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Sequential Local Learning for Latent Graphical Models | cs.LG stat.ML | Learning parameters of latent graphical models (GM) is inherently much harder
than that of no-latent ones since the latent variables make the corresponding
log-likelihood non-concave. Nevertheless, expectation-maximization schemes are
popularly used in practice, but they are typically stuck in local optima. In
the recent years, the method of moments have provided a refreshing angle for
resolving the non-convex issue, but it is applicable to a quite limited class
of latent GMs. In this paper, we aim for enhancing its power via enlarging such
a class of latent GMs. To this end, we introduce two novel concepts, coined
marginalization and conditioning, which can reduce the problem of learning a
larger GM to that of a smaller one. More importantly, they lead to a sequential
learning framework that repeatedly increases the learning portion of given
latent GM, and thus covers a significantly broader and more complicated class
of loopy latent GMs which include convolutional and random regular models.
| Sejun Park, Eunho Yang, Jinwoo Shin | null | 1703.04082 | null | null |
Autoregressive Convolutional Neural Networks for Asynchronous Time
Series | cs.LG | We propose Significance-Offset Convolutional Neural Network, a deep
convolutional network architecture for regression of multivariate asynchronous
time series. The model is inspired by standard autoregressive (AR) models and
gating mechanisms used in recurrent neural networks. It involves an AR-like
weighting system, where the final predictor is obtained as a weighted sum of
adjusted regressors, while the weights are datadependent functions learnt
through a convolutional network. The architecture was designed for applications
on asynchronous time series and is evaluated on such datasets: a hedge fund
proprietary dataset of over 2 million quotes for a credit derivative index, an
artificially generated noisy autoregressive series and UCI household
electricity consumption dataset. The proposed architecture achieves promising
results as compared to convolutional and recurrent neural networks.
| Miko{\l}aj Bi\'nkowski, Gautier Marti, Philippe Donnat | null | 1703.04122 | null | null |
Multiscale Hierarchical Convolutional Networks | cs.LG stat.ML | Deep neural network algorithms are difficult to analyze because they lack
structure allowing to understand the properties of underlying transforms and
invariants. Multiscale hierarchical convolutional networks are structured deep
convolutional networks where layers are indexed by progressively higher
dimensional attributes, which are learned from training data. Each new layer is
computed with multidimensional convolutions along spatial and attribute
variables. We introduce an efficient implementation of such networks where the
dimensionality is progressively reduced by averaging intermediate layers along
attribute indices. Hierarchical networks are tested on CIFAR image data bases
where they obtain comparable precisions to state of the art networks, with much
fewer parameters. We study some properties of the attributes learned from these
databases.
| J\"orn-Henrik Jacobsen, Edouard Oyallon, St\'ephane Mallat, Arnold
W.M. Smeulders | null | 1703.0414 | null | null |
Leak Event Identification in Water Systems Using High Order CRF | cs.LG | Today, detection of anomalous events in civil infrastructures (e.g. water
pipe breaks and leaks) is time consuming and often takes hours or days. Pipe
breakage as one of the most frequent types of failure of water networks often
causes community disruptions ranging from temporary interruptions in services
to extended loss of business and relocation of residents. In this project, we
design and implement a two-phase approach for leak event identification, which
leverages dynamic data from multiple information sources including IoT sensing
data (pressure values and/or flow rates), geophysical data (water systems), and
human inputs (tweets posted on Twitter). In the approach, a high order
Conditional Random Field (CRF) is constructed that enforces predictions based
on IoT observations consistent with human inputs to improve the performance of
event identifications.
Considering the physical water network as a graph, a CRF model is built and
learned by the Structured Support Vector Machine (SSVM) using node features
such as water pressure and flow rate. After that, we built the high order CRF
system by enforcing twitter leakage detection information. An optimal inference
algorithm is proposed for the adapted high order CRF model. Experimental
results show the effectiveness of our system.
| Qing Han, Wentao Zhu and Yang Shi | null | 1703.0417 | null | null |
Continual Learning Through Synaptic Intelligence | cs.LG q-bio.NC stat.ML | While deep learning has led to remarkable advances across diverse
applications, it struggles in domains where the data distribution changes over
the course of learning. In stark contrast, biological neural networks
continually adapt to changing domains, possibly by leveraging complex molecular
machinery to solve many tasks simultaneously. In this study, we introduce
intelligent synapses that bring some of this biological complexity into
artificial neural networks. Each synapse accumulates task relevant information
over time, and exploits this information to rapidly store new memories without
forgetting old ones. We evaluate our approach on continual learning of
classification tasks, and show that it dramatically reduces forgetting while
maintaining computational efficiency.
| Friedemann Zenke, Ben Poole, Surya Ganguli | null | 1703.042 | null | null |
SPARTan: Scalable PARAFAC2 for Large & Sparse Data | cs.LG cs.NA | In exploratory tensor mining, a common problem is how to analyze a set of
variables across a set of subjects whose observations do not align naturally.
For example, when modeling medical features across a set of patients, the
number and duration of treatments may vary widely in time, meaning there is no
meaningful way to align their clinical records across time points for analysis
purposes. To handle such data, the state-of-the-art tensor model is the
so-called PARAFAC2, which yields interpretable and robust output and can
naturally handle sparse data. However, its main limitation up to now has been
the lack of efficient algorithms that can handle large-scale datasets.
In this work, we fill this gap by developing a scalable method to compute the
PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method
exploits special structure within PARAFAC2, leading to a novel algorithmic
reformulation that is both fast (in absolute time) and more memory-efficient
than prior work. We evaluate SPARTan on both synthetic and real datasets,
showing 22X performance gains over the best previous implementation and also
handling larger problem instances for which the baseline fails. Furthermore, we
are able to apply SPARTan to the mining of temporally-evolving phenotypes on
data taken from real and medically complex pediatric patients. The clinical
meaningfulness of the phenotypes identified in this process, as well as their
temporal evolution over time for several patients, have been endorsed by
clinical experts.
| Ioakeim Perros and Evangelos E. Papalexakis and Fei Wang and Richard
Vuduc and Elizabeth Searles and Michael Thompson and Jimeng Sun | null | 1703.04219 | null | null |
Conjugate-Computation Variational Inference : Converting Variational
Inference in Non-Conjugate Models to Inferences in Conjugate Models | cs.LG | Variational inference is computationally challenging in models that contain
both conjugate and non-conjugate terms. Methods specifically designed for
conjugate models, even though computationally efficient, find it difficult to
deal with non-conjugate terms. On the other hand, stochastic-gradient methods
can handle the non-conjugate terms but they usually ignore the conjugate
structure of the model which might result in slow convergence. In this paper,
we propose a new algorithm called Conjugate-computation Variational Inference
(CVI) which brings the best of the two worlds together -- it uses conjugate
computations for the conjugate terms and employs stochastic gradients for the
rest. We derive this algorithm by using a stochastic mirror-descent method in
the mean-parameter space, and then expressing each gradient step as a
variational inference in a conjugate model. We demonstrate our algorithm's
applicability to a large class of models and establish its convergence. Our
experimental results show that our method converges much faster than the
methods that ignore the conjugate structure of the model.
| Mohammad Emtiyaz Khan and Wu Lin | null | 1703.04265 | null | null |
Online Learning with Local Permutations and Delayed Feedback | cs.LG | We propose an Online Learning with Local Permutations (OLLP) setting, in
which the learner is allowed to slightly permute the \emph{order} of the loss
functions generated by an adversary. On one hand, this models natural
situations where the exact order of the learner's responses is not crucial, and
on the other hand, might allow better learning and regret performance, by
mitigating highly adversarial loss sequences. Also, with random permutations,
this can be seen as a setting interpolating between adversarial and stochastic
losses. In this paper, we consider the applicability of this setting to convex
online learning with delayed feedback, in which the feedback on the prediction
made in round $t$ arrives with some delay $\tau$. With such delayed feedback,
the best possible regret bound is well-known to be $O(\sqrt{\tau T})$. We prove
that by being able to permute losses by a distance of at most $M$ (for $M\geq
\tau$), the regret can be improved to $O(\sqrt{T}(1+\sqrt{\tau^2/M}))$, using a
Mirror-Descent based algorithm which can be applied for both Euclidean and
non-Euclidean geometries. We also prove a lower bound, showing that for
$M<\tau/3$, it is impossible to improve the standard $O(\sqrt{\tau T})$ regret
bound by more than constant factors. Finally, we provide some experiments
validating the performance of our algorithm.
| Ohad Shamir, Liran Szlak | null | 1703.04274 | null | null |
Blocking Transferability of Adversarial Examples in Black-Box Learning
Systems | cs.LG | Advances in Machine Learning (ML) have led to its adoption as an integral
component in many applications, including banking, medical diagnosis, and
driverless cars. To further broaden the use of ML models, cloud-based services
offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service
tools as black-box systems. However, ML classifiers are vulnerable to
adversarial examples: inputs that are maliciously modified can cause the
classifier to provide adversary-desired outputs. Moreover, it is known that
adversarial examples generated on one classifier are likely to cause another
classifier to make the same mistake, even if the classifiers have different
architectures or are trained on disjoint datasets. This property, which is
known as transferability, opens up the possibility of attacking black-box
systems by generating adversarial examples on a substitute classifier and
transferring the examples to the target classifier. Therefore, the key to
protect black-box learning systems against the adversarial examples is to block
their transferability. To this end, we propose a training method that, as the
input is more perturbed, the classifier smoothly outputs lower confidence on
the original label and instead predicts that the input is "invalid". In
essence, we augment the output class set with a NULL label and train the
classifier to reject the adversarial examples by classifying them as NULL. In
experiments, we apply a wide range of attacks based on adversarial examples on
the black-box systems. We show that a classifier trained with the proposed
method effectively resists against the adversarial examples, while maintaining
the accuracy on clean data.
| Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang and Radha
Poovendran | null | 1703.04318 | null | null |
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured
Outputs | cs.LG cs.AI cs.CV | We approach structured output prediction by optimizing a deep value network
(DVN) to precisely estimate the task loss on different output configurations
for a given input. Once the model is trained, we perform inference by gradient
descent on the continuous relaxations of the output variables to find outputs
with promising scores from the value network. When applied to image
segmentation, the value network takes an image and a segmentation mask as
inputs and predicts a scalar estimating the intersection over union between the
input and ground truth masks. For multi-label classification, the DVN's
objective is to correctly predict the F1 score for any potential label
configuration. The DVN framework achieves the state-of-the-art results on
multi-label prediction and image segmentation benchmarks.
| Michael Gygli, Mohammad Norouzi, Anelia Angelova | null | 1703.04363 | null | null |
Langevin Dynamics with Continuous Tempering for Training Deep Neural
Networks | cs.LG stat.ML | Minimizing non-convex and high-dimensional objective functions is
challenging, especially when training modern deep neural networks. In this
paper, a novel approach is proposed which divides the training process into two
consecutive phases to obtain better generalization performance: Bayesian
sampling and stochastic optimization. The first phase is to explore the energy
landscape and to capture the "fat" modes; and the second one is to fine-tune
the parameter learned from the first phase. In the Bayesian learning phase, we
apply continuous tempering and stochastic approximation into the Langevin
dynamics to create an efficient and effective sampler, in which the temperature
is adjusted automatically according to the designed "temperature dynamics".
These strategies can overcome the challenge of early trapping into bad local
minima and have achieved remarkable improvements in various types of neural
networks as shown in our theoretical analysis and empirical experiments.
| Nanyang Ye, Zhanxing Zhu, Rafal K. Mantiuk | null | 1703.04379 | null | null |
Bayesian Optimization with Gradients | stat.ML cs.AI cs.LG math.OC | Bayesian optimization has been successful at global optimization of
expensive-to-evaluate multimodal objective functions. However, unlike most
optimization methods, Bayesian optimization typically does not use derivative
information. In this paper we show how Bayesian optimization can exploit
derivative information to decrease the number of objective function evaluations
required for good performance. In particular, we develop a novel Bayesian
optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for
which we show one-step Bayes-optimality, asymptotic consistency, and greater
one-step value of information than is possible in the derivative-free setting.
