactions
using minimal structural assumptions. Overall, deep learning provides
remarkable tools for learning features of the inputs to a prediction problem,
and this work extends these techniques to learning features of structured
outputs. Our experiments provide impressive performance on a variety of
benchmark multi-label classification tasks, demonstrate that our technique can
be used to provide interpretable structure learning, and illuminate fundamental
trade-offs between feed-forward and iterative structured prediction.
| David Belanger, Andrew McCallum | null | 1511.06350 | null | null |
Learning Representations Using Complex-Valued Nets | cs.LG cs.NE | Complex-valued neural networks (CVNNs) are an emerging field of research in
neural networks due to their potential representational properties for audio,
image, and physiological signals. It is common in signal processing to
transform sequences of real values to the complex domain via a set of complex
basis functions, such as the Fourier transform. We show how CVNNs can be used
to learn complex representations of real valued time-series data. We present
methods and results using a framework that can compose holomorphic and
non-holomorphic functions in a multi-layer network using a theoretical result
called the Wirtinger derivative. We test our methods on a representation
learning task for real-valued signals, recurrent complex-valued networks and
their real-valued counterparts. Our results show that recurrent complex-valued
networks can perform as well as their real-valued counterparts while learning
filters that are representative of the domain of the data.
| Andy M. Sarroff, Victor Shepardson, Michael A. Casey | null | 1511.06351 | null | null |
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning
and Applications | cs.LG cs.CV | Features based on sparse representation, especially using the synthesis
dictionary model, have been heavily exploited in signal processing and computer
vision. However, synthesis dictionary learning typically involves NP-hard
sparse coding and expensive learning steps. Recently, sparsifying transform
learning received interest for its cheap computation and its optimal updates in
the alternating algorithms. In this work, we develop a methodology for learning
Flipping and Rotation Invariant Sparsifying Transforms, dubbed FRIST, to better
represent natural images that contain textures with various geometrical
directions. The proposed alternating FRIST learning algorithm involves
efficient optimal updates. We provide a convergence guarantee, and demonstrate
the empirical convergence behavior of the proposed FRIST learning approach.
Preliminary experiments show the promising performance of FRIST learning for
sparse image representation, segmentation, denoising, robust inpainting, and
compressed sensing-based magnetic resonance image reconstruction.
| Bihan Wen, Saiprasad Ravishankar, and Yoram Bresler | null | 1511.06359 | null | null |
Order-Embeddings of Images and Language | cs.LG cs.CL cs.CV | Hypernymy, textual entailment, and image captioning can be seen as special
cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure
of this hierarchy. Towards this goal, we introduce a general method for
learning ordered representations, and show how it can be applied to a variety
of tasks involving images and language. We show that the resulting
representations improve performance over current approaches for hypernym
prediction and image-caption retrieval.
| Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun | null | 1511.06361 | null | null |
Efficient inference in occlusion-aware generative models of images | cs.LG cs.CV | We present a generative model of images based on layering, in which image
layers are individually generated, then composited from front to back. We are
thus able to factor the appearance of an image into the appearance of
individual objects within the image --- and additionally for each individual
object, we can factor content from pose. Unlike prior work on layered models,
we learn a shape prior for each object/layer, allowing the model to tease out
which object is in front by looking for a consistent shape, without needing
access to motion cues or any labeled data. We show that ordinary stochastic
gradient variational bayes (SGVB), which optimizes our fully differentiable
lower-bound on the log-likelihood, is sufficient to learn an interpretable
representation of images. Finally we present experiments demonstrating the
effectiveness of the model for inferring foreground and background objects in
images.
| Jonathan Huang and Kevin Murphy | null | 1511.06362 | null | null |
Dynamic Adaptive Network Intelligence | cs.CL cs.LG | Accurate representational learning of both the explicit and implicit
relationships within data is critical to the ability of machines to perform
more complex and abstract reasoning tasks. We describe the efficient weakly
supervised learning of such inferences by our Dynamic Adaptive Network
Intelligence (DANI) model. We report state-of-the-art results for DANI over
question answering tasks in the bAbI dataset that have proved difficult for
contemporary approaches to learning representation (Weston et al., 2015).
| Richard Searle, Megan Bingham-Walker | null | 1511.06379 | null | null |
Unsupervised Learning of Visual Structure using Predictive Generative
Networks | cs.LG cs.AI cs.CV q-bio.NC | The ability to predict future states of the environment is a central pillar
of intelligence. At its core, effective prediction requires an internal model
of the world and an understanding of the rules by which the world changes.
