categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
1611.01400
| null | null |
http://arxiv.org/pdf/1611.01400v1
|
2016-11-04T14:43:44Z
|
2016-11-04T14:43:44Z
|
Learning to Rank Scientific Documents from the Crowd
|
Finding related published articles is an important task in any science, but with the explosion of new work in the biomedical domain it has become especially challenging. Most existing methodologies use text similarity metrics to identify whether two articles are related or not. However biomedical knowledge discovery is hypothesis-driven. The most related articles may not be ones with the highest text similarities. In this study, we first develop an innovative crowd-sourcing approach to build an expert-annotated document-ranking corpus. Using this corpus as the gold standard, we then evaluate the approaches of using text similarity to rank the relatedness of articles. Finally, we develop and evaluate a new supervised model to automatically rank related scientific articles. Our results show that authors' ranking differ significantly from rankings by text-similarity-based models. By training a learning-to-rank model on a subset of the annotated corpus, we found the best supervised learning-to-rank model (SVM-Rank) significantly surpassed state-of-the-art baseline systems.
|
[
"['Jesse M Lingeman' 'Hong Yu']"
] |
cs.IT cs.LG math.IT
|
10.1162/NECO_a_01056
|
1611.01414
| null | null |
http://arxiv.org/abs/1611.01414v3
|
2017-11-07T17:11:42Z
|
2016-11-04T15:12:47Z
|
Information-Theoretic Bounds and Approximations in Neural Population
Coding
|
While Shannon's mutual information has widespread applications in many
disciplines, for practical applications it is often difficult to calculate its
value accurately for high-dimensional variables because of the curse of
dimensionality. This paper is focused on effective approximation methods for
evaluating mutual information in the context of neural population coding. For
large but finite neural populations, we derive several information-theoretic
asymptotic bounds and approximation formulas that remain valid in
high-dimensional spaces. We prove that optimizing the population density
distribution based on these approximation formulas is a convex optimization
problem which allows efficient numerical solutions. Numerical simulation
results confirmed that our asymptotic formulas were highly accurate for
approximating mutual information for large neural populations. In special
cases, the approximation formulas are exactly equal to the true mutual
information. We also discuss techniques of variable transformation and
dimensionality reduction to facilitate computation of the approximations.
|
[
"Wentao Huang and Kechen Zhang",
"['Wentao Huang' 'Kechen Zhang']"
] |
cs.LG cs.AI
| null |
1611.01423
| null | null |
http://arxiv.org/pdf/1611.01423v2
|
2017-06-10T19:18:55Z
|
2016-11-04T15:30:43Z
|
Learning Continuous Semantic Representations of Symbolic Expressions
|
Combining abstract, symbolic reasoning with continuous neural reasoning is a
grand challenge of representation learning. As a step in this direction, we
propose a new architecture, called neural equivalence networks, for the problem
of learning continuous semantic representations of algebraic and logical
expressions. These networks are trained to represent semantic equivalence, even
of expressions that are syntactically very different. The challenge is that
semantic representations must be computed in a syntax-directed manner, because
semantics is compositional, but at the same time, small changes in syntax can
lead to very large changes in semantics, which can be difficult for continuous
neural architectures. We perform an exhaustive evaluation on the task of
checking equivalence on a highly diverse class of symbolic algebraic and
boolean expression types, showing that our model significantly outperforms
existing architectures.
|
[
"['Miltiadis Allamanis' 'Pankajan Chanthirasegaran' 'Pushmeet Kohli'\n 'Charles Sutton']",
"Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli,\n Charles Sutton"
] |
cs.NE cs.LG
| null |
1611.01427
| null | null |
http://arxiv.org/pdf/1611.01427v3
|
2017-03-30T19:51:47Z
|
2016-11-04T15:47:32Z
|
Sparsely-Connected Neural Networks: Towards Efficient VLSI
Implementation of Deep Neural Networks
|
Recently deep neural networks have received considerable attention due to
their ability to extract and represent high-level abstractions in data sets.
Deep neural networks such as fully-connected and convolutional neural networks
have shown excellent performance on a wide range of recognition and
classification tasks. However, their hardware implementations currently suffer
from large silicon area and high power consumption due to the their high degree
of complexity. The power/energy consumption of neural networks is dominated by
memory accesses, the majority of which occur in fully-connected networks. In
fact, they contain most of the deep neural network parameters. In this paper,
we propose sparsely-connected networks, by showing that the number of
connections in fully-connected networks can be reduced by up to 90% while
improving the accuracy performance on three popular datasets (MNIST, CIFAR10
and SVHN). We then propose an efficient hardware architecture based on
linear-feedback shift registers to reduce the memory requirements of the
proposed sparsely-connected networks. The proposed architecture can save up to
90% of memory compared to the conventional implementations of fully-connected
neural networks. Moreover, implementation results show up to 84% reduction in
the energy consumption of a single neuron of the proposed sparsely-connected
networks compared to a single neuron of fully-connected neural networks.
|
[
"['Arash Ardakani' 'Carlo Condo' 'Warren J. Gross']",
"Arash Ardakani, Carlo Condo and Warren J. Gross"
] |
cs.LG
| null |
1611.01449
| null | null |
http://arxiv.org/pdf/1611.01449v2
|
2018-12-04T15:39:28Z
|
2016-11-04T16:39:20Z
|
Semi-supervised deep learning by metric embedding
|
Deep networks are successfully used as classification models yielding
state-of-the-art results when trained on a large number of labeled samples.
These models, however, are usually much less suited for semi-supervised
problems because of their tendency to overfit easily when trained on small
amounts of data. In this work we will explore a new training objective that is
targeting a semi-supervised regime with only a small subset of labeled data.