Our procedure accommodates noisy and incomplete derivative information, comes
in both sequential and batch forms, and can optionally reduce the computational
cost of inference through automatically selected retention of a single
directional derivative. We also compute the d-KG acquisition function and its
gradient using a novel fast discretization-free technique. We show d-KG
provides state-of-the-art performance compared to a wide range of optimization
procedures with and without gradients, on benchmarks including logistic
regression, deep learning, kernel learning, and k-nearest neighbors.
| Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, and Peter I. Frazier | null | 1703.04389 | null | null |
Social Fingerprinting: detection of spambot groups through DNA-inspired
behavioral modeling | cs.SI cs.CR cs.LG | Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics.
| Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo
Spognardi, Maurizio Tesconi | 10.1109/TDSC.2017.2681672 | 1703.04482 | null | null |
Task-based End-to-end Model Learning in Stochastic Optimization | cs.LG cs.AI | With the increasing popularity of machine learning techniques, it has become
common to see prediction algorithms operating within some larger process.
However, the criteria by which we train these algorithms often differ from the
ultimate criteria on which we evaluate them. This paper proposes an end-to-end
approach for learning probabilistic machine learning models in a manner that
directly captures the ultimate task-based objective for which they will be
used, within the context of stochastic programming. We present three
experimental evaluations of the proposed approach: a classical inventory stock
problem, a real-world electrical grid scheduling task, and a real-world energy
storage arbitrage task. We show that the proposed approach can outperform both
traditional modeling and purely black-box policy optimization approaches in
these applications.
| Priya L. Donti and Brandon Amos and J. Zico Kolter | null | 1703.04529 | null | null |
Sensor Fusion for Robot Control through Deep Reinforcement Learning | cs.RO cs.LG cs.NE cs.SY | Deep reinforcement learning is becoming increasingly popular for robot
control algorithms, with the aim for a robot to self-learn useful feature
representations from unstructured sensory input leading to the optimal
actuation policy. In addition to sensors mounted on the robot, sensors might
also be deployed in the environment, although these might need to be accessed
via an unreliable wireless connection. In this paper, we demonstrate deep
neural network architectures that are able to fuse information coming from
multiple sensors and are robust to sensor failures at runtime. We evaluate our
method on a search and pick task for a robot both in simulation and the real
world.
| Steven Bohez, Tim Verbelen, Elias De Coninck, Bert Vankeirsbilck,
Pieter Simoens, Bart Dhoedt | null | 1703.0455 | null | null |
Optimal Densification for Fast and Accurate Minwise Hashing | cs.DS cs.LG | Minwise hashing is a fundamental and one of the most successful hashing
algorithm in the literature. Recent advances based on the idea of
densification~\cite{Proc:OneHashLSH_ICML14,Proc:Shrivastava_UAI14} have shown
that it is possible to compute $k$ minwise hashes, of a vector with $d$
nonzeros, in mere $(d + k)$ computations, a significant improvement over the
classical $O(dk)$. These advances have led to an algorithmic improvement in the
query complexity of traditional indexing algorithms based on minwise hashing.
Unfortunately, the variance of the current densification techniques is
unnecessarily high, which leads to significantly poor accuracy compared to
vanilla minwise hashing, especially when the data is sparse. In this paper, we
provide a novel densification scheme which relies on carefully tailored
2-universal hashes. We show that the proposed scheme is variance-optimal, and
without losing the runtime efficiency, it is significantly more accurate than
existing densification techniques. As a result, we obtain a significantly
efficient hashing scheme which has the same variance and collision probability
as minwise hashing. Experimental evaluations on real sparse and
high-dimensional datasets validate our claims. We believe that given the
significant advantages, our method will replace minwise hashing implementations
in practice.
| Anshumali Shrivastava | null | 1703.04664 | null | null |
On the benefits of output sparsity for multi-label classification | math.ST cs.LG stat.ML stat.TH | The multi-label classification framework, where each observation can be
associated with a set of labels, has generated a tremendous amount of attention
over recent years. The modern multi-label problems are typically large-scale in
terms of number of observations, features and labels, and the amount of labels
can even be comparable with the amount of observations. In this context,
different remedies have been proposed to overcome the curse of dimensionality.
In this work, we aim at exploiting the output sparsity by introducing a new
loss, called the sparse weighted Hamming loss. This proposed loss can be seen
as a weighted version of classical ones, where active and inactive labels are
weighted separately. Leveraging the influence of sparsity in the loss function,
we provide improved generalization bounds for the empirical risk minimizer, a
suitable property for large-scale problems. For this new loss, we derive rates
of convergence linear in the underlying output-sparsity rather than linear in
the number of labels. In practice, minimizing the associated risk can be
performed efficiently by using convex surrogates and modern convex optimization
algorithms. We provide experiments on various real-world datasets demonstrating
the pertinence of our approach when compared to non-weighted techniques.
| Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Joseph Salmon | null | 1703.04697 | null | null |
Tree Memory Networks for Modelling Long-term Temporal Dependencies | cs.LG cs.CV cs.NE | In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully transitioned to application areas such as trajectory prediction,
which require capturing both short term and long term relationships. In this
paper, we propose a Tree Memory Network (TMN) for modelling long term and short
term relationships in sequence-to-sequence mapping problems. The proposed
network architecture is composed of an input module, controller and a memory
module. In contrast to related literature, which models the memory as a
sequence of historical states, we model the memory as a recursive tree
structure. This structure more effectively captures temporal dependencies
across both short term and long term sequences using its hierarchical
structure. We demonstrate the effectiveness and flexibility of the proposed TMN
in two practical problems, aircraft trajectory modelling and pedestrian
trajectory modelling in a surveillance setting, and in both cases we outperform
the current state-of-the-art. Furthermore, we perform an in depth analysis on
the evolution of the memory module content over time and provide visual
evidence on how the proposed TMN is able to map both long term and short term
relationships efficiently via a hierarchical structure.
| Tharindu Fernando, Simon Denman, Aaron McFadyen, Sridha Sridharan and
Clinton Fookes | null | 1703.04706 | null | null |
Understanding Black-box Predictions via Influence Functions | stat.ML cs.AI cs.LG | How can we explain the predictions of a black-box model? In this paper, we
use influence functions -- a classic technique from robust statistics -- to
trace a model's prediction through the learning algorithm and back to its
training data, thereby identifying training points most responsible for a given
prediction. To scale up influence functions to modern machine learning
settings, we develop a simple, efficient implementation that requires only
oracle access to gradients and Hessian-vector products. We show that even on
non-convex and non-differentiable models where the theory breaks down,
approximations to influence functions can still provide valuable information.
On linear models and convolutional neural networks, we demonstrate that
influence functions are useful for multiple purposes: understanding model
behavior, debugging models, detecting dataset errors, and even creating
visually-indistinguishable training-set attacks.
| Pang Wei Koh and Percy Liang | null | 1703.0473 | null | null |
Weighted Voting Via No-Regret Learning | cs.GT cs.AI cs.LG cs.MA | Voting systems typically treat all voters equally. We argue that perhaps they
should not: Voters who have supported good choices in the past should be given
higher weight than voters who have supported bad ones. To develop a formal
framework for desirable weighting schemes, we draw on no-regret learning.
Specifically, given a voting rule, we wish to design a weighting scheme such
that applying the voting rule, with voters weighted by the scheme, leads to
choices that are almost as good as those endorsed by the best voter in
hindsight. We derive possibility and impossibility results for the existence of
such weighting schemes, depending on whether the voting rule and the weighting
scheme are deterministic or randomized, as well as on the social choice axioms
satisfied by the voting rule.
| Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia | null | 1703.04756 | null | null |
Separation of time scales and direct computation of weights in deep
neural networks | cs.LG physics.data-an stat.ML | Artificial intelligence is revolutionizing our lives at an ever increasing
pace. At the heart of this revolution is the recent advancements in deep neural
networks (DNN), learning to perform sophisticated, high-level tasks. However,
training DNNs requires massive amounts of data and is very computationally
intensive. Gaining analytical understanding of the solutions found by DNNs can
help us devise more efficient training algorithms, replacing the commonly used
mthod of stochastic gradient descent (SGD). We analyze the dynamics of SGD and
show that, indeed, direct computation of the solutions is possible in many
cases. We show that a high performing setup used in DNNs introduces a
separation of time-scales in the training dynamics, allowing SGD to train
layers from the lowest (closest to input) to the highest. We then show that for
each layer, the distribution of solutions found by SGD can be estimated using a
class-based principal component analysis (PCA) of the layer's input. This
finding allows us to forgo SGD entirely and directly derive the DNN parameters
using this class-based PCA, which can be well estimated using significantly
less data than SGD. We implement these results on image datasets MNIST, CIFAR10
and CIFAR100 and find that, in fact, layers derived using our class-based PCA
perform comparable or superior to neural networks of the same size and
architecture trained using SGD. We also confirm that the class-based PCA often
converges using a fraction of the data required for SGD. Thus, using our method
training time can be reduced both by requiring less training data than SGD, and
by eliminating layers in the costly backpropagation step of the training.
| Nima Dehmamy, Neda Rohani and Aggelos Katsaggelos | null | 1703.04757 | null | null |
Audio Scene Classification with Deep Recurrent Neural Networks | cs.SD cs.LG | We introduce in this work an efficient approach for audio scene
classification using deep recurrent neural networks. An audio scene is firstly
transformed into a sequence of high-level label tree embedding feature vectors.
The vector sequence is then divided into multiple subsequences on which a deep
GRU-based recurrent neural network is trained for sequence-to-label
classification. The global predicted label for the entire sequence is finally
obtained via aggregation of subsequence classification outputs. We will show
that our approach obtains an F1-score of 97.7% on the LITIS Rouen dataset,
which is the largest dataset publicly available for the task. Compared to the
best previously reported result on the dataset, our approach is able to reduce
the relative classification error by 35.3%.
| Huy Phan, Philipp Koch, Fabrice Katzberg, Marco Maass, Radoslaw Mazur,
Alfred Mertins | null | 1703.0477 | null | null |
Discriminate-and-Rectify Encoders: Learning from Image Transformation
Sets | cs.CV cs.LG stat.ML | The complexity of a learning task is increased by transformations in the
input space that preserve class identity. Visual object recognition for example
is affected by changes in viewpoint, scale, illumination or planar
transformations. While drastically altering the visual appearance, these
changes are orthogonal to recognition and should not be reflected in the
representation or feature encoding used for learning. We introduce a framework
for weakly supervised learning of image embeddings that are robust to
transformations and selective to the class distribution, using sets of
transforming examples (orbit sets), deep parametrizations and a novel
orbit-based loss. The proposed loss combines a discriminative, contrastive part
for orbits with a reconstruction error that learns to rectify orbit
transformations. The learned embeddings are evaluated in distance metric-based
tasks, such as one-shot classification under geometric transformations, as well
as face verification and retrieval under more realistic visual variability. Our
results suggest that orbit sets, suitably computed or observed, can be used for
efficient, weakly-supervised learning of semantically relevant image
embeddings.
| Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos | null | 1703.04775 | null | null |
Online Learning Rate Adaptation with Hypergradient Descent | cs.LG stat.ML | We introduce a general method for improving the convergence rate of
gradient-based optimizers that is easy to implement and works well in practice.
We demonstrate the effectiveness of the method in a range of optimization
problems by applying it to stochastic gradient descent, stochastic gradient
descent with Nesterov momentum, and Adam, showing that it significantly reduces
the need for the manual tuning of the initial learning rate for these commonly
used algorithms. Our method works by dynamically updating the learning rate
during optimization using the gradient with respect to the learning rate of the
update rule itself. Computing this "hypergradient" needs little additional
computation, requires only one extra copy of the original gradient to be stored
in memory, and relies upon nothing more than what is provided by reverse-mode
automatic differentiation.
| Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark
Schmidt, Frank Wood | null | 1703.04782 | null | null |
Distributed Dual Coordinate Ascent in General Tree Networks and
Communication Network Effect on Synchronous Machine Learning | cs.DC cs.IT cs.LG math.IT | Due to the big size of data and limited data storage volume of a single
computer or a single server, data are often stored in a distributed manner.
Thus, performing large-scale machine learning operations with the distributed
datasets through communication networks is often required. In this paper, we
study the convergence rate of the distributed dual coordinate ascent for
distributed machine learning problems in a general tree-structured network.
Since a tree network model can be understood as the generalization of a star
network model, our algorithm can be thought of as the generalization of the
distributed dual coordinate ascent in a star network model. We provide the
convergence rate of the distributed dual coordinate ascent over a general tree
network in a recursive manner and analyze the network effect on the convergence
rate. Secondly, by considering network communication delays, we optimize the
distributed dual coordinate ascent algorithm to maximize its convergence speed.