Here, we explore the internal models developed by deep neural networks trained
using a loss based on predicting future frames in synthetic video sequences,
using a CNN-LSTM-deCNN framework. We first show that this architecture can
achieve excellent performance in visual sequence prediction tasks, including
state-of-the-art performance in a standard 'bouncing balls' dataset (Sutskever
et al., 2009). Using a weighted mean-squared error and adversarial loss
(Goodfellow et al., 2014), the same architecture successfully extrapolates
out-of-the-plane rotations of computer-generated faces. Furthermore, despite
being trained end-to-end to predict only pixel-level information, our
Predictive Generative Networks learn a representation of the latent structure
of the underlying three-dimensional objects themselves. Importantly, we find
that this representation is naturally tolerant to object transformations, and
generalizes well to new tasks, such as classification of static images. Similar
models trained solely with a reconstruction loss fail to generalize as
effectively. We argue that prediction can serve as a powerful unsupervised loss
for learning rich internal representations of high-level object features.
| William Lotter, Gabriel Kreiman, David Cox | null | 1511.06380 | null | null |
Manifold Regularized Deep Neural Networks using Adversarial Examples | cs.LG cs.CV | Learning meaningful representations using deep neural networks involves
designing efficient training schemes and well-structured networks. Currently,
the method of stochastic gradient descent that has a momentum with dropout is
one of the most popular training protocols. Based on that, more advanced
methods (i.e., Maxout and Batch Normalization) have been proposed in recent
years, but most still suffer from performance degradation caused by small
perturbations, also known as adversarial examples. To address this issue, we
propose manifold regularized networks (MRnet) that utilize a novel training
objective function that minimizes the difference between multi-layer embedding
results of samples and those adversarial. Our experimental results demonstrated
that MRnet is more resilient to adversarial examples and helps us to generalize
representations on manifolds. Furthermore, combining MRnet and dropout allowed
us to achieve competitive classification performances for three well-known
benchmarks: MNIST, CIFAR-10, and SVHN.
| Taehoon Lee, Minsuk Choi, and Sungroh Yoon | null | 1511.06381 | null | null |
Iterative Refinement of the Approximate Posterior for Directed Belief
Networks | cs.LG stat.ML | Variational methods that rely on a recognition network to approximate the
posterior of directed graphical models offer better inference and learning than
previous methods. Recent advances that exploit the capacity and flexibility in
this approach have expanded what kinds of models can be trained. However, as a
proposal for the posterior, the capacity of the recognition network is limited,
which can constrain the representational power of the generative model and
increase the variance of Monte Carlo estimates. To address these issues, we
introduce an iterative refinement procedure for improving the approximate
posterior of the recognition network and show that training with the refined
posterior is competitive with state-of-the-art methods. The advantages of
refinement are further evident in an increased effective sample size, which
implies a lower variance of gradient estimates.
| R Devon Hjelm and Kyunghyun Cho and Junyoung Chung and Russ
Salakhutdinov and Vince Calhoun and Nebojsa Jojic | null | 1511.06382 | null | null |
A Unified Gradient Regularization Family for Adversarial Examples | cs.LG stat.ML | Adversarial examples are augmented data points generated by imperceptible
perturbation of input samples. They have recently drawn much attention with the
machine learning and data mining community. Being difficult to distinguish from
real examples, such adversarial examples could change the prediction of many of
the best learning models including the state-of-the-art deep learning models.
Recent attempts have been made to build robust models that take into account
adversarial examples. However, these methods can either lead to performance
drops or lack mathematical motivations. In this paper, we propose a unified
framework to build robust machine learning models against adversarial examples.
More specifically, using the unified framework, we develop a family of gradient
regularization methods that effectively penalize the gradient of loss function
w.r.t. inputs. Our proposed framework is appealing in that it offers a unified
view to deal with adversarial examples. It incorporates another
recently-proposed perturbation based approach as a special case. In addition,
we present some visual effects that reveals semantic meaning in those
perturbations, and thus support our regularization method and provide another
explanation for generalizability of adversarial examples. By applying this
technique to Maxout networks, we conduct a series of experiments and achieve
encouraging results on two benchmark datasets. In particular,we attain the best
accuracy on MNIST data (without data augmentation) and competitive performance
on CIFAR-10 data.