This criterion is based on a deep metric embedding over distance relations
within the set of labeled samples, together with constraints over the
embeddings of the unlabeled set. The final learned representations are
discriminative in euclidean space, and hence can be used with subsequent
nearest-neighbor classification using the labeled samples.
|
[
"Elad Hoffer, Nir Ailon",
"['Elad Hoffer' 'Nir Ailon']"
] |
cs.LG cs.AI stat.ML
| null |
1611.01455
| null | null |
http://arxiv.org/pdf/1611.01455v1
|
2016-11-04T17:08:54Z
|
2016-11-04T17:08:54Z
|
Ways of Conditioning Generative Adversarial Networks
|
The GANs are generative models whose random samples realistically reflect
natural images. It also can generate samples with specific attributes by
concatenating a condition vector into the input, yet research on this field is
not well studied. We propose novel methods of conditioning generative
adversarial networks (GANs) that achieve state-of-the-art results on MNIST and
CIFAR-10. We mainly introduce two models: an information retrieving model that
extracts conditional information from the samples, and a spatial bilinear
pooling model that forms bilinear features derived from the spatial cross
product of an image and a condition vector. These methods significantly enhance
log-likelihood of test data under the conditional distributions compared to the
methods of concatenation.
|
[
"['Hanock Kwak' 'Byoung-Tak Zhang']",
"Hanock Kwak and Byoung-Tak Zhang"
] |
cs.LG cs.SI stat.ML
| null |
1611.01456
| null | null |
http://arxiv.org/pdf/1611.01456v1
|
2016-11-04T17:16:17Z
|
2016-11-04T17:16:17Z
|
Learning heat diffusion graphs
|
Effective information analysis generally boils down to properly identifying
the structure or geometry of the data, which is often represented by a graph.
In some applications, this structure may be partly determined by design
constraints or pre-determined sensing arrangements, like in road transportation
networks for example. In general though, the data structure is not readily
available and becomes pretty difficult to define. In particular, the global
smoothness assumptions, that most of the existing works adopt, are often too
general and unable to properly capture localized properties of data. In this
paper, we go beyond this classical data model and rather propose to represent
information as a sparse combination of localized functions that live on a data
structure represented by a graph. Based on this model, we focus on the problem
of inferring the connectivity that best explains the data samples at different
vertices of a graph that is a priori unknown. We concentrate on the case where
the observed data is actually the sum of heat diffusion processes, which is a
quite common model for data on networks or other irregular structures. We cast
a new graph learning problem and solve it with an efficient nonconvex
optimization algorithm. Experiments on both synthetic and real world data
finally illustrate the benefits of the proposed graph learning framework and
confirm that the data structure can be efficiently learned from data
observations only. We believe that our algorithm will help solving key
questions in diverse application domains such as social and biological network
analysis where it is crucial to unveil proper geometry for data understanding
and inference.
|
[
"['Dorina Thanou' 'Xiaowen Dong' 'Daniel Kressner' 'Pascal Frossard']",
"Dorina Thanou, Xiaowen Dong, Daniel Kressner, and Pascal Frossard"
] |
cs.LG
| null |
1611.01457
| null | null |
http://arxiv.org/pdf/1611.01457v4
|
2017-05-23T18:52:37Z
|
2016-11-04T17:20:22Z
|
Multi-task learning with deep model based reinforcement learning
|
In recent years, model-free methods that use deep learning have achieved
great success in many different reinforcement learning environments. Most
successful approaches focus on solving a single task, while multi-task
reinforcement learning remains an open problem. In this paper, we present a
model based approach to deep reinforcement learning which we use to solve
different tasks simultaneously. We show that our approach not only does not
degrade but actually benefits from learning multiple tasks. For our model, we
also present a new kind of recurrent neural network inspired by residual
networks that decouples memory from computation allowing to model complex
environments that do not require lots of memory.
|
[
"Asier Mujika",
"['Asier Mujika']"
] |
cs.LG cs.CL stat.ML
| null |
1611.01462
| null | null |
http://arxiv.org/pdf/1611.01462v3
|
2017-03-11T19:13:52Z
|
2016-11-04T17:36:20Z
|
Tying Word Vectors and Word Classifiers: A Loss Framework for Language
Modeling
|
Recurrent neural networks have been very successful at predicting sequences
of words in tasks such as language modeling. However, all such models are based
on the conventional classification framework, where the model is trained
against one-hot targets, and each word is represented both as an input and as
an output in isolation. This causes inefficiencies in learning both in terms of
utilizing all of the information and in terms of the number of parameters
needed to train. We introduce a novel theoretical framework that facilitates
better learning in language modeling, and show that our framework leads to
tying together the input embedding and the output projection matrices, greatly
reducing the number of trainable variables. Our framework leads to state of the
art performance on the Penn Treebank with a variety of network models.
|
[
"['Hakan Inan' 'Khashayar Khosravi' 'Richard Socher']",
"Hakan Inan, Khashayar Khosravi, Richard Socher"
] |
cs.LG cond-mat.dis-nn cs.AI cs.CC stat.ML
| null |
1611.01491
| null | null |
http://arxiv.org/pdf/1611.01491v6
|
2018-02-28T02:23:47Z
|
2016-11-04T18:54:50Z
|
Understanding Deep Neural Networks with Rectified Linear Units
|
In this paper we investigate the family of functions representable by deep
neural networks (DNN) with rectified linear units (ReLU). We give an algorithm
to train a ReLU DNN with one hidden layer to *global optimality* with runtime
polynomial in the data size albeit exponential in the input dimension. Further,
we improve on the known lower bounds on size (from exponential to super
exponential) for approximating a ReLU deep net function by a shallower ReLU
net. Our gap theorems hold for smoothly parametrized families of "hard"
functions, contrary to countable, discrete families known in the literature. An
example consequence of our gap theorems is the following: for every natural
number $k$ there exists a function representable by a ReLU DNN with $k^2$
hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$
hidden layers will require at least $\frac{1}{2}k^{k+1}-1$ total nodes.