From our analytical result, we can choose the optimal number of local
iterations depending on the communication delay severity to achieve the fastest
convergence speed. In numerical experiments, we consider machine learning
scenarios over communication networks, where local workers cannot directly
reach to a central node due to constraints in communication, and demonstrate
that the usability of our distributed dual coordinate ascent algorithm in tree
networks. Additionally, we show that adapting number of local and global
iterations to network communication delays in the distributed dual coordinated
ascent algorithm can improve its convergence speed.
| Myung Cho, Lifeng Lai, Weiyu Xu | null | 1703.04785 | null | null |
Learned Optimizers that Scale and Generalize | cs.LG cs.NE stat.ML | Learning to learn has emerged as an important direction for achieving
artificial intelligence. Two of the primary barriers to its adoption are an
inability to scale to larger problems and a limited ability to generalize to
new tasks. We introduce a learned gradient descent optimizer that generalizes
well to new tasks, and which has significantly reduced memory and computation
overhead. We achieve this by introducing a novel hierarchical RNN architecture,
with minimal per-parameter overhead, augmented with additional architectural
features that mirror the known structure of optimization tasks. We also develop
a meta-training ensemble of small, diverse optimization tasks capturing common
properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM
on problems in this corpus. More importantly, it performs comparably or better
when applied to small convolutional neural networks, despite seeing no neural
networks in its meta-training set. Finally, it generalizes to train Inception
V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps,
optimization problems that are of a vastly different scale than those it was
trained on. We release an open source implementation of the meta-training
algorithm.
| Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio
Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein | null | 1703.04813 | null | null |
Neural Graph Machines: Learning Neural Networks Using Graphs | cs.LG cs.NE | Label propagation is a powerful and flexible semi-supervised learning
technique on graphs. Neural networks, on the other hand, have proven track
records in many supervised learning tasks. In this work, we propose a training
framework with a graph-regularised objective, namely "Neural Graph Machines",
that can combine the power of neural networks and label propagation. This work
generalises previous literature on graph-augmented training of neural networks,
enabling it to be applied to multiple neural architectures (Feed-forward NNs,
CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the
neural networks to harness both labeled and unlabeled data by: (a) allowing the
network to train using labeled data as in the supervised setting, (b) biasing
the network to learn similar hidden representations for neighboring nodes on a
graph, in the same vein as label propagation. Such architectures with the
proposed objective can be trained efficiently using stochastic gradient descent
and scaled to large graphs, with a runtime that is linear in the number of
edges. The proposed joint training approach convincingly outperforms many
existing methods on a wide range of tasks (multi-label classification on social
graphs, news categorization, document classification and semantic intent
classification), with multiple forms of graph inputs (including graphs with and
without node-level features) and using different types of neural networks.
| Thang D. Bui, Sujith Ravi, Vivek Ramavajjala | null | 1703.04818 | null | null |
Classification in biological networks with hypergraphlet kernels | stat.ML cs.LG | Biological and cellular systems are often modeled as graphs in which vertices
represent objects of interest (genes, proteins, drugs) and edges represent
relational ties among these objects (binds-to, interacts-with, regulates). This
approach has been highly successful owing to the theory, methodology and
software that support analysis and learning on graphs. Graphs, however, often
suffer from information loss when modeling physical systems due to their
inability to accurately represent multiobject relationships. Hypergraphs, a
generalization of graphs, provide a framework to mitigate information loss and
unify disparate graph-based methodologies. In this paper, we present a
hypergraph-based approach for modeling physical systems and formulate vertex
classification, edge classification and link prediction problems on
(hyper)graphs as instances of vertex classification on (extended, dual)
hypergraphs in a semi-supervised setting. We introduce a novel kernel method on
vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The
method is based on exact and inexact (via hypergraph edit distances)
enumeration of small simple hypergraphs, referred to as hypergraphlets, rooted
at a vertex of interest. We extensively evaluate this method and show its
potential use in a positive-unlabeled setting to estimate the number of missing
and false positive links in protein-protein interaction networks.
| Jose Lugo-Martinez and Predrag Radivojac | null | 1703.04823 | null | null |
Encoding Sentences with Graph Convolutional Networks for Semantic Role
Labeling | cs.CL cs.LG | Semantic role labeling (SRL) is the task of identifying the
predicate-argument structure of a sentence. It is typically regarded as an
important step in the standard NLP pipeline. As the semantic representations
are closely related to syntactic ones, we exploit syntactic information in our
model. We propose a version of graph convolutional networks (GCNs), a recent
class of neural networks operating on graphs, suited to model syntactic
dependency graphs. GCNs over syntactic dependency trees are used as sentence
encoders, producing latent feature representations of words in a sentence. We
observe that GCN layers are complementary to LSTM ones: when we stack both GCN
and LSTM layers, we obtain a substantial improvement over an already
state-of-the-art LSTM SRL model, resulting in the best reported scores on the
standard benchmark (CoNLL-2009) both for Chinese and English.
| Diego Marcheggiani and Ivan Titov | null | 1703.04826 | null | null |
Budgeted Batch Bayesian Optimization With Unknown Batch Sizes | cs.LG | Parameter settings profoundly impact the performance of machine learning
algorithms and laboratory experiments. The classical grid search or trial-error
methods are exponentially expensive in large parameter spaces, and Bayesian
optimization (BO) offers an elegant alternative for global optimization of
black box functions. In situations where the black box function can be
evaluated at multiple points simultaneously, batch Bayesian optimization is
used. Current batch BO approaches are restrictive in that they fix the number
of evaluations per batch, and this can be wasteful when the number of specified
evaluations is larger than the number of real maxima in the underlying
acquisition function. We present the Budgeted Batch Bayesian Optimization (B3O)
for hyper-parameter tuning and experimental design - we identify the
appropriate batch size for each iteration in an elegant way. To set the batch
size flexible, we use the infinite Gaussian mixture model (IGMM) for
automatically identifying the number of peaks in the underlying acquisition
functions. We solve the intractability of estimating the IGMM directly from the
acquisition function by formulating the batch generalized slice sampling to
efficiently draw samples from the acquisition function. We perform extensive
experiments for both synthetic functions and two real world applications -
machine learning hyper-parameter tuning and experimental design for alloy
hardening. We show empirically that the proposed B3O outperforms the existing
fixed batch BO approaches in finding the optimum whilst requiring a fewer
number of evaluations, thus saving cost and time.
| Vu Nguyen and Santu Rana and Sunil Gupta and Cheng Li and Svetha
Venkatesh | null | 1703.04842 | null | null |
Information Theoretic Optimal Learning of Gaussian Graphical Models | cs.LG cs.IT math.IT math.ST stat.TH | What is the optimal number of independent observations from which a sparse
Gaussian Graphical Model can be correctly recovered? Information-theoretic
arguments provide a lower bound on the minimum number of samples necessary to
perfectly identify the support of any multivariate normal distribution as a
function of model parameters. For a model defined on a sparse graph with $p$
nodes, a maximum degree $d$ and minimum normalized edge strength $\kappa$, this
necessary number of samples scales at least as $d \log p/\kappa^2$. The sample
complexity requirements of existing methods for perfect graph reconstruction
exhibit dependency on additional parameters that do not enter in the lower
bound. The question of whether the lower bound is tight and achievable by a
polynomial time algorithm remains open. In this paper, we constructively answer
this question and propose an algorithm, termed DICE, whose sample complexity
matches the information-theoretic lower bound up to a universal constant
factor. We also propose a related algorithm SLICE that has a slightly higher
sample complexity, but can be implemented as a mixed integer quadratic program
which makes it attractive in practice. Importantly, SLICE retains a critical
advantage of DICE in that its sample complexity only depends on quantities
present in the information theoretic lower bound. We anticipate that this
result will stimulate future search of computationally efficient sample-optimal
algorithms.
| Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov | null | 1703.04886 | null | null |
Riemannian stochastic quasi-Newton algorithm with variance reduction and
its convergence analysis | cs.LG cs.NA math.NA math.OC stat.ML | Stochastic variance reduction algorithms have recently become popular for
minimizing the average of a large, but finite number of loss functions. The
present paper proposes a Riemannian stochastic quasi-Newton algorithm with
variance reduction (R-SQN-VR). The key challenges of averaging, adding, and
subtracting multiple gradients are addressed with notions of retraction and
vector transport. We present convergence analyses of R-SQN-VR on both
non-convex and retraction-convex functions under retraction and vector
transport operators. The proposed algorithm is evaluated on the Karcher mean
computation on the symmetric positive-definite manifold and the low-rank matrix
completion on the Grassmann manifold. In all cases, the proposed algorithm
outperforms the state-of-the-art Riemannian batch and stochastic gradient
algorithms.
| Hiroyuki Kasai and Hiroyuki Sato and Bamdev Mishra | null | 1703.0489 | null | null |
Sharp Minima Can Generalize For Deep Nets | cs.LG | Despite their overwhelming capacity to overfit, deep learning architectures
tend to generalize relatively well to unseen data, allowing them to be deployed
in practice. However, explaining why this is the case is still an open area of
research. One standing hypothesis that is gaining popularity, e.g. Hochreiter &
Schmidhuber (1997); Keskar et al. (2017), is that the flatness of minima of the
loss function found by stochastic gradient based methods results in good
generalization. This paper argues that most notions of flatness are problematic
for deep models and can not be directly applied to explain generalization.
Specifically, when focusing on deep networks with rectifier units, we can
exploit the particular geometry of parameter space induced by the inherent
symmetries that these architectures exhibit to build equivalent models
corresponding to arbitrarily sharper minima. Furthermore, if we allow to
reparametrize a function, the geometry of its parameters can change drastically
without affecting its generalization properties.
| Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio | null | 1703.04933 | null | null |
Resilience: A Criterion for Learning in the Presence of Arbitrary
Outliers | cs.LG cs.AI cs.CC cs.CR stat.ML | We introduce a criterion, resilience, which allows properties of a dataset
(such as its mean or best low rank approximation) to be robustly computed, even
in the presence of a large fraction of arbitrary additional data. Resilience is
a weaker condition than most other properties considered so far in the
literature, and yet enables robust estimation in a broader variety of settings.
We provide new information-theoretic results on robust distribution learning,
robust estimation of stochastic block models, and robust mean estimation under
bounded $k$th moments. We also provide new algorithmic results on robust
distribution learning, as well as robust mean estimation in $\ell_p$-norms.
Among our proof techniques is a method for pruning a high-dimensional
distribution with bounded $1$st moments to a stable "core" with bounded $2$nd
moments, which may be of independent interest.
| Jacob Steinhardt and Moses Charikar and Gregory Valiant | null | 1703.0494 | null | null |
Matched bipartite block model with covariates | cs.SI cs.LG stat.ML | Community detection or clustering is a fundamental task in the analysis of
network data. Many real networks have a bipartite structure which makes
community detection challenging. In this paper, we consider a model which
allows for matched communities in the bipartite setting, in addition to node
covariates with information about the matching. We derive a simple fast
algorithm for fitting the model based on variational inference ideas and show
its effectiveness on both simulated and real data. A variation of the model to
allow for degree-correction is also considered, in addition to a novel approach
to fitting such degree-corrected models.
| Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li | null | 1703.04943 | null | null |
Deep learning with convolutional neural networks for EEG decoding and
visualization | cs.LG cs.NE | PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping:
http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full
Code available here: https://github.com/robintibor/braindecode
| Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique
Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael
Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball | 10.1002/hbm.23730 | 1703.05051 | null | null |
Online Learning for Distribution-Free Prediction | cs.LG stat.CO stat.ML | We develop an online learning method for prediction, which is important in
problems with large and/or streaming data sets. We formulate the learning
approach using a covariance-fitting methodology, and show that the resulting
predictor has desirable computational and distribution-free properties: It is
implemented online with a runtime that scales linearly in the number of
samples; has a constant memory requirement; avoids local minima problems; and
prunes away redundant feature dimensions without relying on restrictive
assumptions on the data distribution. In conjunction with the split conformal
approach, it also produces distribution-free prediction confidence intervals in
a computationally efficient manner. The method is demonstrated on both real and
synthetic datasets.
| Dave Zachariah and Petre Stoica and Thomas B. Sch\"on | null | 1703.0506 | null | null |
Selective Harvesting over Networks | cs.SI cs.LG stat.ML | Active search (AS) on graphs focuses on collecting certain labeled nodes
(targets) given global knowledge of the network topology and its edge weights
under a query budget. However, in most networks, nodes, topology and edge
weights are all initially unknown. We introduce selective harvesting, a variant
of AS where the next node to be queried must be chosen among the neighbors of
the current queried node set; the available training data for deciding which
node to query is restricted to the subgraph induced by the queried set (and
their node attributes) and their neighbors (without any node or edge
attributes). Therefore, selective harvesting is a sequential decision problem,
where we must decide which node to query at each step. A classifier trained in
this scenario suffers from a tunnel vision effect: without recourse to
independent sampling, the urge to query promising nodes forces classifiers to
gather increasingly biased training data, which we show significantly hurts the
performance of AS methods and standard classifiers. We find that it is possible
to collect a much larger set of targets by using multiple classifiers, not by
combining their predictions as an ensemble, but switching between classifiers
used at each step, as a way to ease the tunnel vision effect. We discover that
switching classifiers collects more targets by (a) diversifying the training
data and (b) broadening the choices of nodes that can be queried next. This
highlights an exploration, exploitation, and diversification trade-off in our
problem that goes beyond the exploration and exploitation duality found in
classic sequential decision problems. From these observations we propose D3TS,
a method based on multi-armed bandits for non-stationary stochastic processes
that enforces classifier diversity, matching or exceeding the performance of
competing methods on seven real network datasets in our evaluation.
| Fabricio Murai, Diogo Renn\'o, Bruno Ribeiro, Gisele L. Pappa, Don
Towsley, Krista Gile | null | 1703.05082 | null | null |
A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators
for Partition Function Computation in Log-Linear Models | stat.ML cs.DB cs.DS cs.LG | Log-linear models are arguably the most successful class of graphical models
for large-scale applications because of their simplicity and tractability.