| Chunchuan Lyu, Kaizhu Huang, Hai-Ning Liang | null | 1511.06385 | null | null |
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In
Neural Word Embeddings | cs.CL cs.LG | Neural word representations have proven useful in Natural Language Processing
(NLP) tasks due to their ability to efficiently model complex semantic and
syntactic word relationships. However, most techniques model only one
representation per word, despite the fact that a single word can have multiple
meanings or "senses". Some techniques model words by using multiple vectors
that are clustered based on context. However, recent neural approaches rarely
focus on the application to a consuming NLP algorithm. Furthermore, the
training process of recent word-sense models is expensive relative to
single-sense embedding processes. This paper presents a novel approach which
addresses these concerns by modeling multiple embeddings for each word based on
supervised disambiguation, which provides a fast and accurate way for a
consuming NLP model to select a sense-disambiguated embedding. We demonstrate
that these embeddings can disambiguate both contrastive senses such as nominal
and verbal senses as well as nuanced senses such as sarcasm. We further
evaluate Part-of-Speech disambiguated embeddings on neural dependency parsing,
yielding a greater than 8% average error reduction in unlabeled attachment
scores across 6 languages.
| Andrew Trask, Phil Michalak, John Liu | null | 1511.06388 | null | null |
Unsupervised and Semi-supervised Learning with Categorical Generative
Adversarial Networks | stat.ML cs.LG | In this paper we present a method for learning a discriminative classifier
from unlabeled or partially labeled data. Our approach is based on an objective
function that trades-off mutual information between observed examples and their
predicted categorical class distribution, against robustness of the classifier
to an adversarial generative model. The resulting algorithm can either be
interpreted as a natural generalization of the generative adversarial networks
(GAN) framework or as an extension of the regularized information maximization
(RIM) framework to robust classification against an optimal adversary. We
empirically evaluate our method - which we dub categorical generative
adversarial networks (or CatGAN) - on synthetic data as well as on challenging
image classification tasks, demonstrating the robustness of the learned
classifiers. We further qualitatively assess the fidelity of samples generated
by the adversarial generator that is learned alongside the discriminative
classifier, and identify links between the CatGAN objective and discriminative
clustering algorithms (such as RIM).
| Jost Tobias Springenberg | null | 1511.06390 | null | null |
Order Matters: Sequence to sequence for sets | stat.ML cs.CL cs.LG | Sequences have become first class citizens in supervised learning thanks to
the resurgence of recurrent neural networks. Many complex tasks that require
mapping from or to a sequence of observations can now be formulated with the
sequence-to-sequence (seq2seq) framework which employs the chain rule to
efficiently represent the joint probability of sequences. In many cases,
however, variable sized inputs and/or outputs might not be naturally expressed
as sequences. For instance, it is not clear how to input a set of numbers into
a model where the task is to sort them; similarly, we do not know how to
organize outputs when they correspond to random variables and the task is to
model their unknown joint probability. In this paper, we first show using
various examples that the order in which we organize input and/or output data
matters significantly when learning an underlying model. We then discuss an
extension of the seq2seq framework that goes beyond sequences and handles input
sets in a principled way. In addition, we propose a loss which, by searching
over possible orders during training, deals with the lack of structure of
output sets. We show empirical evidence of our claims regarding ordering, and
on the modifications to the seq2seq framework on benchmark language modeling
and parsing tasks, as well as two artificial tasks -- sorting numbers and
estimating the joint probability of unknown graphical models.
| Oriol Vinyals, Samy Bengio, Manjunath Kudlur | null | 1511.06391 | null | null |
Neural Random-Access Machines | cs.LG cs.NE | In this paper, we propose and investigate a new neural network architecture
called Neural Random Access Machine. It can manipulate and dereference pointers
to an external variable-size random-access memory. The model is trained from
pure input-output examples using backpropagation.
We evaluate the new model on a number of simple algorithmic tasks whose
solutions require pointer manipulation and dereferencing. Our results show that
the proposed model can learn to solve algorithmic tasks of such type and is
capable of operating on simple data structures like linked-lists and binary
trees. For easier tasks, the learned solutions generalize to sequences of
arbitrary length. Moreover, memory access during inference can be done in a
constant time under some assumptions.
| Karol Kurach, Marcin Andrychowicz, Ilya Sutskever | null | 1511.06392 | null | null |
Fixed Point Quantization of Deep Convolutional Networks | cs.LG | In recent years increasingly complex architectures for deep convolution
networks (DCNs) have been proposed to boost the performance on image
recognition tasks. However, the gains in performance have come at a cost of
substantial increase in computation and model storage resources. Fixed point
implementation of DCNs has the potential to alleviate some of these
complexities and facilitate potential deployment on embedded hardware. In this
paper, we propose a quantizer design for fixed point implementation of DCNs. We
formulate and solve an optimization problem to identify optimal fixed point
bit-width allocation across DCN layers. Our experiments show that in comparison
to equal bit-width settings, the fixed point DCNs with optimized bit width
allocation offer >20% reduction in the model size without any loss in accuracy
on CIFAR-10 benchmark. We also demonstrate that fine-tuning can further enhance
the accuracy of fixed point DCNs beyond that of the original floating point
model. In doing so, we report a new state-of-the-art fixed point performance of
6.78% error-rate on CIFAR-10 benchmark.