Finally, for the family of $\mathbb{R}^n\to \mathbb{R}$ DNNs with ReLU
activations, we show a new lowerbound on the number of affine pieces, which is
larger than previous constructions in certain regimes of the network
architecture and most distinctively our lowerbound is demonstrated by an
explicit construction of a *smoothly parameterized* family of functions
attaining this scaling. Our construction utilizes the theory of zonotopes from
polyhedral theory.
|
[
"Raman Arora, Amitabh Basu, Poorya Mianjy and Anirbit Mukherjee",
"['Raman Arora' 'Amitabh Basu' 'Poorya Mianjy' 'Anirbit Mukherjee']"
] |
cs.LG q-bio.BM
| null |
1611.01503
| null | null |
http://arxiv.org/pdf/1611.01503v1
|
2016-11-04T19:32:15Z
|
2016-11-04T19:32:15Z
|
Protein Secondary Structure Prediction Using Deep Multi-scale
Convolutional Neural Networks and Next-Step Conditioning
|
Recently developed deep learning techniques have significantly improved the
accuracy of various speech and image recognition systems. In this paper we
adapt some of these techniques for protein secondary structure prediction. We
first train a series of deep neural networks to predict eight-class secondary
structure labels given a protein's amino acid sequence information and find
that using recent methods for regularization, such as dropout and weight-norm
constraining, leads to measurable gains in accuracy. We then adapt recent
convolutional neural network architectures--Inception, ReSNet, and DenseNet
with Batch Normalization--to the problem of protein structure prediction. These
convolutional architectures make heavy use of multi-scale filter layers that
simultaneously compute features on several scales, and use residual connections
to prevent underfitting. Using a carefully modified version of these
architectures, we achieve state-of-the-art performance of 70.0% per amino acid
accuracy on the public CB513 benchmark dataset. Finally, we explore additions
from sequence-to-sequence learning, altering the model to make its predictions
conditioned on both the protein's amino acid sequence and its past secondary
structure labels. We introduce a new method of ensembling such a conditional
model with our convolutional model, an approach which reaches 70.6% Q8 accuracy
on CB513. We argue that these results can be further refined for larger boosts
in prediction accuracy through more sophisticated attempts to control
overfitting of conditional models. We aim to release the code for these
experiments as part of the TensorFlow repository.
|
[
"Akosua Busia, Jasmine Collins, Navdeep Jaitly",
"['Akosua Busia' 'Jasmine Collins' 'Navdeep Jaitly']"
] |
stat.ML cs.AI cs.LG
| null |
1611.01504
| null | null |
http://arxiv.org/pdf/1611.01504v1
|
2016-11-04T19:33:35Z
|
2016-11-04T19:33:35Z
|
Estimating Causal Direction and Confounding of Two Discrete Variables
|
We propose a method to classify the causal relationship between two discrete
variables given only the joint distribution of the variables, acknowledging
that the method is subject to an inherent baseline error. We assume that the
causal system is acyclicity, but we do allow for hidden common causes. Our
algorithm presupposes that the probability distributions $P(C)$ of a cause $C$
is independent from the probability distribution $P(E\mid C)$ of the
cause-effect mechanism. While our classifier is trained with a Bayesian
assumption of flat hyperpriors, we do not make this assumption about our test
data. This work connects to recent developments on the identifiability of
causal models over continuous variables under the assumption of "independent
mechanisms". Carefully-commented Python notebooks that reproduce all our
experiments are available online at
http://vision.caltech.edu/~kchalupk/code.html.
|
[
"Krzysztof Chalupka, Frederick Eberhardt and Pietro Perona",
"['Krzysztof Chalupka' 'Frederick Eberhardt' 'Pietro Perona']"
] |
cs.LG
| null |
1611.01505
| null | null |
http://arxiv.org/pdf/1611.01505v3
|
2018-06-11T04:28:33Z
|
2016-11-04T19:42:45Z
|
Eve: A Gradient Based Optimization Method with Locally and Globally
Adaptive Learning Rates
|
Adaptive gradient methods for stochastic optimization adjust the learning
rate for each parameter locally. However, there is also a global learning rate
which must be tuned in order to get the best performance. In this paper, we
present a new algorithm that adapts the learning rate locally for each
parameter separately, and also globally for all parameters together.
Specifically, we modify Adam, a popular method for training deep learning
models, with a coefficient that captures properties of the objective function.
Empirically, we show that our method, which we call Eve, outperforms Adam and
other popular methods in training deep neural networks, like convolutional
neural networks for image classification, and recurrent neural networks for
language tasks.
|
[
"Hiroaki Hayashi, Jayanth Koushik, Graham Neubig",
"['Hiroaki Hayashi' 'Jayanth Koushik' 'Graham Neubig']"
] |
stat.ML cs.LG
| null |
1611.0154
| null | null | null | null | null |
Topology and Geometry of Half-Rectified Network Optimization
|
The loss surface of deep neural networks has recently attracted interest in
the optimization and machine learning communities as a prime example of
high-dimensional non-convex problem. Some insights were recently gained using
spin glass models and mean-field approximations, but at the expense of strongly
simplifying the nonlinear nature of the model.
In this work, we do not make any such assumption and study conditions on the
data distribution and model architecture that prevent the existence of bad
local minima. Our theoretical work quantifies and formalizes two important
\emph{folklore} facts: (i) the landscape of deep linear networks has a
radically different topology from that of deep half-rectified ones, and (ii)
that the energy landscape in the non-linear case is fundamentally controlled by
the interplay between the smoothness of the data distribution and model
over-parametrization. Our main theoretical contribution is to prove that
half-rectified single layer networks are asymptotically connected, and we
provide explicit bounds that reveal the aforementioned interplay.