Learning and inference with these models require calculating the partition
function, which is a major bottleneck and intractable for large state spaces.
Importance Sampling (IS) and MCMC-based approaches are lucrative. However, the
condition of having a "good" proposal distribution is often not satisfied in
practice.
In this paper, we add a new dimension to efficient estimation via sampling.
We propose a new sampling scheme and an unbiased estimator that estimates the
partition function accurately in sub-linear time. Our samples are generated in
near-constant time using locality sensitive hashing (LSH), and so are
correlated and unnormalized. We demonstrate the effectiveness of our proposed
approach by comparing the accuracy and speed of estimating the partition
function against other state-of-the-art estimation techniques including IS and
the efficient variant of Gumbel-Max sampling. With our efficient sampling
scheme, we accurately train real-world language models using only 1-2% of
computations.
| Ryan Spring, Anshumali Shrivastava | null | 1703.0516 | null | null |
Prototypical Networks for Few-shot Learning | cs.LG stat.ML | We propose prototypical networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each new class. Prototypical networks
learn a metric space in which classification can be performed by computing
distances to prototype representations of each class. Compared to recent
approaches for few-shot learning, they reflect a simpler inductive bias that is
beneficial in this limited-data regime, and achieve excellent results. We
provide an analysis showing that some simple design decisions can yield
substantial improvements over recent approaches involving complicated
architectural choices and meta-learning. We further extend prototypical
networks to zero-shot learning and achieve state-of-the-art results on the
CU-Birds dataset.
| Jake Snell, Kevin Swersky, Richard S. Zemel | null | 1703.05175 | null | null |
Deep Embedding Forest: Forest-based Serving with Deep Embedding Features | cs.LG | Deep Neural Networks (DNN) have demonstrated superior ability to extract high
level embedding vectors from low level features. Despite the success, the
serving time is still the bottleneck due to expensive run-time computation of
multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems
require additional hardware that are not in the mainstream design of most
commercial applications. In contrast, tree or forest-based models are widely
adopted because of low serving cost, but heavily depend on carefully engineered
features. This work proposes a Deep Embedding Forest model that benefits from
the best of both worlds. The model consists of a number of embedding layers and
a forest/tree layer. The former maps high dimensional (hundreds of thousands to
millions) and heterogeneous low-level features to the lower dimensional
(thousands) vectors, and the latter ensures fast serving.
Built on top of a representative DNN model called Deep Crossing, and two
forest/tree-based models including XGBoost and LightGBM, a two-step Deep
Embedding Forest algorithm is demonstrated to achieve on-par or slightly better
performance as compared with the DNN counterpart, with only a fraction of
serving time on conventional hardware. After comparing with a joint
optimization algorithm called partial fuzzification, also proposed in this
paper, it is concluded that the two-step Deep Embedding Forest has achieved
near optimal performance. Experiments based on large scale data sets (up to 1
billion samples) from a major sponsored search engine proves the efficacy of
the proposed model.
| Jie Zhu, Ying Shan, JC Mao, Dong Yu, Holakou Rahmanian, Yi Zhang | null | 1703.05291 | null | null |
Neural Networks for Beginners. A fast implementation in Matlab, Torch,
TensorFlow | cs.LG cs.CV cs.MS stat.ML | This report provides an introduction to some Machine Learning tools within
the most common development environments. It mainly focuses on practical
problems, skipping any theoretical introduction. It is oriented to both
students trying to approach Machine Learning and experts looking for new
frameworks.
| Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca,
Andrea Zugarini | null | 1703.05298 | null | null |
A Study of Complex Deep Learning Networks on High Performance,
Neuromorphic, and Quantum Computers | cs.NE cs.LG | Current Deep Learning approaches have been very successful using
convolutional neural networks (CNN) trained on large graphical processing units
(GPU)-based computers. Three limitations of this approach are: 1) they are
based on a simple layered network topology, i.e., highly connected layers,
without intra-layer connections; 2) the networks are manually configured to
achieve optimal results, and 3) the implementation of neuron model is expensive
in both cost and power. In this paper, we evaluate deep learning models using
three different computing architectures to address these problems: quantum
computing to train complex topologies, high performance computing (HPC) to
automatically determine network topology, and neuromorphic computing for a
low-power hardware implementation. We use the MNIST dataset for our experiment,
due to input size limitations of current quantum computers. Our results show
the feasibility of using the three architectures in tandem to address the above
deep learning limitations. We show a quantum computer can find high quality
values of intra-layer connections weights, in a tractable time as the
complexity of the network increases; a high performance computer can find
optimal layer-based topologies; and a neuromorphic computer can represent the
complex topology and weights derived from the other architectures in low power
memristive hardware.
| Thomas E. Potok, Catherine Schuman, Steven R. Young, Robert M. Patton,
Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, Gangotree Chakma | null | 1703.05364 | null | null |
Convolutional Recurrent Neural Networks for Small-Footprint Keyword
Spotting | cs.CL cs.AI cs.LG | Keyword spotting (KWS) constitutes a major component of human-technology
interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate,
while minimizing the footprint size, latency and complexity are the goals for
KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks
(CRNNs). Inspired by large-scale state-of-the-art speech recognition systems,
we combine the strengths of convolutional layers and recurrent layers to
exploit local structure and long-range context. We analyze the effect of
architecture parameters, and propose training strategies to improve
performance. With only ~230k parameters, our CRNN model yields acceptably low
latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise
ratio.
| Sercan O. Arik, Markus Kliegl, Rewon Child, Joel Hestness, Andrew
Gibiansky, Chris Fougner, Ryan Prenger, Adam Coates | null | 1703.0539 | null | null |
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play | cs.LG | We describe a simple scheme that allows an agent to learn about its
environment in an unsupervised manner. Our scheme pits two versions of the same
agent, Alice and Bob, against one another. Alice proposes a task for Bob to
complete; and then Bob attempts to complete the task. In this work we will
focus on two kinds of environments: (nearly) reversible environments and
environments that can be reset. Alice will "propose" the task by doing a
sequence of actions and then Bob must undo or repeat them, respectively. Via an
appropriate reward structure, Alice and Bob automatically generate a curriculum
of exploration, enabling unsupervised training of the agent. When Bob is
deployed on an RL task within the environment, this unsupervised training
reduces the number of supervised episodes needed to learn, and in some cases
converges to a higher reward.
| Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve,
Arthur Szlam and Rob Fergus | null | 1703.05407 | null | null |
Aggregation of Classifiers: A Justifiable Information Granularity
Approach | cs.LG stat.ML | In this study, we introduce a new approach to combine multi-classifiers in an
ensemble system. Instead of using numeric membership values encountered in
fixed combining rules, we construct interval membership values associated with
each class prediction at the level of meta-data of observation by using
concepts of information granules. In the proposed method, uncertainty
(diversity) of findings produced by the base classifiers is quantified by
interval-based information granules. The discriminative decision model is
generated by considering both the bounds and the length of the obtained
intervals. We select ten and then fifteen learning algorithms to build a
heterogeneous ensemble system and then conducted the experiment on a number of
UCI datasets. The experimental results demonstrate that the proposed approach
performs better than the benchmark algorithms including six fixed combining
methods, one trainable combining method, AdaBoost, Bagging, and Random
Subspace.
| Tien Thanh Nguyen, Xuan Cuong Pham, Alan Wee-Chung Liew, Witold
Pedrycz | null | 1703.05411 | null | null |
Cost-complexity pruning of random forests | stat.ML cs.LG | Random forests perform bootstrap-aggregation by sampling the training samples
with replacement. This enables the evaluation of out-of-bag error which serves
as a internal cross-validation mechanism. Our motivation lies in using the
unsampled training samples to improve each decision tree in the ensemble. We
study the effect of using the out-of-bag samples to improve the generalization
error first of the decision trees and second the random forest by post-pruning.
A preliminary empirical study on four UCI repository datasets show consistent
decrease in the size of the forests without considerable loss in accuracy.
| Kiran Bangalore Ravi, Jean Serra | null | 1703.0543 | null | null |
Look into Person: Self-supervised Structure-sensitive Learning and A New
Benchmark for Human Parsing | cs.CV cs.AI cs.LG | Human parsing has recently attracted a lot of research interests due to its
huge application potentials. However existing datasets have limited number of
images and annotations, and lack the variety of human appearances and the
coverage of challenging cases in unconstrained environment. In this paper, we
introduce a new benchmark "Look into Person (LIP)" that makes a significant
advance in terms of scalability, diversity and difficulty, a contribution that
we feel is crucial for future developments in human-centric analysis. This
comprehensive dataset contains over 50,000 elaborately annotated images with 19
semantic part labels, which are captured from a wider range of viewpoints,
occlusions and background complexity. Given these rich annotations we perform
detailed analyses of the leading human parsing approaches, gaining insights
into the success and failures of these methods. Furthermore, in contrast to the
existing efforts on improving the feature discriminative capability, we solve
human parsing by exploring a novel self-supervised structure-sensitive learning
approach, which imposes human pose structures into parsing results without
resorting to extra supervision (i.e., no need for specifically labeling human
joints in model training). Our self-supervised learning framework can be
injected into any advanced neural networks to help incorporate rich high-level
knowledge regarding human joints from a global perspective and improve the
parsing results. Extensive evaluations on our LIP and the public
PASCAL-Person-Part dataset demonstrate the superiority of our method.
| Ke Gong, Xiaodan Liang, Dongyu Zhang, Xiaohui Shen, Liang Lin | null | 1703.05446 | null | null |
Minimax Regret Bounds for Reinforcement Learning | stat.ML cs.AI cs.LG | We consider the problem of provably optimal exploration in reinforcement
learning for finite horizon MDPs. We show that an optimistic modification to
value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} +
H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states,
$A$ the number of actions and $T$ the number of time-steps. This result
improves over the best previous known bound $\tilde{O}(HS \sqrt{AT})$ achieved
by the UCRL2 algorithm of Jaksch et al., 2010. The key significance of our new
results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of
$\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of
$\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contains two key
insights. We use careful application of concentration inequalities to the
optimal value function as a whole, rather than to the transitions probabilities
(to improve scaling in $S$), and we define Bernstein-based "exploration
bonuses" that use the empirical variance of the estimated values at the next
states (to improve scaling in $H$).
| Mohammad Gheshlaghi Azar and Ian Osband and R\'emi Munos | null | 1703.05449 | null | null |
Efficient Online Learning for Optimizing Value of Information: Theory
and Application to Interactive Troubleshooting | cs.AI cs.LG stat.ML | We consider the optimal value of information (VoI) problem, where the goal is
to sequentially select a set of tests with a minimal cost, so that one can
efficiently make the best decision based on the observed outcomes. Existing
algorithms are either heuristics with no guarantees, or scale poorly (with
exponential run time in terms of the number of available tests). Moreover,
these methods assume a known distribution over the test outcomes, which is
often not the case in practice. We propose an efficient sampling-based online
learning framework to address the above issues. First, assuming the
distribution over hypotheses is known, we propose a dynamic hypothesis
enumeration strategy, which allows efficient information gathering with strong
theoretical guarantees. We show that with sufficient amount of samples, one can
identify a near-optimal decision with high probability. Second, when the
parameters of the hypotheses distribution are unknown, we propose an algorithm
which learns the parameters progressively via posterior sampling in an online
fashion. We further establish a rigorous bound on the expected regret. We
demonstrate the effectiveness of our approach on a real-world interactive
troubleshooting application and show that one can efficiently make high-quality
decisions with low cost.