| Darryl D. Lin, Sachin S. Talathi, V. Sreekanth Annapureddy | null | 1511.06393 | null | null |
Geodesics of learned representations | cs.CV cs.LG | We develop a new method for visualizing and refining the invariances of
learned representations. Specifically, we test for a general form of
invariance, linearization, in which the action of a transformation is confined
to a low-dimensional subspace. Given two reference images (typically, differing
by some transformation), we synthesize a sequence of images lying on a path
between them that is of minimal length in the space of the representation (a
"representational geodesic"). If the transformation relating the two reference
images is linearized by the representation, this sequence should follow the
gradual evolution of this transformation. We use this method to assess the
invariance properties of a state-of-the-art image classification network and
find that geodesics generated for image pairs differing by translation,
rotation, and dilation do not evolve according to their associated
transformations. Our method also suggests a remedy for these failures, and
following this prescription, we show that the modified representation is able
to linearize a variety of geometric image transformations.
| Olivier J. H\'enaff and Eero P. Simoncelli | null | 1511.06394 | null | null |
Multilingual Relation Extraction using Compositional Universal Schema | cs.CL cs.LG | Universal schema builds a knowledge base (KB) of entities and relations by
jointly embedding all relation types from input KBs as well as textual patterns
expressing relations from raw text. In most previous applications of universal
schema, each textual pattern is represented as a single embedding, preventing
generalization to unseen patterns. Recent work employs a neural network to
capture patterns' compositional semantics, providing generalization to all
possible input text. In response, this paper introduces significant further
improvements to the coverage and flexibility of universal schema relation
extraction: predictions for entities unseen in training and multilingual
transfer learning to domains with no annotation. We evaluate our model through
extensive experiments on the English and Spanish TAC KBP benchmark,
outperforming the top system from TAC 2013 slot-filling using no handwritten
patterns or additional annotation. We also consider a multilingual setting in
which English training data entities overlap with the seed KB, but Spanish text
does not. Despite having no annotation for Spanish data, we train an accurate
predictor, with additional improvements obtained by tying word embeddings
across languages. Furthermore, we find that multilingual training improves
English relation extraction accuracy. Our approach is thus suited to
broad-coverage automated knowledge base construction in a variety of languages
and domains.
| Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, Andrew
McCallum | null | 1511.06396 | null | null |
Compressing Word Embeddings | cs.CL cs.LG | Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.
| Martin Andrews | null | 1511.06397 | null | null |
Denoising Criterion for Variational Auto-Encoding Framework | cs.LG | Denoising autoencoders (DAE) are trained to reconstruct their clean inputs
with noise injected at the input level, while variational autoencoders (VAE)
are trained with noise injected in their stochastic hidden layer, with a
regularizer that encourages this noise injection. In this paper, we show that
injecting noise both in input and in the stochastic hidden layer can be
advantageous and we propose a modified variational lower bound as an improved
objective function in this setup. When input is corrupted, then the standard
VAE lower bound involves marginalizing the encoder conditional distribution
over the input noise, which makes the training criterion intractable. Instead,
we propose a modified training criterion which corresponds to a tractable bound
when input is corrupted. Experimentally, we find that the proposed denoising
variational autoencoder (DVAE) yields better average log-likelihood than the
VAE and the importance weighted autoencoder on the MNIST and Frey Face
datasets.
| Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, Yoshua Bengio | null | 1511.06406 | null | null |
Recurrent Models for Auditory Attention in Multi-Microphone Distance
Speech Recognition | cs.LG cs.CL | Integration of multiple microphone data is one of the key ways to achieve
robust speech recognition in noisy environments or when the speaker is located
at some distance from the input device. Signal processing techniques such as
beamforming are widely used to extract a speech signal of interest from
background noise. These techniques, however, are highly dependent on prior
spatial information about the microphones and the environment in which the
system is being used. In this work, we present a neural attention network that
directly combines multi-channel audio to generate phonetic states without
requiring any prior knowledge of the microphone layout or any explicit signal
preprocessing for speech enhancement. We embed an attention mechanism within a
Recurrent Neural Network (RNN) based acoustic model to automatically tune its
attention to a more reliable input source. Unlike traditional multi-channel
preprocessing, our system can be optimized towards the desired output in one
step. Although attention-based models have recently achieved impressive results
on sequence-to-sequence learning, no attention mechanisms have previously been
applied to learn potentially asynchronous and non-stationary multiple inputs.