The conditioning of gradient descent is the next challenge we address. We
study this question through the geometry of the level sets, and we introduce an
algorithm to efficiently estimate the regularity of such sets on large-scale
networks. Our empirical results show that these level sets remain connected
throughout all the learning phase, suggesting a near convex behavior, but they
become exponentially more curvy as the energy level decays, in accordance to
what is observed in practice with very low curvature attractors.
|
[
"C. Daniel Freeman and Joan Bruna"
] |
null | null |
1611.01540
| null | null |
http://arxiv.org/pdf/1611.01540v4
|
2017-06-01T19:46:41Z
|
2016-11-04T21:17:42Z
|
Topology and Geometry of Half-Rectified Network Optimization
|
The loss surface of deep neural networks has recently attracted interest in the optimization and machine learning communities as a prime example of high-dimensional non-convex problem. Some insights were recently gained using spin glass models and mean-field approximations, but at the expense of strongly simplifying the nonlinear nature of the model. In this work, we do not make any such assumption and study conditions on the data distribution and model architecture that prevent the existence of bad local minima. Our theoretical work quantifies and formalizes two important emph{folklore} facts: (i) the landscape of deep linear networks has a radically different topology from that of deep half-rectified ones, and (ii) that the energy landscape in the non-linear case is fundamentally controlled by the interplay between the smoothness of the data distribution and model over-parametrization. Our main theoretical contribution is to prove that half-rectified single layer networks are asymptotically connected, and we provide explicit bounds that reveal the aforementioned interplay. The conditioning of gradient descent is the next challenge we address. We study this question through the geometry of the level sets, and we introduce an algorithm to efficiently estimate the regularity of such sets on large-scale networks. Our empirical results show that these level sets remain connected throughout all the learning phase, suggesting a near convex behavior, but they become exponentially more curvy as the energy level decays, in accordance to what is observed in practice with very low curvature attractors.
|
[
"['C. Daniel Freeman' 'Joan Bruna']"
] |
stat.ML cs.LG
| null |
1611.01541
| null | null |
http://arxiv.org/pdf/1611.01541v1
|
2016-11-04T21:21:53Z
|
2016-11-04T21:21:53Z
|
Classification with Ultrahigh-Dimensional Features
|
Although much progress has been made in classification with high-dimensional
features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014},
classification with ultrahigh-dimensional features, wherein the features much
outnumber the sample size, defies most existing work. This paper introduces a
novel and computationally feasible multivariate screening and classification
method for ultrahigh-dimensional data. Leveraging inter-feature correlations,
the proposed method enables detection of marginally weak and sparse signals and
recovery of the true informative feature set, and achieves asymptotic optimal
misclassification rates. We also show that the proposed procedure provides more
powerful discovery boundaries compared to those in \citet{CaiSun:2014} and
\citet{JJin:2009}. The performance of the proposed procedure is evaluated using
simulation studies and demonstrated via classification of patients with
different post-transplantation renal functional types.
|
[
"Yanming Li, Hyokyoung Hong, Jian Kang, Kevin He, Ji Zhu, Yi Li",
"['Yanming Li' 'Hyokyoung Hong' 'Jian Kang' 'Kevin He' 'Ji Zhu' 'Yi Li']"
] |
cs.CL cs.LG
| null |
1611.01547
| null | null |
http://arxiv.org/pdf/1611.01547v5
|
2017-04-05T15:26:51Z
|
2016-11-04T21:35:07Z
|
Automated Generation of Multilingual Clusters for the Evaluation of
Distributed Representations
|
We propose a language-agnostic way of automatically generating sets of
semantically similar clusters of entities along with sets of "outlier"
elements, which may then be used to perform an intrinsic evaluation of word
embeddings in the outlier detection task. We used our methodology to create a
gold-standard dataset, which we call WikiSem500, and evaluated multiple
state-of-the-art embeddings. The results show a correlation between performance
on this dataset and performance on sentiment analysis.
|
[
"['Philip Blair' 'Yuval Merhav' 'Joel Barry']",
"Philip Blair, Yuval Merhav, and Joel Barry"
] |
cs.NE cs.AI cs.CL cs.LG
| null |
1611.01576
| null | null |
http://arxiv.org/pdf/1611.01576v2
|
2016-11-21T20:52:34Z
|
2016-11-05T00:31:25Z
|
Quasi-Recurrent Neural Networks
|
Recurrent neural networks are a powerful tool for modeling sequential data,
but the dependence of each timestep's computation on the previous timestep's
output limits parallelism and makes RNNs unwieldy for very long sequences. We
introduce quasi-recurrent neural networks (QRNNs), an approach to neural
sequence modeling that alternates convolutional layers, which apply in parallel
across timesteps, and a minimalist recurrent pooling function that applies in
parallel across channels. Despite lacking trainable recurrent layers, stacked
QRNNs have better predictive accuracy than stacked LSTMs of the same hidden
size. Due to their increased parallelism, they are up to 16 times faster at
train and test time. Experiments on language modeling, sentiment
classification, and character-level neural machine translation demonstrate
these advantages and underline the viability of QRNNs as a basic building block
for a variety of sequence tasks.
|
[
"['James Bradbury' 'Stephen Merity' 'Caiming Xiong' 'Richard Socher']",
"James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher"
] |
cs.LG cs.AI cs.NE
| null |
1611.01578
| null | null |
http://arxiv.org/pdf/1611.01578v2
|
2017-02-15T05:28:05Z
|
2016-11-05T00:41:37Z
|
Neural Architecture Search with Reinforcement Learning
|
Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and train this RNN with reinforcement learning to maximize the
expected accuracy of the generated architectures on a validation set. On the
CIFAR-10 dataset, our method, starting from scratch, can design a novel network
architecture that rivals the best human-invented architecture in terms of test
set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is
0.09 percent better and 1.05x faster than the previous state-of-the-art model
that used a similar architectural scheme. On the Penn Treebank dataset, our
model can compose a novel recurrent cell that outperforms the widely-used LSTM
cell, and other state-of-the-art baselines. Our cell achieves a test set
perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than
the previous state-of-the-art model. The cell can also be transferred to the
character language modeling task on PTB and achieves a state-of-the-art
perplexity of 1.214.