| Yuxin Chen, Jean-Michel Renders, Morteza Haghir Chehreghani, Andreas
Krause | null | 1703.05452 | null | null |
Using Reinforcement Learning for Demand Response of Domestic Hot Water
Buffers: a Real-Life Demonstration | cs.SY cs.LG | This paper demonstrates a data-driven control approach for demand response in
real-life residential buildings. The objective is to optimally schedule the
heating cycles of the Domestic Hot Water (DHW) buffer to maximize the
self-consumption of the local photovoltaic (PV) production. A model-based
reinforcement learning technique is used to tackle the underlying sequential
decision-making problem. The proposed algorithm learns the stochastic occupant
behavior, predicts the PV production and takes into account the dynamics of the
system. A real-life experiment with six residential buildings is performed
using this algorithm. The results show that the self-consumption of the PV
production is significantly increased, compared to the default thermostat
control.
| Oscar De Somer, Ana Soares, Tristan Kuijpers, Koen Vossen, Koen
Vanthournout and Fred Spiessens | null | 1703.05486 | null | null |
Shift Aggregate Extract Networks | cs.LG stat.ML | We introduce an architecture based on deep hierarchical decompositions to
learn effective representations of large graphs. Our framework extends classic
R-decompositions used in kernel methods, enabling nested "part-of-part"
relations. Unlike recursive neural networks, which unroll a template on input
graphs directly, we unroll a neural network template over the decomposition
hierarchy, allowing us to deal with the high degree variability that typically
characterize social network graphs. Deep hierarchical decompositions are also
amenable to domain compression, a technique that reduces both space and time
complexity by exploiting symmetries. We show empirically that our approach is
competitive with current state-of-the-art graph classification methods,
particularly when dealing with social network datasets.
| Francesco Orsini, Daniele Baracchi and Paolo Frasconi | null | 1703.05537 | null | null |
Fraternal Twins: Unifying Attacks on Machine Learning and Digital
Watermarking | cs.CR cs.LG | Machine learning is increasingly used in security-critical applications, such
as autonomous driving, face recognition and malware detection. Most learning
methods, however, have not been designed with security in mind and thus are
vulnerable to different types of attacks. This problem has motivated the
research field of adversarial machine learning that is concerned with attacking
and defending learning methods. Concurrently, a different line of research has
tackled a very similar problem: In digital watermarking information are
embedded in a signal in the presence of an adversary. As a consequence, this
research field has also extensively studied techniques for attacking and
defending watermarking methods.
The two research communities have worked in parallel so far, unnoticeably
developing similar attack and defense strategies. This paper is a first effort
to bring these communities together. To this end, we present a unified notation
of black-box attacks against machine learning and watermarking that reveals the
similarity of both settings. To demonstrate the efficacy of this unified view,
we apply concepts from watermarking to machine learning and vice versa. We show
that countermeasures from watermarking can mitigate recent model-extraction
attacks and, similarly, that techniques for hardening machine learning can fend
off oracle attacks against watermarks. Our work provides a conceptual link
between two research fields and thereby opens novel directions for improving
the security of both, machine learning and digital watermarking.
| Erwin Quiring, Daniel Arp, Konrad Rieck | null | 1703.05561 | null | null |
Convolutional neural network architecture for geometric matching | cs.CV cs.LG | We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.
| Ignacio Rocco, Relja Arandjelovi\'c, Josef Sivic | null | 1703.05593 | null | null |
End-to-End Learning for Structured Prediction Energy Networks | stat.ML cs.LG | Structured Prediction Energy Networks (SPENs) are a simple, yet expressive
family of structured prediction models (Belanger and McCallum, 2016). An energy
function over candidate structured outputs is given by a deep network, and
predictions are formed by gradient-based optimization. This paper presents
end-to-end learning for SPENs, where the energy function is discriminatively
trained by back-propagating through gradient-based prediction. In our
experience, the approach is substantially more accurate than the structured SVM
method of Belanger and McCallum (2016), as it allows us to use more
sophisticated non-convex energies. We provide a collection of techniques for
improving the speed, accuracy, and memory requirements of end-to-end SPENs, and
demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005
semantic role labeling tasks. In both, inexact minimization of non-convex SPEN
energies is superior to baseline methods that use simplistic energy functions
that can be minimized exactly.
| David Belanger, Bishan Yang, Andrew McCallum | null | 1703.05667 | null | null |
Neural Sketch Learning for Conditional Program Generation | cs.PL cs.LG | We study the problem of generating source code in a strongly typed, Java-like
programming language, given a label (for example a set of API calls or types)
carrying a small amount of information about the code that is desired. The
generated programs are expected to respect a "realistic" relationship between
programs and labels, as exemplified by a corpus of labeled programs available
during training.
Two challenges in such conditional program generation are that the generated
programs must satisfy a rich set of syntactic and semantic constraints, and
that source code contains many low-level features that impede learning. We
address these problems by training a neural generator not on code but on
program sketches, or models of program syntax that abstract out names and
operations that do not generalize across programs. During generation, we infer
a posterior distribution over sketches, then concretize samples from this
distribution into type-safe programs using combinatorial techniques. We
implement our ideas in a system for generating API-heavy Java code, and show
that it can often predict the entire body of a method given just a few API
calls or data types that appear in the method.
| Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine | null | 1703.05698 | null | null |
Particle Value Functions | cs.LG cs.AI | The policy gradients of the expected return objective can react slowly to
rare rewards. Yet, in some cases agents may wish to emphasize the low or high
returns regardless of their probability. Borrowing from the economics and
control literature, we review the risk-sensitive value function that arises
from an exponential utility and illustrate its effects on an example. This
risk-sensitive value function is not always applicable to reinforcement
learning problems, so we introduce the particle value function defined by a
particle filter over the distributions of an agent's experience, which bounds
the risk-sensitive one. We illustrate the benefit of the policy gradients of
this objective in Cliffworld.
| Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess,
Arnaud Doucet, Andriy Mnih, Yee Whye Teh | null | 1703.0582 | null | null |
Automatically identifying, counting, and describing wild animals in
camera-trap images with deep learning | cs.CV cs.LG | Having accurate, detailed, and up-to-date information about the location and
behavior of animals in the wild would revolutionize our ability to study and
conserve ecosystems. We investigate the ability to automatically, accurately,
and inexpensively collect such data, which could transform many fields of
biology, ecology, and zoology into "big data" sciences. Motion sensor "camera
traps" enable collecting wildlife pictures inexpensively, unobtrusively, and
frequently. However, extracting information from these pictures remains an
expensive, time-consuming, manual task. We demonstrate that such information
can be automatically extracted by deep learning, a cutting-edge type of
artificial intelligence. We train deep convolutional neural networks to
identify, count, and describe the behaviors of 48 species in the
3.2-million-image Snapshot Serengeti dataset. Our deep neural networks
automatically identify animals with over 93.8% accuracy, and we expect that
number to improve rapidly in years to come. More importantly, if our system
classifies only images it is confident about, our system can automate animal
identification for 99.3% of the data while still performing at the same 96.6%
accuracy as that of crowdsourced teams of human volunteers, saving more than
8.4 years (at 40 hours per week) of human labeling effort (i.e. over 17,000
hours) on this 3.2-million-image dataset. Those efficiency gains immediately
highlight the importance of using deep neural networks to automate data
extraction from camera-trap images. Our results suggest that this technology
could enable the inexpensive, unobtrusive, high-volume, and even real-time
collection of a wealth of information about vast numbers of animals in the
wild.
| Mohammed Sadegh Norouzzadeh, Anh Nguyen, Margaret Kosmala, Ali
Swanson, Meredith Palmer, Craig Packer, Jeff Clune | null | 1703.0583 | null | null |
Conditional Accelerated Lazy Stochastic Gradient Descent | cs.LG stat.ML | In this work we introduce a conditional accelerated lazy stochastic gradient
descent algorithm with optimal number of calls to a stochastic first-order
oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
over the projection-free, Online Frank-Wolfe based stochastic gradient descent
of Hazan and Kale [2012] with convergence rate
$O\left(\frac{1}{\varepsilon^4}\right)$.
| Guanghui Lan, Sebastian Pokutta, Yi Zhou and Daniel Zink | null | 1703.0584 | null | null |
Empirical Evaluation of Parallel Training Algorithms on Acoustic
Modeling | cs.CL cs.LG cs.SD eess.AS | Deep learning models (DLMs) are state-of-the-art techniques in speech
recognition. However, training good DLMs can be time consuming especially for
production-size models and corpora. Although several parallel training
algorithms have been proposed to improve training efficiency, there is no clear
guidance on which one to choose for the task in hand due to lack of systematic
and fair comparison among them. In this paper we aim at filling this gap by
comparing four popular parallel training algorithms in speech recognition,
namely asynchronous stochastic gradient descent (ASGD), blockwise model-update
filtering (BMUF), bulk synchronous parallel (BSP) and elastic averaging
stochastic gradient descent (EASGD), on 1000-hour LibriSpeech corpora using
feed-forward deep neural networks (DNNs) and convolutional, long short-term
memory, DNNs (CLDNNs). Based on our experiments, we recommend using BMUF as the
top choice to train acoustic models since it is most stable, scales well with
number of GPUs, can achieve reproducible results, and in many cases even
outperforms single-GPU SGD. ASGD can be used as a substitute in some cases.
| Wenpeng Li, BinBin Zhang, Lei Xie, Dong Yu | null | 1703.0588 | null | null |
Learning Robust Visual-Semantic Embeddings | cs.CV cs.CL cs.LG | Many of the existing methods for learning joint embedding of images and text
use only supervised information from paired images and its textual attributes.
Taking advantage of the recent success of unsupervised learning in deep neural
networks, we propose an end-to-end learning framework that is able to extract
more robust multi-modal representations across domains. The proposed method
combines representation learning models (i.e., auto-encoders) together with
cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn
joint embeddings for semantic and visual features. A novel technique of
unsupervised-data adaptation inference is introduced to construct more
comprehensive embeddings for both labeled and unlabeled data. We evaluate our
method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with
a wide range of applications, including zero and few-shot image recognition and
retrieval, from inductive to transductive settings. Empirically, we show that
our framework improves over the current state of the art on many of the
considered tasks.
| Yao-Hung Hubert Tsai and Liang-Kang Huang and Ruslan Salakhutdinov | null | 1703.05908 | null | null |
Unsupervised Anomaly Detection with Generative Adversarial Networks to
Guide Marker Discovery | cs.CV cs.LG | Obtaining models that capture imaging markers relevant for disease
progression and treatment monitoring is challenging. Models are typically based
on large amounts of data with annotated examples of known markers aiming at
automating detection. High annotation effort and the limitation to a vocabulary
of known markers limit the power of such approaches. Here, we perform
unsupervised learning to identify anomalies in imaging data as candidates for
markers. We propose AnoGAN, a deep convolutional generative adversarial network
to learn a manifold of normal anatomical variability, accompanying a novel
anomaly scoring scheme based on the mapping from image space to a latent space.
Applied to new data, the model labels anomalies, and scores image patches
indicating their fit into the learned distribution. Results on optical
coherence tomography images of the retina demonstrate that the approach
correctly identifies anomalous images, such as images containing retinal fluid
or hyperreflective foci.
| Thomas Schlegl, Philipp Seeb\"ock, Sebastian M. Waldstein, Ursula
Schmidt-Erfurth, Georg Langs | null | 1703.05921 | null | null |
Comparison of Different Methods for Tissue Segmentation in
Histopathological Whole-Slide Images | cs.CV cs.LG | Tissue segmentation is an important pre-requisite for efficient and accurate
diagnostics in digital pathology. However, it is well known that whole-slide
scanners can fail in detecting all tissue regions, for example due to the
tissue type, or due to weak staining because their tissue detection algorithms
are not robust enough. In this paper, we introduce two different convolutional
neural network architectures for whole slide image segmentation to accurately
identify the tissue sections. We also compare the algorithms to a published
traditional method. We collected 54 whole slide images with differing stains
and tissue types from three laboratories to validate our algorithms. We show
that while the two methods do not differ significantly they outperform their
traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).
| P\'eter B\'andi, Rob van de Loo, Milad Intezar, Daan Geijs, Francesco
Ciompi, Bram van Ginneken, Jeroen van der Laak, Geert Litjens | null | 1703.0599 | null | null |
Online Learning for Offloading and Autoscaling in Energy Harvesting
Mobile Edge Computing | cs.LG cs.NI | Mobile edge computing (a.k.a. fog computing) has recently emerged to enable
in-situ processing of delay-sensitive applications at the edge of mobile
networks. Providing grid power supply in support of mobile edge computing,
however, is costly and even infeasible (in certain rugged or under-developed
areas), thus mandating on-site renewable energy as a major or even sole power
supply in increasingly many scenarios. Nonetheless, the high intermittency and
unpredictability of renewable energy make it very challenging to deliver a high
quality of service to users in energy harvesting mobile edge computing systems.