We evaluate our neural attention model on the CHiME-3 challenge task, and show
that the model achieves comparable performance to beamforming using a purely
data-driven method.
| Suyoun Kim, Ian Lane | null | 1511.06407 | null | null |
Learning to Generate Images with Perceptual Similarity Metrics | cs.LG cs.CV | Deep networks are increasingly being applied to problems involving image
synthesis, e.g., generating images from textual descriptions and reconstructing
an input image from a compact representation. Supervised training of
image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the
mismatch between a generated image and its corresponding target image. We
propose instead to use a loss function that is better calibrated to human
perceptual judgments of image quality: the multiscale structural-similarity
score (MS-SSIM). Because MS-SSIM is differentiable, it is easily incorporated
into gradient-descent learning. We compare the consequences of using MS-SSIM
versus PL loss on training deterministic and stochastic autoencoders. For three
different architectures, we collected human judgments of the quality of image
reconstructions. Observers reliably prefer images synthesized by
MS-SSIM-optimized models over those synthesized by PL-optimized models, for two
distinct PL measures ($\ell_1$ and $\ell_2$ distances). We also explore the
effect of training objective on image encoding and analyze conditions under
which perceptually-optimized representations yield better performance on image
classification. Finally, we demonstrate the superiority of
perceptually-optimized networks for super-resolution imaging. Just as computer
vision has advanced through the use of convolutional architectures that mimic
the structure of the mammalian visual system, we argue that significant
additional advances can be made in modeling images through the use of training
objectives that are well aligned to characteristics of human perception.
| Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C.
Mozer, Richard S. Zemel | null | 1511.06409 | null | null |
Better Computer Go Player with Neural Network and Long-term Prediction | cs.LG cs.AI | Competing with top human players in the ancient game of Go has been a
long-term goal of artificial intelligence. Go's high branching factor makes
traditional search techniques ineffective, even on leading-edge hardware, and
Go's evaluation function could change drastically with one stone change. Recent
works [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not
strictly necessary for machine Go players. A pure pattern-matching approach,
based on a Deep Convolutional Neural Network (DCNN) that predicts the next
move, can perform as well as Monte Carlo Tree Search (MCTS)-based open source
Go engines such as Pachi [Baudis & Gailly (2012)] if its search budget is
limited. We extend this idea in our bot named darkforest, which relies on a
DCNN designed for long-term predictions. Darkforest substantially improves the
win rate for pattern-matching approaches against MCTS-based approaches, even
with looser search budgets. Against human players, the newest versions,
darkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a
substantial improvement upon the estimated 4k-5k ranks for DCNN reported in
Clark & Storkey (2015) based on games against other machine players. Adding
MCTS to darkfores2 creates a much stronger player named darkfmcts3: with 5000
rollouts, it beats Pachi with 10k rollouts in all 250 games; with 75k rollouts
it achieves a stable 5d level in KGS server, on par with state-of-the-art Go
AIs (e.g., Zen, DolBaram, CrazyStone) except for AlphaGo [Silver et al.
(2016)]; with 110k rollouts, it won the 3rd place in January KGS Go Tournament.
| Yuandong Tian and Yan Zhu | null | 1511.06410 | null | null |
Training Deep Neural Networks via Direct Loss Minimization | cs.LG | Supervised training of deep neural nets typically relies on minimizing
cross-entropy. However, in many domains, we are interested in performing well
on metrics specific to the application. In this paper we propose a direct loss
minimization approach to train deep neural networks, which provably minimizes
the application-specific loss function. This is often non-trivial, since these
functions are neither smooth nor decomposable and thus are not amenable to
optimization with standard gradient-based methods. We demonstrate the
effectiveness of our approach in the context of maximizing average precision
for ranking problems. Towards this goal, we develop a novel dynamic programming
algorithm that can efficiently compute the weight updates. Our approach proves
superior to a variety of baselines in the context of action classification and
object detection, especially in the presence of label noise.
| Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun | null | 1511.06411 | null | null |
QBDC: Query by dropout committee for training deep supervised
architecture | cs.LG cs.CV | While the current trend is to increase the depth of neural networks to
increase their performance, the size of their training database has to grow
accordingly. We notice an emergence of tremendous databases, although providing
labels to build a training set still remains a very expensive task. We tackle
the problem of selecting the samples to be labelled in an online fashion. In
this paper, we present an active learning strategy based on query by committee
and dropout technique to train a Convolutional Neural Network (CNN). We derive
a commmittee of partial CNNs resulting from batchwise dropout runs on the
initial CNN. We evaluate our active learning strategy for CNN on MNIST
benchmark, showing in particular that selecting less than 30 % from the
annotated database is enough to get similar error rate as using the full
training set on MNIST. We also studied the robustness of our method against
adversarial examples.