|
[
"['Barret Zoph' 'Quoc V. Le']",
"Barret Zoph and Quoc V. Le"
] |
cs.LG stat.ML
|
10.1007/s10994-016-5604-6
|
1611.01586
| null | null |
http://arxiv.org/abs/1611.01586v1
|
2016-11-05T01:58:12Z
|
2016-11-05T01:58:12Z
|
Class-prior Estimation for Learning from Positive and Unlabeled Data
|
We consider the problem of estimating the class prior in an unlabeled
dataset. Under the assumption that an additional labeled dataset is available,
the class prior can be estimated by fitting a mixture of class-wise data
distributions to the unlabeled data distribution. However, in practice, such an
additional labeled dataset is often not available. In this paper, we show that,
with additional samples coming only from the positive class, the class prior of
the unlabeled dataset can be estimated correctly. Our key idea is to use
properly penalized divergences for model fitting to cancel the error caused by
the absence of negative samples. We further show that the use of the penalized
$L_1$-distance gives a computationally efficient algorithm with an analytic
solution. The consistency, stability, and estimation error are theoretically
analyzed. Finally, we experimentally demonstrate the usefulness of the proposed
method.
|
[
"['Marthinus C. du Plessis' 'Gang Niu' 'Masashi Sugiyama']",
"Marthinus C. du Plessis, Gang Niu, and Masashi Sugiyama"
] |
cs.LG cs.CL cs.CV
| null |
1611.01599
| null | null |
http://arxiv.org/pdf/1611.01599v2
|
2016-12-16T16:09:34Z
|
2016-11-05T04:05:18Z
|
LipNet: End-to-End Sentence-level Lipreading
|
Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, existing work on models trained end-to-end perform only word
classification, rather than sentence-level sequence prediction. Studies have
shown that human lipreading performance increases for longer words (Easton &
Basala, 1982), indicating the importance of features capturing temporal context
in an ambiguous communication channel. Motivated by this observation, we
present LipNet, a model that maps a variable-length sequence of video frames to
text, making use of spatiotemporal convolutions, a recurrent network, and the
connectionist temporal classification loss, trained entirely end-to-end. To the
best of our knowledge, LipNet is the first end-to-end sentence-level lipreading
model that simultaneously learns spatiotemporal visual features and a sequence
model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level,
overlapped speaker split task, outperforming experienced human lipreaders and
the previous 86.4% word-level state-of-the-art accuracy (Gergen et al., 2016).
|
[
"['Yannis M. Assael' 'Brendan Shillingford' 'Shimon Whiteson'\n 'Nando de Freitas']",
"Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de\n Freitas"
] |
cs.NE cs.LG
| null |
1611.016
| null | null | null | null | null |
Loss-aware Binarization of Deep Networks
|
Deep neural network models, though very powerful and highly successful, are
computationally expensive in terms of space and time. Recently, there have been
a number of attempts on binarizing the network weights and activations. This
greatly reduces the network size, and replaces the underlying multiplications
to additions or even XNOR bit operations. However, existing binarization
schemes are based on simple matrix approximation and ignore the effect of
binarization on the loss. In this paper, we propose a proximal Newton algorithm
with diagonal Hessian approximation that directly minimizes the loss w.r.t. the
binarized weights. The underlying proximal step has an efficient closed-form
solution, and the second-order information can be efficiently obtained from the
second moments already computed by the Adam optimizer. Experiments on both
feedforward and recurrent networks show that the proposed loss-aware
binarization algorithm outperforms existing binarization schemes, and is also
more robust for wide and deep networks.
|
[
"Lu Hou, Quanming Yao, James T. Kwok"
] |
null | null |
1611.01600
| null | null |
http://arxiv.org/pdf/1611.01600v3
|
2018-05-10T11:20:09Z
|
2016-11-05T04:23:42Z
|
Loss-aware Binarization of Deep Networks
|
Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on binarizing the network weights and activations. This greatly reduces the network size, and replaces the underlying multiplications to additions or even XNOR bit operations. However, existing binarization schemes are based on simple matrix approximation and ignore the effect of binarization on the loss. In this paper, we propose a proximal Newton algorithm with diagonal Hessian approximation that directly minimizes the loss w.r.t. the binarized weights. The underlying proximal step has an efficient closed-form solution, and the second-order information can be efficiently obtained from the second moments already computed by the Adam optimizer. Experiments on both feedforward and recurrent networks show that the proposed loss-aware binarization algorithm outperforms existing binarization schemes, and is also more robust for wide and deep networks.
|
[
"['Lu Hou' 'Quanming Yao' 'James T. Kwok']"
] |
cs.LG stat.ML
| null |
1611.01606
| null | null |
http://arxiv.org/pdf/1611.01606v1
|
2016-11-05T05:42:40Z
|
2016-11-05T05:42:40Z
|
Learning to Play in a Day: Faster Deep Reinforcement Learning by
Optimality Tightening
|
We propose a novel training algorithm for reinforcement learning which
combines the strength of deep Q-learning with a constrained optimization
approach to tighten optimality and encourage faster reward propagation. Our
novel technique makes deep reinforcement learning more practical by drastically
reducing the training time. We evaluate the performance of our approach on the
49 games of the challenging Arcade Learning Environment, and report significant
improvements in both training time and accuracy.