In this paper, we address the challenge of incorporating renewables into mobile
edge computing and propose an efficient reinforcement learning-based resource
management algorithm, which learns on-the-fly the optimal policy of dynamic
workload offloading (to the centralized cloud) and edge server provisioning to
minimize the long-term system cost (including both service delay and
operational cost). Our online learning algorithm uses a decomposition of the
(offline) value iteration and (online) reinforcement learning, thus achieving a
significant improvement of learning rate and run-time performance when compared
to standard reinforcement learning algorithms such as Q-learning. We prove the
convergence of the proposed algorithm and analytically show that the learned
policy has a simple monotone structure amenable to practical implementation.
Our simulation results validate the efficacy of our algorithm, which
significantly improves the edge computing performance compared to fixed or
myopic optimization schemes and conventional reinforcement learning algorithms.
| Jie Xu, Lixing Chen, Shaolei Ren | null | 1703.0606 | null | null |
Block CUR: Decomposing Matrices using Groups of Columns | stat.ML cs.DC cs.DS cs.LG | A common problem in large-scale data analysis is to approximate a matrix
using a combination of specifically sampled rows and columns, known as CUR
decomposition. Unfortunately, in many real-world environments, the ability to
sample specific individual rows or columns of the matrix is limited by either
system constraints or cost. In this paper, we consider matrix approximation by
sampling predefined \emph{blocks} of columns (or rows) from the matrix. We
present an algorithm for sampling useful column blocks and provide novel
guarantees for the quality of the approximation. This algorithm has application
in problems as diverse as biometric data analysis to distributed computing. We
demonstrate the effectiveness of the proposed algorithms for computing the
Block CUR decomposition of large matrices in a distributed setting with
multiple nodes in a compute cluster, where such blocks correspond to columns
(or rows) of the matrix stored on the same node, which can be retrieved with
much less overhead than retrieving individual columns stored across different
nodes. In the biometric setting, the rows correspond to different users and
columns correspond to users' biometric reaction to external stimuli, {\em
e.g.,}~watching video content, at a particular time instant. There is
significant cost in acquiring each user's reaction to lengthy content so we
sample a few important scenes to approximate the biometric response. An
individual time sample in this use case cannot be queried in isolation due to
the lack of context that caused that biometric reaction. Instead, collections
of time segments ({\em i.e.,} blocks) must be presented to the user. The
practical application of these algorithms is shown via experimental results
using real-world user biometric data from a content testing environment.
| Urvashi Oswal, Swayambhoo Jain, Kevin S. Xu, and Brian Eriksson | null | 1703.06065 | null | null |
Machine learning approach for early detection of autism by combining
questionnaire and home video screening | cs.CY cs.LG | Existing screening tools for early detection of autism are expensive,
cumbersome, time-intensive, and sometimes fall short in predictive value. In
this work, we apply Machine Learning (ML) to gold standard clinical data
obtained across thousands of children at risk for autism spectrum disorders to
create a low-cost, quick, and easy to apply autism screening tool that performs
as well or better than most widely used standardized instruments. This new tool
combines two screening methods into a single assessment, one based on short,
structured parent-report questionnaires and the other on tagging key behaviors
from short, semi-structured home videos of children. To overcome the scarcity,
sparsity, and imbalance of training data, we apply creative feature selection,
feature engineering, and novel feature encoding techniques. We allow for
inconclusive determination where appropriate in order to boost screening
accuracy when conclusive. We demonstrate a significant accuracy improvement
over standard screening tools in a clinical study sample of 162 children.
| Halim Abbas, Ford Garberson, Eric Glover, Dennis P Wall | null | 1703.06076 | null | null |
Modeling Relational Data with Graph Convolutional Networks | stat.ML cs.AI cs.DB cs.LG | Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
remain incomplete. We introduce Relational Graph Convolutional Networks
(R-GCNs) and apply them to two standard knowledge base completion tasks: Link
prediction (recovery of missing facts, i.e. subject-predicate-object triples)
and entity classification (recovery of missing entity attributes). R-GCNs are
related to a recent class of neural networks operating on graphs, and are
developed specifically to deal with the highly multi-relational data
characteristic of realistic knowledge bases. We demonstrate the effectiveness
of R-GCNs as a stand-alone model for entity classification. We further show
that factorization models for link prediction such as DistMult can be
significantly improved by enriching them with an encoder model to accumulate
evidence over multiple inference steps in the relational graph, demonstrating a
large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
| Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den
Berg, Ivan Titov, Max Welling | null | 1703.06103 | null | null |
Nonconvex One-bit Single-label Multi-label Learning | stat.ML cs.LG | We study an extreme scenario in multi-label learning where each training
instance is endowed with a single one-bit label out of multiple labels. We
formulate this problem as a non-trivial special case of one-bit rank-one matrix
sensing and develop an efficient non-convex algorithm based on alternating
power iteration. The proposed algorithm is able to recover the underlying
low-rank matrix model with linear convergence. For a rank-$k$ model with $d_1$
features and $d_2$ classes, the proposed algorithm achieves $O(\epsilon)$
recovery error after retrieving $O(k^{1.5}d_1 d_2/\epsilon)$ one-bit labels
within $O(kd)$ memory. Our bound is nearly optimal in the order of
$O(1/\epsilon)$. This significantly improves the state-of-the-art sampling
complexity of one-bit multi-label learning. We perform experiments to verify
our theory and evaluate the performance of the proposed algorithm.
| Shuang Qiu and Tingjin Luo and Jieping Ye and Ming Lin | null | 1703.06104 | null | null |
Deep Sets | cs.LG stat.ML | We study the problem of designing models for machine learning tasks defined
on \emph{sets}. In contrast to traditional approach of operating on fixed
dimensional vectors, we consider objective functions defined on sets that are
invariant to permutations. Such problems are widespread, ranging from
estimation of population statistics \cite{poczos13aistats}, to anomaly
detection in piezometer data of embankment dams \cite{Jung15Exploration}, to
cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem
characterizes the permutation invariant functions and provides a family of
functions to which any permutation invariant objective function must belong.
This family of functions has a special structure which enables us to design a
deep network architecture that can operate on sets and which can be deployed on
a variety of scenarios including both unsupervised and supervised learning
tasks. We also derive the necessary and sufficient conditions for permutation
equivariance in deep models. We demonstrate the applicability of our method on
population statistic estimation, point cloud classification, set expansion, and
outlier detection.
| Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos,
Ruslan Salakhutdinov, Alexander Smola | null | 1703.06114 | null | null |
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers | cs.LG cs.IR | Accurately evaluating new policies (e.g. ad-placement models, ranking
functions, recommendation functions) is one of the key prerequisites for
improving interactive systems. While the conventional approach to evaluation
relies on online A/B tests, recent work has shown that counterfactual
estimators can provide an inexpensive and fast alternative, since they can be
applied offline using log data that was collected from a different policy
fielded in the past. In this paper, we address the question of how to estimate
the performance of a new target policy when we have log data from multiple
historic policies. This question is of great relevance in practice, since
policies get updated frequently in most online systems. We show that naively
combining data from multiple logging policies can be highly suboptimal. In
particular, we find that the standard Inverse Propensity Score (IPS) estimator
suffers especially when logging and target policies diverge -- to a point where
throwing away data improves the variance of the estimator. We therefore propose
two alternative estimators which we characterize theoretically and compare
experimentally. We find that the new estimators can provide substantially
improved estimation accuracy.
| Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims | 10.1145/3097983.3098155 | 1703.0618 | null | null |
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under
Partial Observability | cs.LG cs.AI cs.MA | Many real-world tasks involve multiple agents with partial observability and
limited communication. Learning is challenging in these settings due to local
viewpoints of agents, which perceive the world as non-stationary due to
concurrently-exploring teammates. Approaches that learn specialized policies
for individual tasks face problems when applied to the real world: not only do
agents have to learn and store distinct policies for each task, but in practice
identities of tasks are often non-observable, making these approaches
inapplicable. This paper formalizes and addresses the problem of multi-task
multi-agent reinforcement learning under partial observability. We introduce a
decentralized single-task learning approach that is robust to concurrent
interactions of teammates, and present an approach for distilling single-task
policies into a unified policy that performs well across multiple related
tasks, without explicit provision of task identity.
| Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How,
John Vian | null | 1703.06182 | null | null |
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks | stat.ML cs.CV cs.LG cs.NE | We propose and systematically evaluate three strategies for training
dynamically-routed artificial neural networks: graphs of learned
transformations through which different input signals may take different paths.
Though some approaches have advantages over others, the resulting networks are
often qualitatively similar. We find that, in dynamically-routed networks
trained to classify images, layers and branches become specialized to process
distinct categories of images. Additionally, given a fixed computational
budget, dynamically-routed networks tend to perform better than comparable
statically-routed networks.
| Mason McGill and Pietro Perona | null | 1703.06217 | null | null |
Curriculum Dropout | cs.NE cs.LG stat.ML | Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregation technique, where an exponential number of smaller
networks are averaged in order to get a more powerful ensemble. In this paper,
we show that using a fixed dropout probability during training is a suboptimal
choice. We thus propose a time scheduling for the probability of retaining
neurons in the network. This induces an adaptive regularization scheme that
smoothly increases the difficulty of the optimization problem. This idea of
"starting easy" and adaptively increasing the difficulty of the learning
problem has its roots in curriculum learning and allows one to train better
models. Indeed, we prove that our optimization strategy implements a very
general curriculum scheme, by gradually adding noise to both the input and
intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network
architectures show that our method, named Curriculum Dropout, frequently yields
to better generalization and, at worst, performs just as well as the standard
Dropout method.
| Pietro Morerio, Jacopo Cavazza, Riccardo Volpi, Rene Vidal, Vittorio
Murino | null | 1703.06229 | null | null |
An Automated Auto-encoder Correlation-based Health-Monitoring and
Prognostic Method for Machine Bearings | cs.LG cs.NE stat.ML | This paper studies an intelligent ultimate technique for health-monitoring
and prognostic of common rotary machine components, particularly bearings.
During a run-to-failure experiment, rich unsupervised features from vibration
sensory data are extracted by a trained sparse auto-encoder. Then, the
correlation of the extracted attributes of the initial samples (presumably
healthy at the beginning of the test) with the succeeding samples is calculated
and passed through a moving-average filter. The normalized output is named
auto-encoder correlation-based (AEC) rate which stands for an informative
attribute of the system depicting its health status and precisely identifying
the degradation starting point. We show that AEC technique well-generalizes in
several run-to-failure tests. AEC collects rich unsupervised features form the
vibration data fully autonomous. We demonstrate the superiority of the AEC over
many other state-of-the-art approaches for the health monitoring and prognostic
of machine bearings.
| Ramin M. Hasani, Guodong Wang and Radu Grosu | null | 1703.06272 | null | null |
Multi-talker Speech Separation with Utterance-level Permutation
Invariant Training of Deep Recurrent Neural Networks | cs.SD cs.LG eess.AS | In this paper we propose the utterance-level Permutation Invariant Training
(uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning
based solution for speaker independent multi-talker speech separation.