| Melanie Ducoffe and Frederic Precioso | null | 1511.06412 | null | null |
Fast Parallel SAME Gibbs Sampling on General Discrete Bayesian Networks | cs.LG stat.ML | A fundamental task in machine learning and related fields is to perform
inference on Bayesian networks. Since exact inference takes exponential time in
general, a variety of approximate methods are used. Gibbs sampling is one of
the most accurate approaches and provides unbiased samples from the posterior
but it has historically been too expensive for large models. In this paper, we
present an optimized, parallel Gibbs sampler augmented with state replication
(SAME or State Augmented Marginal Estimation) to decrease convergence time. We
find that SAME can improve the quality of parameter estimates while
accelerating convergence. Experiments on both synthetic and real data show that
our Gibbs sampler is substantially faster than the state of the art sampler,
JAGS, without sacrificing accuracy. Our ultimate objective is to introduce the
Gibbs sampler to researchers in many fields to expand their range of feasible
inference problems.
| Daniel Seita, Haoyu Chen, and John Canny | null | 1511.06416 | null | null |
Binding via Reconstruction Clustering | cs.LG cs.NE | Disentangled distributed representations of data are desirable for machine
learning, since they are more expressive and can generalize from fewer
examples. However, for complex data, the distributed representations of
multiple objects present in the same input can interfere and lead to
ambiguities, which is commonly referred to as the binding problem. We argue for
the importance of the binding problem to the field of representation learning,
and develop a probabilistic framework that explicitly models inputs as a
composition of multiple objects. We propose an unsupervised algorithm that uses
denoising autoencoders to dynamically bind features together in multi-object
inputs through an Expectation-Maximization-like clustering process. The
effectiveness of this method is demonstrated on artificially generated datasets
of binary images, showing that it can even generalize to bind together new
objects never seen by the autoencoder during training.
| Klaus Greff, Rupesh Kumar Srivastava, J\"urgen Schmidhuber | null | 1511.06418 | null | null |
Canonical Autocorrelation Analysis | stat.ML cs.LG | We present an extension of sparse Canonical Correlation Analysis (CCA)
designed for finding multiple-to-multiple linear correlations within a single
set of variables. Unlike CCA, which finds correlations between two sets of data
where the rows are matched exactly but the columns represent separate sets of
variables, the method proposed here, Canonical Autocorrelation Analysis (CAA),
finds multivariate correlations within just one set of variables. This can be
useful when we look for hidden parsimonious structures in data, each involving
only a small subset of all features. In addition, the discovered correlations
are highly interpretable as they are formed by pairs of sparse linear
combinations of the original features. We show how CAA can be of use as a tool
for anomaly detection when the expected structure of correlations is not
followed by anomalous data. We illustrate the utility of CAA in two application
domains where single-class and unsupervised learning of correlation structures
are particularly relevant: breast cancer diagnosis and radiation threat
detection. When applied to the Wisconsin Breast Cancer data, single-class CAA
is competitive with supervised methods used in literature. On the radiation
threat detection task, unsupervised CAA performs significantly better than an
unsupervised alternative prevalent in the domain, while providing valuable
additional insights for threat analysis.
| Maria De-Arteaga, Artur Dubrawski, Peter Huggins | null | 1511.06419 | null | null |
Skip-Thought Memory Networks | cs.NE cs.CL cs.LG | Question Answering (QA) is fundamental to natural language processing in that
most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly
supervised memory network models that have been proposed so far struggle at
answering questions that involve relations among multiple entities (such as
facebook's bAbi qa5-three-arg-relations in (Weston et al., 2015)). To address
this problem of learning multi-argument multi-hop semantic relations for the
purpose of QA, we propose a method that combines the jointly learned long-term
read-write memory and attentive inference components of end-to-end memory
networks (MemN2N) (Sukhbaatar et al., 2015) with distributed sentence vector
representations encoded by a Skip-Thought model (Kiros et al., 2015). This
choice to append Skip-Thought Vectors to the existing MemN2N framework is
motivated by the fact that Skip-Thought Vectors have been shown to accurately
model multi-argument semantic relations (Kiros et al., 2015).
| Ethan Caballero | null | 1511.06420 | null | null |
Deep Manifold Traversal: Changing Labels with Convolutional Features | cs.LG cs.CV stat.ML | Many tasks in computer vision can be cast as a "label changing" problem,
where the goal is to make a semantic change to the appearance of an image or
some subject in an image in order to alter the class membership. Although
successful task-specific methods have been developed for some label changing
applications, to date no general purpose method exists. Motivated by this we
propose deep manifold traversal, a method that addresses the problem in its
most general form: it first approximates the manifold of natural images then
morphs a test image along a traversal path away from a source class and towards
a target class while staying near the manifold throughout. The resulting
algorithm is surprisingly effective and versatile. It is completely data
driven, requiring only an example set of images from the desired source and
target domains. We demonstrate deep manifold traversal on highly diverse label
changing tasks: changing an individual's appearance (age and hair color),
changing the season of an outdoor image, and transforming a city skyline
towards nighttime.
| Jacob R. Gardner, Paul Upchurch, Matt J. Kusner, Yixuan Li, Kilian Q.