|
[
"['Frank S. He' 'Yang Liu' 'Alexander G. Schwing' 'Jian Peng']",
"Frank S. He and Yang Liu and Alexander G. Schwing and Jian Peng"
] |
cs.LG cs.AI math.OC stat.ML
| null |
1611.01626
| null | null |
http://arxiv.org/pdf/1611.01626v3
|
2017-04-07T15:20:05Z
|
2016-11-05T10:49:37Z
|
Combining policy gradient and Q-learning
|
Policy gradient is an efficient technique for improving a policy in a
reinforcement learning setting. However, vanilla online variants are on-policy
only and not able to take advantage of off-policy data. In this paper we
describe a new technique that combines policy gradient with off-policy
Q-learning, drawing experience from a replay buffer. This is motivated by
making a connection between the fixed points of the regularized policy gradient
algorithm and the Q-values. This connection allows us to estimate the Q-values
from the action preferences of the policy, to which we apply Q-learning
updates. We refer to the new technique as 'PGQL', for policy gradient and
Q-learning. We also establish an equivalency between action-value fitting
techniques and actor-critic algorithms, showing that regularized policy
gradient techniques can be interpreted as advantage function learning
algorithms. We conclude with some numerical examples that demonstrate improved
data efficiency and stability of PGQL. In particular, we tested PGQL on the
full suite of Atari games and achieved performance exceeding that of both
asynchronous advantage actor-critic (A3C) and Q-learning.
|
[
"Brendan O'Donoghue, Remi Munos, Koray Kavukcuoglu and Volodymyr Mnih",
"[\"Brendan O'Donoghue\" 'Remi Munos' 'Koray Kavukcuoglu' 'Volodymyr Mnih']"
] |
cs.LG cs.CV cs.NE q-bio.NC
| null |
1611.01639
| null | null |
http://arxiv.org/pdf/1611.01639v7
|
2018-01-20T13:44:32Z
|
2016-11-05T12:32:16Z
|
Robustly representing uncertainty in deep neural networks through
sampling
|
As deep neural networks (DNNs) are applied to increasingly challenging
problems, they will need to be able to represent their own uncertainty.
Modeling uncertainty is one of the key features of Bayesian methods. Using
Bernoulli dropout with sampling at prediction time has recently been proposed
as an efficient and well performing variational inference method for DNNs.
However, sampling from other multiplicative noise based variational
distributions has not been investigated in depth. We evaluated Bayesian DNNs
trained with Bernoulli or Gaussian multiplicative masking of either the units
(dropout) or the weights (dropconnect). We tested the calibration of the
probabilistic predictions of Bayesian convolutional neural networks (CNNs) on
MNIST and CIFAR-10. Sampling at prediction time increased the calibration of
the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or
Bernoulli, led to more robust representation of uncertainty compared to
sampling of units. However, using either Gaussian or Bernoulli dropout led to
increased test set classification accuracy. Based on these findings we used
both Bernoulli dropout and Gaussian dropconnect concurrently, which we show
approximates the use of a spike-and-slab variational distribution without
increasing the number of learned parameters. We found that spike-and-slab
sampling had higher test set performance than Gaussian dropconnect and more
robustly represented its uncertainty compared to Bernoulli dropout.
|
[
"Patrick McClure, Nikolaus Kriegeskorte",
"['Patrick McClure' 'Nikolaus Kriegeskorte']"
] |
cs.DM cs.DS cs.IT cs.LG math.CO math.IT
| null |
1611.01655
| null | null |
http://arxiv.org/pdf/1611.01655v3
|
2017-04-25T10:44:06Z
|
2016-11-05T13:55:25Z
|
Twenty (simple) questions
|
A basic combinatorial interpretation of Shannon's entropy function is via the
"20 questions" game. This cooperative game is played by two players, Alice and
Bob: Alice picks a distribution $\pi$ over the numbers $\{1,\ldots,n\}$, and
announces it to Bob. She then chooses a number $x$ according to $\pi$, and Bob
attempts to identify $x$ using as few Yes/No queries as possible, on average.
An optimal strategy for the "20 questions" game is given by a Huffman code
for $\pi$: Bob's questions reveal the codeword for $x$ bit by bit. This
strategy finds $x$ using fewer than $H(\pi)+1$ questions on average. However,
the questions asked by Bob could be arbitrary. In this paper, we investigate
the following question: Are there restricted sets of questions that match the
performance of Huffman codes, either exactly or approximately?
Our first main result shows that for every distribution $\pi$, Bob has a
strategy that uses only questions of the form "$x < c$?" and "$x = c$?", and
uncovers $x$ using at most $H(\pi)+1$ questions on average, matching the
performance of Huffman codes in this sense. We also give a natural set of
$O(rn^{1/r})$ questions that achieve a performance of at most $H(\pi)+r$, and
show that $\Omega(rn^{1/r})$ questions are required to achieve such a
guarantee.
Our second main result gives a set $\mathcal{Q}$ of $1.25^{n+o(n)}$ questions
such that for every distribution $\pi$, Bob can implement an optimal strategy
for $\pi$ using only questions from $\mathcal{Q}$. We also show that
$1.25^{n-o(n)}$ questions are needed, for infinitely many $n$. If we allow a
small slack of $r$ over the optimal strategy, then roughly $(rn)^{\Theta(1/r)}$
questions are necessary and sufficient.
|
[
"['Yuval Dagan' 'Yuval Filmus' 'Ariel Gabizon' 'Shay Moran']",
"Yuval Dagan, Yuval Filmus, Ariel Gabizon, Shay Moran"
] |
cs.LG cs.MA cs.NE
| null |
1611.01673
| null | null |
http://arxiv.org/pdf/1611.01673v3
|
2017-03-02T21:20:59Z
|
2016-11-05T16:56:44Z
|
Generative Multi-Adversarial Networks
|
Generative adversarial networks (GANs) are a framework for producing a
generative model by way of a two-player minimax game. In this paper, we propose
the \emph{Generative Multi-Adversarial Network} (GMAN), a framework that
extends GANs to multiple discriminators. In previous work, the successful
training of GANs requires modifying the minimax objective to accelerate
training early on. In contrast, GMAN can be reliably trained with the original,
untampered objective. We explore a number of design perspectives with the
discriminator role ranging from formidable adversary to forgiving teacher.