Specifically, uPIT extends the recently proposed Permutation Invariant Training
(PIT) technique with an utterance-level cost function, hence eliminating the
need for solving an additional permutation problem during inference, which is
otherwise required by frame-level PIT. We achieve this using Recurrent Neural
Networks (RNNs) that, during training, minimize the utterance-level separation
error, hence forcing separated frames belonging to the same speaker to be
aligned to the same output stream. In practice, this allows RNNs, trained with
uPIT, to separate multi-talker mixed speech without any prior knowledge of
signal duration, number of speakers, speaker identity or gender. We evaluated
uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks
and found that uPIT outperforms techniques based on Non-negative Matrix
Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and
compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network
(DANet). Furthermore, we found that models trained with uPIT generalize well to
unseen speakers and languages. Finally, we found that a single model, trained
with uPIT, can handle both two-speaker, and three-speaker speech mixtures.
| Morten Kolb{\ae}k, Dong Yu, Zheng-Hua Tan, Jesper Jensen | null | 1703.06284 | null | null |
Deep Tensor Encoding | cs.IR cs.LG stat.ML | Learning an encoding of feature vectors in terms of an over-complete
dictionary or a information geometric (Fisher vectors) construct is wide-spread
in statistical signal processing and computer vision. In content based
information retrieval using deep-learning classifiers, such encodings are
learnt on the flattened last layer, without adherence to the multi-linear
structure of the underlying feature tensor. We illustrate a variety of feature
encodings incl. sparse dictionary coding and Fisher vectors along with
proposing that a structured tensor factorization scheme enables us to perform
retrieval that can be at par, in terms of average precision, with Fisher vector
encoded image signatures. In short, we illustrate how structural constraints
increase retrieval fidelity.
| B Sengupta and E Vasquez and Y Qian | null | 1703.06324 | null | null |
Spectrum Estimation from a Few Entries | stat.ML cs.DS cs.LG cs.NA | Singular values of a data in a matrix form provide insights on the structure
of the data, the effective dimensionality, and the choice of hyper-parameters
on higher-level data analysis tools. However, in many practical applications
such as collaborative filtering and network analysis, we only get a partial
observation. Under such scenarios, we consider the fundamental problem of
recovering spectral properties of the underlying matrix from a sampling of its
entries. We are particularly interested in directly recovering the spectrum,
which is the set of singular values, and also in sample-efficient approaches
for recovering a spectral sum function, which is an aggregate sum of the same
function applied to each of the singular values. We propose first estimating
the Schatten $k$-norms of a matrix, and then applying Chebyshev approximation
to the spectral sum function or applying moment matching in Wasserstein
distance to recover the singular values. The main technical challenge is in
accurately estimating the Schatten norms from a sampling of a matrix. We
introduce a novel unbiased estimator based on counting small structures in a
graph and provide guarantees that match its empirical performance. Our
theoretical analysis shows that Schatten norms can be recovered accurately from
strictly smaller number of samples compared to what is needed to recover the
underlying low-rank matrix. Numerical experiments suggest that we significantly
improve upon a competing approach of using matrix completion methods.
| Ashish Khetan, Sewoong Oh | null | 1703.06327 | null | null |
Transfer Learning for Sequence Tagging with Hierarchical Recurrent
Networks | cs.CL cs.LG | Recent papers have shown that neural networks obtain state-of-the-art
performance on several different sequence tagging tasks. One appealing property
of such systems is their generality, as excellent performance can be achieved
with a unified architecture and without task-specific feature engineering.
However, it is unclear if such systems can be used for tasks without large
amounts of training data. In this paper we explore the problem of transfer
learning for neural sequence taggers, where a source task with plentiful
annotations (e.g., POS tagging on Penn Treebank) is used to improve performance
on a target task with fewer available annotations (e.g., POS tagging for
microblogs). We examine the effects of transfer learning for deep hierarchical
recurrent networks across domains, applications, and languages, and show that
significant improvement can often be obtained. These improvements lead to
improvements over the current state-of-the-art on several well-studied tasks.
| Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen | null | 1703.06345 | null | null |
Optimal and Myopic Information Acquisition | cs.GT cs.LG math.ST stat.TH | We consider the problem of optimal dynamic information acquisition from many
correlated information sources. Each period, the decision-maker jointly takes
an action and allocates a fixed number of observations across the available
sources. His payoff depends on the actions taken and on an unknown state. In
the canonical setting of jointly normal information sources, we show that the
optimal dynamic information acquisition rule proceeds myopically after finitely
many periods. If signals are acquired in large blocks each period, then the
optimal rule turns out to be myopic from period 1. These results demonstrate
the possibility of robust and "simple" optimal information acquisition, and
simplify the analysis of dynamic information acquisition in a widely used
informational environment.
| Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis | null | 1703.06367 | null | null |
Semi-Supervised Learning with Competitive Infection Models | cs.LG | The goal in semi-supervised learning is to effectively combine labeled and
unlabeled data. One way to do this is by encouraging smoothness across edges in
a graph whose nodes correspond to input examples. In many graph-based methods,
labels can be thought of as propagating over the graph, where the underlying
propagation mechanism is based on random walks or on averaging dynamics. While
theoretically elegant, these dynamics suffer from several drawbacks which can
hurt predictive performance.
Our goal in this work is to explore alternative mechanisms for propagating
labels. In particular, we propose a method based on dynamic infection
processes, where unlabeled nodes can be "infected" with the label of their
already infected neighbors. Our algorithm is efficient and scalable, and an
analysis of the underlying optimization objective reveals a surprising relation
to other Laplacian approaches. We conclude with a thorough set of experiments
across multiple benchmarks and various learning settings.
| Nir Rosenfeld and Amir Globerson | null | 1703.06426 | null | null |
Near Optimal Hamiltonian-Control and Learning via Chattering | cs.LG | Many applications require solving non-linear control problems that are
classically not well behaved. This paper develops a simple and efficient
chattering algorithm that learns near optimal decision policies through an
open-loop feedback strategy. The optimal control problem reduces to a series of
linear optimization programs that can be easily solved to recover a relaxed
optimal trajectory. This algorithm is implemented on a real-time enterprise
scheduling and control process.
| Peeyush Kumar, Wolf Kohn, Zelda B. Zabinsky | null | 1703.06485 | null | null |
Generating Multi-label Discrete Patient Records using Generative
Adversarial Networks | cs.LG cs.NE | Access to electronic health record (EHR) data has motivated computational
advances in medical research. However, various concerns, particularly over
privacy, can limit access to and collaborative use of EHR data. Sharing
synthetic EHR data could mitigate risk. In this paper, we propose a new
approach, medical Generative Adversarial Network (medGAN), to generate
realistic synthetic patient records. Based on input real patient records,
medGAN can generate high-dimensional discrete variables (e.g., binary and count
features) via a combination of an autoencoder and generative adversarial
networks. We also propose minibatch averaging to efficiently avoid mode
collapse, and increase the learning efficiency with batch normalization and
shortcut connections. To demonstrate feasibility, we showed that medGAN
generates synthetic patient records that achieve comparable performance to real
data on many experiments including distribution statistics, predictive modeling
tasks and a medical expert review. We also empirically observe a limited
privacy risk in both identity and attribute disclosure using medGAN.
| Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F.
Stewart, Jimeng Sun | null | 1703.0649 | null | null |
Bernoulli Rank-$1$ Bandits for Click Feedback | cs.LG stat.ML | The probability that a user will click a search result depends both on its
relevance and its position on the results page. The position based model
explains this behavior by ascribing to every item an attraction probability,
and to every position an examination probability. To be clicked, a result must
be both attractive and examined. The probabilities of an item-position pair
being clicked thus form the entries of a rank-$1$ matrix. We propose the
learning problem of a Bernoulli rank-$1$ bandit where at each step, the
learning agent chooses a pair of row and column arms, and receives the product
of their Bernoulli-distributed values as a reward. This is a special case of
the stochastic rank-$1$ bandit problem considered in recent work that proposed
an elimination based algorithm Rank1Elim, and showed that Rank1Elim's regret
scales linearly with the number of rows and columns on "benign" instances.
These are the instances where the minimum of the average row and column rewards
$\mu$ is bounded away from zero. The issue with Rank1Elim is that it fails to
be competitive with straightforward bandit strategies as $\mu \rightarrow 0$.
In this paper we propose Rank1ElimKL which simply replaces the (crude)
confidence intervals of Rank1Elim with confidence intervals based on
Kullback-Leibler (KL) divergences, and with the help of a novel result
concerning the scaling of KL divergences we prove that with this change, our
algorithm will be competitive no matter the value of $\mu$. Experiments with
synthetic data confirm that on benign instances the performance of Rank1ElimKL
is significantly better than that of even Rank1Elim, while experiments with
models derived from real data confirm that the improvements are significant
across the board, regardless of whether the data is benign or not.
| Sumeet Katariya, Branislav Kveton, Csaba Szepesv\'ari, Claire Vernade,
Zheng Wen | null | 1703.06513 | null | null |
Recurrent Collective Classification | cs.LG | We propose a new method for training iterative collective classifiers for
labeling nodes in network data. The iterative classification algorithm (ICA) is
a canonical method for incorporating relational information into
classification. Yet, existing methods for training ICA models rely on the
assumption that relational features reflect the true labels of the nodes. This
unrealistic assumption introduces a bias that is inconsistent with the actual
prediction algorithm. In this paper, we introduce recurrent collective
classification (RCC), a variant of ICA analogous to recurrent neural network
prediction. RCC accommodates any differentiable local classifier and relational
feature functions. We provide gradient-based strategies for optimizing over
model parameters to more directly minimize the loss function. In our
experiments, this direct loss minimization translates to improved accuracy and
robustness on real network data. We demonstrate the robustness of RCC in
settings where local classification is very noisy, settings that are
particularly challenging for ICA.
| Shuangfei Fan, Bert Huang | null | 1703.06514 | null | null |
The Relationship Between Agnostic Selective Classification Active
Learning and the Disagreement Coefficient | cs.LG | A selective classifier (f,g) comprises a classification function f and a
binary selection function g, which determines if the classifier abstains from
prediction, or uses f to predict. The classifier is called
pointwise-competitive if it classifies each point identically to the best
classifier in hindsight (from the same class), whenever it does not abstain.
The quality of such a classifier is quantified by its rejection mass, defined
to be the probability mass of the points it rejects. A "fast" rejection rate is
achieved if the rejection mass is bounded from above by O(1/m) where m is the
number of labeled examples used to train the classifier (and O hides
logarithmic factors). Pointwise-competitive selective (PCS) classifiers are
intimately related to disagreement-based active learning and it is known that
in the realizable case, a fast rejection rate of a known PCS algorithm (called
Consistent Selective Strategy) is equivalent to an exponential speedup of the
well-known CAL active algorithm.
We focus on the agnostic setting, for which there is a known algorithm called
LESS that learns a PCS classifier and achieves a fast rejection rate (depending
on Hanneke's disagreement coefficient) under strong assumptions. We present an
improved PCS learning algorithm called ILESS for which we show a fast rate
(depending on Hanneke's disagreement coefficient) without any assumptions. Our
rejection bound smoothly interpolates the realizable and agnostic settings. The
main result of this paper is an equivalence between the following three
entities: (i) the existence of a fast rejection rate for any PCS learning
algorithm (such as ILESS); (ii) a poly-logarithmic bound for Hanneke's
disagreement coefficient; and (iii) an exponential speedup for a new
disagreement-based active learner called ActiveiLESS.
| Roei Gelbhart, Ran El-Yaniv | null | 1703.06536 | null | null |
Learning Cooperative Visual Dialog Agents with Deep Reinforcement
Learning | cs.CV cs.AI cs.CL cs.LG | We introduce the first goal-driven training for visual question answering and
dialog agents. Specifically, we pose a cooperative 'image guessing' game
between two agents -- Qbot and Abot -- who communicate in natural language
dialog so that Qbot can select an unseen image from a lineup of images. We use
deep reinforcement learning (RL) to learn the policies of these agents
end-to-end -- from pixels to multi-agent multi-round dialog to game reward.
We demonstrate two experimental results.
First, as a 'sanity check' demonstration of pure RL (from scratch), we show
results on a synthetic world, where the agents communicate in ungrounded
vocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We find
that two bots invent their own communication protocol and start using certain
symbols to ask/answer about certain visual attributes (shape/color/style).
Thus, we demonstrate the emergence of grounded language and communication among
'visual' dialog agents with no human supervision.
Second, we conduct large-scale real-image experiments on the VisDial dataset,
where we pretrain with supervised dialog data and show that the RL 'fine-tuned'
agents significantly outperform SL agents. Interestingly, the RL Qbot learns to
ask questions that Abot is good at, ultimately resulting in more informative
dialog and a better team.
| Abhishek Das, Satwik Kottur, Jos\'e M. F. Moura, Stefan Lee, Dhruv
Batra | null | 1703.06585 | null | null |
A Systematic Study of Online Class Imbalance Learning with Concept Drift | cs.LG | As an emerging research topic, online class imbalance learning often combines
the challenges of both class imbalance and concept drift. It deals with data
streams having very skewed class distributions, where concept drift may occur.