Weinberger, Kavita Bala, John E. Hopcroft | null | 1511.06421 | null | null |
All you need is a good init | cs.LG | Layer-sequential unit-variance (LSUV) initialization - a simple method for
weight initialization for deep net learning - is proposed. The method consists
of the two steps. First, pre-initialize weights of each convolution or
inner-product layer with orthonormal matrices. Second, proceed from the first
to the final layer, normalizing the variance of the output of each layer to be
equal to one.
Experiment with different activation functions (maxout, ReLU-family, tanh)
show that the proposed initialization leads to learning of very deep nets that
(i) produces networks with test accuracy better or equal to standard methods
and (ii) is at least as fast as the complex schemes proposed specifically for
very deep nets such as FitNets (Romero et al. (2015)) and Highway (Srivastava
et al. (2015)).
Performance is evaluated on GoogLeNet, CaffeNet, FitNets and Residual nets
and the state-of-the-art, or very close to it, is achieved on the MNIST,
CIFAR-10/100 and ImageNet datasets.
| Dmytro Mishkin, Jiri Matas | null | 1511.06422 | null | null |
An Information Retrieval Approach to Finding Dependent Subspaces of
Multiple Views | stat.ML cs.LG | Finding relationships between multiple views of data is essential both for
exploratory analysis and as pre-processing for predictive tasks. A prominent
approach is to apply variants of Canonical Correlation Analysis (CCA), a
classical method seeking correlated components between views. The basic CCA is
restricted to maximizing a simple dependency criterion, correlation, measured
directly between data coordinates. We introduce a new method that finds
dependent subspaces of views directly optimized for the data analysis task of
\textit{neighbor retrieval between multiple views}. We optimize mappings for
each view such as linear transformations to maximize cross-view similarity
between neighborhoods of data samples. The criterion arises directly from the
well-defined retrieval task, detects nonlinear and local similarities, is able
to measure dependency of data relationships rather than only individual data
coordinates, and is related to well understood measures of information
retrieval quality. In experiments we show the proposed method outperforms
alternatives in preserving cross-view neighborhood similarities, and yields
insights into local dependencies between multiple views.
| Ziyuan Lin and Jaakko Peltonen | null | 1511.06423 | null | null |
First Step toward Model-Free, Anonymous Object Tracking with Recurrent
Neural Networks | cs.CV cs.LG | In this paper, we propose and study a novel visual object tracking approach
based on convolutional networks and recurrent networks. The proposed approach
is distinct from the existing approaches to visual object tracking, such as
filtering-based ones and tracking-by-detection ones, in the sense that the
tracking system is explicitly trained off-line to track anonymous objects in a
noisy environment. The proposed visual tracking model is end-to-end trainable,
minimizing any adversarial effect from mismatches in object representation and
between the true underlying dynamics and learning dynamics. We empirically show
that the proposed tracking approach works well in various scenarios by
generating artificial video sequences with varying conditions; the number of
objects, amount of noise and the match between the training shapes and test
shapes.
| Quan Gan, Qipeng Guo, Zheng Zhang, Kyunghyun Cho | null | 1511.06425 | null | null |
A Controller-Recognizer Framework: How necessary is recognition for
control? | cs.LG cs.CV | Recently there has been growing interest in building active visual object
recognizers, as opposed to the usual passive recognizers which classifies a
given static image into a predefined set of object categories. In this paper we
propose to generalize these recently proposed end-to-end active visual
recognizers into a controller-recognizer framework. A model in the
controller-recognizer framework consists of a controller, which interfaces with
an external manipulator, and a recognizer which classifies the visual input
adjusted by the manipulator. We describe two most recently proposed
controller-recognizer models: recurrent attention model and spatial transformer
network as representative examples of controller-recognizer models. Based on
this description we observe that most existing end-to-end
controller-recognizers tightly, or completely, couple a controller and
recognizer. We ask a question whether this tight coupling is necessary, and try
to answer this empirically by building a controller-recognizer model with a
decoupled controller and recognizer. Our experiments revealed that it is not
always necessary to tightly couple them and that by decoupling a controller and
recognizer, there is a possibility of building a generic controller that is
pretrained and works together with any subsequent recognizer.