Image generation tasks comparing the proposed framework to standard GANs
demonstrate GMAN produces higher quality samples in a fraction of the
iterations when measured by a pairwise GAM-type metric.
|
[
"Ishan Durugkar, Ian Gemp, Sridhar Mahadevan",
"['Ishan Durugkar' 'Ian Gemp' 'Sridhar Mahadevan']"
] |
cs.LG cs.NE
| null |
1611.01678
| null | null |
http://arxiv.org/pdf/1611.01678v1
|
2016-11-05T17:17:14Z
|
2016-11-05T17:17:14Z
|
Comparing learning algorithms in neural network for diagnosing
cardiovascular disease
|
Today data mining techniques are exploited in medical science for diagnosing,
overcoming and treating diseases. Neural network is one of the techniques which
are widely used for diagnosis in medical field. In this article efficiency of
nine algorithms, which are basis of neural network learning in diagnosing
cardiovascular diseases, will be assessed. Algorithms are assessed in terms of
accuracy, sensitivity, transparency, AROC and convergence rate by means of 10
fold cross validation. The results suggest that in training phase, Lonberg-M
algorithm has the best efficiency in terms of all metrics, algorithm OSS has
maximum accuracy in testing phase, algorithm SCG has the maximum transparency
and algorithm CGB has the maximum sensitivity.
|
[
"['Mirmorsal Madani']",
"Mirmorsal Madani"
] |
cs.LG cs.DS cs.GT
| null |
1611.01688
| null | null |
http://arxiv.org/pdf/1611.01688v3
|
2019-08-05T14:10:08Z
|
2016-11-05T18:54:59Z
|
Oracle-Efficient Online Learning and Auction Design
|
We consider the design of computationally efficient online learning
algorithms in an adversarial setting in which the learner has access to an
offline optimization oracle. We present an algorithm called Generalized
Follow-the-Perturbed-Leader and provide conditions under which it is
oracle-efficient while achieving vanishing regret. Our results make significant
progress on an open problem raised by Hazan and Koren, who showed that
oracle-efficient algorithms do not exist in general and asked whether one can
identify properties under which oracle-efficient online learning may be
possible.
Our auction-design framework considers an auctioneer learning an optimal
auction for a sequence of adversarially selected valuations with the goal of
achieving revenue that is almost as good as the optimal auction in hindsight,
among a class of auctions. We give oracle-efficient learning results for: (1)
VCG auctions with bidder-specific reserves in single-parameter settings, (2)
envy-free item pricing in multi-item auctions, and (3) s-level auctions of
Morgenstern and Roughgarden for single-item settings. The last result leads to
an approximation of the overall optimal Myerson auction when bidders'
valuations are drawn according to a fast-mixing Markov process, extending prior
work that only gave such guarantees for the i.i.d. setting.
Finally, we derive various extensions, including: (1) oracle-efficient
algorithms for the contextual learning setting in which the learner has access
to side information (such as bidder demographics), (2) learning with
approximate oracles such as those based on Maximal-in-Range algorithms, and (3)
no-regret bidding in simultaneous auctions, resolving an open problem of
Daskalakis and Syrgkanis.
|
[
"['Miroslav Dudík' 'Nika Haghtalab' 'Haipeng Luo' 'Robert E. Schapire'\n 'Vasilis Syrgkanis' 'Jennifer Wortman Vaughan']",
"Miroslav Dud\\'ik, Nika Haghtalab, Haipeng Luo, Robert E. Schapire,\n Vasilis Syrgkanis, Jennifer Wortman Vaughan"
] |
cs.CL cs.AI cs.LG stat.ML
| null |
1611.01702
| null | null |
http://arxiv.org/pdf/1611.01702v2
|
2017-02-27T03:03:38Z
|
2016-11-05T21:25:07Z
|
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
|
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based
language model designed to directly capture the global semantic meaning
relating words in a document via latent topics. Because of their sequential
nature, RNNs are good at capturing the local structure of a word sequence -
both semantic and syntactic - but might face difficulty remembering long-range
dependencies. Intuitively, these long-range dependencies are of semantic
nature. In contrast, latent topic models are able to capture the global
underlying semantic structure of a document but do not account for word
ordering. The proposed TopicRNN model integrates the merits of RNNs and latent
topic models: it captures local (syntactic) dependencies using an RNN and
global (semantic) dependencies using latent topics. Unlike previous work on
contextual RNN language modeling, our model is learned end-to-end. Empirical
results on word prediction show that TopicRNN outperforms existing contextual
RNN baselines. In addition, TopicRNN can be used as an unsupervised feature
extractor for documents. We do this for sentiment analysis on the IMDB movie
review dataset and report an error rate of $6.28\%$. This is comparable to the
state-of-the-art $5.91\%$ resulting from a semi-supervised approach. Finally,
TopicRNN also yields sensible topics, making it a useful alternative to
document models such as latent Dirichlet allocation.
|
[
"Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley",
"['Adji B. Dieng' 'Chong Wang' 'Jianfeng Gao' 'John Paisley']"
] |
stat.ML cs.AI cs.LG
| null |
1611.01708
| null | null |
http://arxiv.org/pdf/1611.01708v2
|
2017-03-27T03:07:49Z
|
2016-11-05T23:02:25Z
|
Detecting Dependencies in Sparse, Multivariate Databases Using
Probabilistic Programming and Non-parametric Bayes
|
Datasets with hundreds of variables and many missing values are commonplace.