It has recently received increased research attention; however, very little
work addresses the combined problem where both class imbalance and concept
drift coexist. As the first systematic study of handling concept drift in
class-imbalanced data streams, this paper first provides a comprehensive review
of current research progress in this field, including current research focuses
and open challenges. Then, an in-depth experimental study is performed, with
the goal of understanding how to best overcome concept drift in online learning
with class imbalance. Based on the analysis, a general guideline is proposed
for the development of an effective algorithm.
| Shuo Wang, Leandro L. Minku, Xin Yao | null | 1703.06683 | null | null |
QMDP-Net: Deep Learning for Planning under Partial Observability | cs.AI cs.LG cs.NE stat.ML | This paper introduces the QMDP-net, a neural network architecture for
planning under partial observability. The QMDP-net combines the strengths of
model-free learning and model-based planning. It is a recurrent policy network,
but it represents a policy for a parameterized set of tasks by connecting a
model with a planning algorithm that solves the model, thus embedding the
solution structure of planning in a network learning architecture. The QMDP-net
is fully differentiable and allows for end-to-end training. We train a QMDP-net
on different tasks so that it can generalize to new ones in the parameterized
task set and "transfer" to other similar tasks beyond the set. In preliminary
experiments, QMDP-net showed strong performance on several robotic tasks in
simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it
sometimes outperforms the QMDP algorithm in the experiments, as a result of
end-to-end learning.
| Peter Karkus, David Hsu, Wee Sun Lee | null | 1703.06692 | null | null |
Independence clustering (without a matrix) | cs.LG cs.IT math.IT stat.ML | The independence clustering problem is considered in the following
formulation: given a set $S$ of random variables, it is required to find the
finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the
clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is
the target, pairwise similarity measurements are of no use, and thus
traditional clustering algorithms are inapplicable. The distribution of the
random variables in $S$ is, in general, unknown, but a sample is available.
Thus, the problem is cast in terms of time series. Two forms of sampling are
considered: i.i.d.\ and stationary time series, with the main emphasis being on
the latter, more general, case. A consistent, computationally tractable
algorithm for each of the settings is proposed, and a number of open directions
for further research are outlined.
| Daniil Ryabko | null | 1703.067 | null | null |
On the effect of pooling on the geometry of representations | cs.LG | In machine learning and neuroscience, certain computational structures and
algorithms are known to yield disentangled representations without us
understanding why, the most striking examples being perhaps convolutional
neural networks and the ventral stream of the visual cortex in humans and
primates. As for the latter, it was conjectured that representations may be
disentangled by being flattened progressively and at a local scale. An attempt
at a formalization of the role of invariance in learning representations was
made recently, being referred to as I-theory. In this framework and using the
language of differential geometry, we show that pooling over a group of
transformations of the input contracts the metric and reduces its curvature,
and provide quantitative bounds, in the aim of moving towards a theoretical
understanding on how to disentangle representations.
| Gary B\'ecigneul | null | 1703.06726 | null | null |
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents | cs.LG cs.CR stat.ML | We introduce two tactics to attack agents trained by deep reinforcement
learning algorithms using adversarial examples, namely the strategically-timed
attack and the enchanting attack. In the strategically-timed attack, the
adversary aims at minimizing the agent's reward by only attacking the agent at
a small subset of time steps in an episode. Limiting the attack activity to
this subset helps prevent detection of the attack by the agent. We propose a
novel method to determine when an adversarial example should be crafted and
applied. In the enchanting attack, the adversary aims at luring the agent to a
designated target state. This is achieved by combining a generative model and a
planning algorithm: while the generative model predicts the future states, the
planning algorithm generates a preferred sequence of actions for luring the
agent. A sequence of adversarial examples is then crafted to lure the agent to
take the preferred sequence of actions. We apply the two tactics to the agents
trained by the state-of-the-art deep reinforcement learning algorithm including
DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much
reward as the uniform attack (i.e., attacking at every time step) does by
attacking the agent 4 times less often. Our enchanting attack lures the agent
toward designated target states with a more than 70% success rate. Videos are
available at http://yenchenlin.me/adversarial_attack_RL/
| Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu
Liu, Min Sun | null | 1703.06748 | null | null |
Efficient variational Bayesian neural network ensembles for outlier
detection | stat.ML cs.LG | In this work we perform outlier detection using ensembles of neural networks
obtained by variational approximation of the posterior in a Bayesian neural
network setting. The variational parameters are obtained by sampling from the
true posterior by gradient descent. We show our outlier detection results are
comparable to those obtained using other efficient ensembling methods.
| Nick Pawlowski, Miguel Jaques, Ben Glocker | null | 1703.06749 | null | null |
On the Use of Default Parameter Settings in the Empirical Evaluation of
Classification Algorithms | cs.LG stat.ML | We demonstrate that, for a range of state-of-the-art machine learning
algorithms, the differences in generalisation performance obtained using
default parameter settings and using parameters tuned via cross-validation can
be similar in magnitude to the differences in performance observed between
state-of-the-art and uncompetitive learning systems. This means that fair and
rigorous evaluation of new learning algorithms requires performance comparison
against benchmark methods with best-practice model selection procedures, rather
than using default parameter settings. We investigate the sensitivity of three
key machine learning algorithms (support vector machine, random forest and
rotation forest) to their default parameter settings, and provide guidance on
determining sensible default parameter values for implementations of these
algorithms. We also conduct an experimental comparison of these three
algorithms on 121 classification problems and find that, perhaps surprisingly,
rotation forest is significantly more accurate on average than both random
forest and a support vector machine.
| Anthony Bagnall and Gavin C. Cawley | null | 1703.06777 | null | null |
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient
Descent | cs.LG math.OC stat.ML | In this paper, we propose a novel sufficient decrease technique for variance
reduced stochastic gradient descent methods such as SAG, SVRG and SAGA. In
order to make sufficient decrease for stochastic optimization, we design a new
sufficient decrease criterion, which yields sufficient decrease versions of
variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct. We
introduce a coefficient to scale current iterate and satisfy the sufficient
decrease property, which takes the decisions to shrink, expand or move in the
opposite direction, and then give two specific update rules of the coefficient
for Lasso and ridge regression. Moreover, we analyze the convergence properties
of our algorithms for strongly convex problems, which show that both of our
algorithms attain linear convergence rates. We also provide the convergence
guarantees of our algorithms for non-strongly convex problems. Our experimental
results further verify that our algorithms achieve significantly better
performance than their counterparts.
| Fanhua Shang, Yuanyuan Liu, James Cheng, Kelvin Kai Wing Ng, Yuichi
Yoshida | null | 1703.06807 | null | null |
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions | cs.LG cs.NE | The driving force behind deep networks is their ability to compactly
represent rich classes of functions. The primary notion for formally reasoning
about this phenomenon is expressive efficiency, which refers to a situation
where one network must grow unfeasibly large in order to realize (or
approximate) functions of another. To date, expressive efficiency analyses
focused on the architectural feature of depth, showing that deep networks are
representationally superior to shallow ones. In this paper we study the
expressive efficiency brought forth by connectivity, motivated by the
observation that modern networks interconnect their layers in elaborate ways.
We focus on dilated convolutional networks, a family of deep models delivering
state of the art performance in sequence processing tasks. By introducing and
analyzing the concept of mixed tensor decompositions, we prove that
interconnecting dilated convolutional networks can lead to expressive
efficiency. In particular, we show that even a single connection between
intermediate layers can already lead to an almost quadratic gap, which in
large-scale settings typically makes the difference between a model that is
practical and one that is not. Empirical evaluation demonstrates how the
expressive efficiency of connectivity, similarly to that of depth, translates
into gains in accuracy. This leads us to believe that expressive efficiency may
serve a key role in the development of new tools for deep network design.
| Nadav Cohen, Ronen Tamari, Amnon Shashua | null | 1703.06846 | null | null |
Counterfactual Fairness | stat.ML cs.CY cs.LG | Machine learning can impact people with legal or ethical consequences when it
is used to automate decisions in areas such as insurance, lending, hiring, and
predictive policing. In many of these scenarios, previous decisions have been
made that are unfairly biased against certain subpopulations, for example those
of a particular race, gender, or sexual orientation. Since this past data may
be biased, machine learning predictors must account for this to avoid
perpetuating or creating discriminatory practices. In this paper, we develop a
framework for modeling fairness using tools from causal inference. Our
definition of counterfactual fairness captures the intuition that a decision is
fair towards an individual if it is the same in (a) the actual world and (b) a
counterfactual world where the individual belonged to a different demographic
group. We demonstrate our framework on a real-world problem of fair prediction
of success in law school.
| Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva | null | 1703.06856 | null | null |
On the Limitation of Convolutional Neural Networks in Recognizing
Negative Images | cs.CV cs.LG stat.ML | Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance on a variety of computer vision tasks, particularly visual
classification problems, where new algorithms reported to achieve or even
surpass the human performance. In this paper, we examine whether CNNs are
capable of learning the semantics of training data. To this end, we evaluate
CNNs on negative images, since they share the same structure and semantics as
regular images and humans can classify them correctly. Our experimental results
indicate that when training on regular images and testing on negative images,
the model accuracy is significantly lower than when it is tested on regular
images. This leads us to the conjecture that current training methods do not
effectively train models to generalize the concepts. We then introduce the
notion of semantic adversarial examples - transformed inputs that semantically
represent the same objects, but the model does not classify them correctly -
and present negative images as one class of such inputs.
| Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal and Radha Poovendran | null | 1703.06857 | null | null |
Dance Dance Convolution | cs.LG cs.MM cs.NE cs.SD stat.ML | Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players
perform steps on a dance platform in synchronization with music as directed by
on-screen step charts. While many step charts are available in standardized
packs, players may grow tired of existing charts, or wish to dance to a song
for which no chart exists. We introduce the task of learning to choreograph.
Given a raw audio track, the goal is to produce a new step chart. This task
decomposes naturally into two subtasks: deciding when to place steps and
deciding which steps to select. For the step placement task, we combine
recurrent and convolutional neural networks to ingest spectrograms of low-level
audio features to predict steps, conditioned on chart difficulty. For step
selection, we present a conditional LSTM generative model that substantially
outperforms n-gram and fixed-window approaches.
| Chris Donahue, Zachary C. Lipton, Julian McAuley | null | 1703.06891 | null | null |
A Comparison of deep learning methods for environmental sound | cs.SD cs.LG | Environmental sound detection is a challenging application of machine
learning because of the noisy nature of the signal, and the small amount of
(labeled) data that is typically available. This work thus presents a
comparison of several state-of-the-art Deep Learning models on the IEEE
challenge on Detection and Classification of Acoustic Scenes and Events (DCASE)
2016 challenge task and data, classifying sounds into one of fifteen common
indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest
path, library, train, etc. In total, 13 hours of stereo audio recordings are
available, making this one of the largest datasets available. We perform
experiments on six sets of features, including standard Mel-frequency cepstral
coefficients (MFCC), Binaural MFCC, log Mel-spectrum and two different large-
scale temporal pooling features extracted using OpenSMILE. On these features,
we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN),
Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and
i-vector. Using the late-fusion approach, we improve the performance of the
baseline 72.5% by 15.6% in 4-fold Cross Validation (CV) avg. accuracy and 11%
in test accuracy, which matches the best result of the DCASE 2016 challenge.
With large feature sets, deep neural network models out- perform traditional
methods and achieve the best performance among all the studied methods.
Consistent with other work, the best performing single model is the
non-temporal DNN model, which we take as evidence that sounds in the DCASE
challenge do not exhibit strong temporal dynamics.
| Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das | null | 1703.06902 | null | null |
Domain Randomization for Transferring Deep Neural Networks from
Simulation to the Real World | cs.RO cs.LG | Bridging the 'reality gap' that separates simulated robotics from experiments
on hardware could accelerate robotic research through improved data
availability. This paper explores domain randomization, a simple technique for
training models on simulated images that transfer to real images by randomizing
rendering in the simulator. With enough variability in the simulator, the real
world may appear to the model as just another variation. We focus on the task
of object localization, which is a stepping stone to general robotic
manipulation skills. We find that it is possible to train a real-world object
detector that is accurate to $1.5$cm and robust to distractors and partial
occlusions using only data from a simulator with non-realistic random textures.
To demonstrate the capabilities of our detectors, we show they can be used to
perform grasping in a cluttered environment. To our knowledge, this is the
first successful transfer of a deep neural network trained only on simulated
RGB images (without pre-training on real images) to the real world for the
purpose of robotic control.
| Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba,
Pieter Abbeel | null | 1703.06907 | null | null |
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