| Marcin Moczulski, Kelvin Xu, Aaron Courville, Kyunghyun Cho | null | 1511.06428 | null | null |
Patterns for Learning with Side Information | cs.LG stat.ML | Supervised, semi-supervised, and unsupervised learning estimate a function
given input/output samples. Generalization of the learned function to unseen
data can be improved by incorporating side information into learning. Side
information are data that are neither from the input space nor from the output
space of the function, but include useful information for learning it. In this
paper we show that learning with side information subsumes a variety of related
approaches, e.g. multi-task learning, multi-view learning and learning using
privileged information. Our main contributions are (i) a new perspective that
connects these previously isolated approaches, (ii) insights about how these
methods incorporate different types of prior knowledge, and hence implement
different patterns, (iii) facilitating the application of these methods in
novel tasks, as well as (iv) a systematic experimental evaluation of these
patterns in two supervised learning tasks.
| Rico Jonschkowski, Sebastian H\"ofer, Oliver Brock | null | 1511.06429 | null | null |
Deconstructing the Ladder Network Architecture | cs.LG | The Manual labeling of data is and will remain a costly endeavor. For this
reason, semi-supervised learning remains a topic of practical importance. The
recently proposed Ladder Network is one such approach that has proven to be
very successful. In addition to the supervised objective, the Ladder Network
also adds an unsupervised objective corresponding to the reconstruction costs
of a stack of denoising autoencoders. Although the empirical results are
impressive, the Ladder Network has many components intertwined, whose
contributions are not obvious in such a complex architecture. In order to help
elucidate and disentangle the different ingredients in the Ladder Network
recipe, this paper presents an extensive experimental investigation of variants
of the Ladder Network in which we replace or remove individual components to
gain more insight into their relative importance. We find that all of the
components are necessary for achieving optimal performance, but they do not
contribute equally. For semi-supervised tasks, we conclude that the most
important contribution is made by the lateral connection, followed by the
application of noise, and finally the choice of what we refer to as the
`combinator function' in the decoder path. We also find that as the number of
labeled training examples increases, the lateral connections and reconstruction
criterion become less important, with most of the improvement in generalization
being due to the injection of noise in each layer. Furthermore, we present a
new type of combinator function that outperforms the original design in both
fully- and semi-supervised tasks, reducing record test error rates on
Permutation-Invariant MNIST to 0.57% for the supervised setting, and to 0.97%
and 1.0% for semi-supervised settings with 1000 and 100 labeled examples
respectively.
| Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua
Bengio | null | 1511.06430 | null | null |
Delving Deeper into Convolutional Networks for Learning Video
Representations | cs.CV cs.LG cs.NE | We propose an approach to learn spatio-temporal features in videos from
intermediate visual representations we call "percepts" using
Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts
that are extracted from all level of a deep convolutional network trained on
the large ImageNet dataset. While high-level percepts contain highly
discriminative information, they tend to have a low-spatial resolution.
Low-level percepts, on the other hand, preserve a higher spatial resolution
from which we can model finer motion patterns. Using low-level percepts can
leads to high-dimensionality video representations. To mitigate this effect and
control the model number of parameters, we introduce a variant of the GRU model
that leverages the convolution operations to enforce sparse connectivity of the
model units and share parameters across the input spatial locations.
We empirically validate our approach on both Human Action Recognition and
Video Captioning tasks. In particular, we achieve results equivalent to
state-of-art on the YouTube2Text dataset using a simpler text-decoder model and
without extra 3D CNN features.
| Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville | null | 1511.06432 | null | null |
Blending LSTMs into CNNs | cs.LG | We consider whether deep convolutional networks (CNNs) can represent decision
functions with similar accuracy as recurrent networks such as LSTMs. First, we
show that a deep CNN with an architecture inspired by the models recently
introduced in image recognition can yield better accuracy than previous
convolutional and LSTM networks on the standard 309h Switchboard automatic
speech recognition task. Then we show that even more accurate CNNs can be
trained under the guidance of LSTMs using a variant of model compression, which
we call model blending because the teacher and student models are similar in
complexity but different in inductive bias. Blending further improves the
accuracy of our CNN, yielding a computationally efficient model of accuracy
higher than any of the other individual models. Examining the effect of "dark
knowledge" in this model compression task, we find that less than 1% of the
highest probability labels are needed for accurate model compression.
| Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban,
Shengjie Wang, Ozlem Aslan, Matthai Philipose, Matthew Richardson, Charles
Sutton | null | 1511.06433 | null | null |
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