In this setting, it is both statistically and computationally challenging to
detect true predictive relationships between variables and also to suppress
false positives. This paper proposes an approach that combines probabilistic
programming, information theory, and non-parametric Bayes. It shows how to use
Bayesian non-parametric modeling to (i) build an ensemble of joint probability
models for all the variables; (ii) efficiently detect marginal independencies;
and (iii) estimate the conditional mutual information between arbitrary subsets
of variables, subject to a broad class of constraints. Users can access these
capabilities using BayesDB, a probabilistic programming platform for
probabilistic data analysis, by writing queries in a simple, SQL-like language.
This paper demonstrates empirically that the method can (i) detect
context-specific (in)dependencies on challenging synthetic problems and (ii)
yield improved sensitivity and specificity over baselines from statistics and
machine learning, on a real-world database of over 300 sparsely observed
indicators of macroeconomic development and public health.
|
[
"Feras Saad, Vikash Mansinghka",
"['Feras Saad' 'Vikash Mansinghka']"
] |
cs.LG cs.CL
| null |
1611.01714
| null | null |
http://arxiv.org/pdf/1611.01714v1
|
2016-11-06T01:32:39Z
|
2016-11-06T01:32:39Z
|
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
|
In this paper we present a technique to train neural network models on small
amounts of data. Current methods for training neural networks on small amounts
of rich data typically rely on strategies such as fine-tuning a pre-trained
neural network or the use of domain-specific hand-engineered features. Here we
take the approach of treating network layers, or entire networks, as modules
and combine pre-trained modules with untrained modules, to learn the shift in
distributions between data sets. The central impact of using a modular approach
comes from adding new representations to a network, as opposed to replacing
representations via fine-tuning. Using this technique, we are able surpass
results using standard fine-tuning transfer learning approaches, and we are
also able to significantly increase performance over such approaches when using
smaller amounts of data.
|
[
"['Ark Anderson' 'Kyle Shaffer' 'Artem Yankov' 'Court D. Corley'\n 'Nathan O. Hodas']",
"Ark Anderson, Kyle Shaffer, Artem Yankov, Court D. Corley, Nathan O.\n Hodas"
] |
stat.ML cs.LG
| null |
1611.01722
| null | null |
http://arxiv.org/pdf/1611.01722v2
|
2016-11-26T01:08:47Z
|
2016-11-06T02:40:41Z
|
Learning to Draw Samples: With Application to Amortized MLE for
Generative Adversarial Learning
|
We propose a simple algorithm to train stochastic neural networks to draw
samples from given target distributions for probabilistic inference. Our method
is based on iteratively adjusting the neural network parameters so that the
output changes along a Stein variational gradient that maximumly decreases the
KL divergence with the target distribution. Our method works for any target
distribution specified by their unnormalized density function, and can train
any black-box architectures that are differentiable in terms of the parameters
we want to adapt. As an application of our method, we propose an amortized MLE
algorithm for training deep energy model, where a neural sampler is adaptively
trained to approximate the likelihood function. Our method mimics an
adversarial game between the deep energy model and the neural sampler, and
obtains realistic-looking images competitive with the state-of-the-art results.
|
[
"['Dilin Wang' 'Qiang Liu']",
"Dilin Wang, Qiang Liu"
] |
cs.CL cs.LG
| null |
1611.01724
| null | null |
http://arxiv.org/pdf/1611.01724v2
|
2017-09-11T21:00:30Z
|
2016-11-06T03:17:42Z
|
Words or Characters? Fine-grained Gating for Reading Comprehension
|
Previous work combines word-level and character-level representations using
concatenation or scalar weighting, which is suboptimal for high-level tasks
like reading comprehension. We present a fine-grained gating mechanism to
dynamically combine word-level and character-level representations based on
properties of the words. We also extend the idea of fine-grained gating to
modeling the interaction between questions and paragraphs for reading
comprehension. Experiments show that our approach can improve the performance
on reading comprehension tasks, achieving new state-of-the-art results on the
Children's Book Test dataset. To demonstrate the generality of our gating
mechanism, we also show improved results on a social media tag prediction task.
|
[
"Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen,\n Ruslan Salakhutdinov",
"['Zhilin Yang' 'Bhuwan Dhingra' 'Ye Yuan' 'Junjie Hu' 'William W. Cohen'\n 'Ruslan Salakhutdinov']"
] |
cs.CR cs.LG
| null |
1611.01726
| null | null |
http://arxiv.org/pdf/1611.01726v1
|
2016-11-06T04:07:29Z
|
2016-11-06T04:07:29Z
|
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for
Designing Host-Based Intrusion Detection Systems
|
In computer security, designing a robust intrusion detection system is one of
the most fundamental and important problems. In this paper, we propose a
system-call language-modeling approach for designing anomaly-based host
intrusion detection systems. To remedy the issue of high false-alarm rates
commonly arising in conventional methods, we employ a novel ensemble method
that blends multiple thresholding classifiers into a single one, making it
possible to accumulate 'highly normal' sequences. The proposed system-call
language model has various advantages leveraged by the fact that it can learn
the semantic meaning and interactions of each system call that existing methods
cannot effectively consider. Through diverse experiments on public benchmark
datasets, we demonstrate the validity and effectiveness of the proposed method.
Moreover, we show that our model possesses high portability, which is one of
the key aspects of realizing successful intrusion detection systems.
|
[
"Gyuwan Kim, Hayoon Yi, Jangho Lee, Yunheung Paek, Sungroh Yoon",
"['Gyuwan Kim' 'Hayoon Yi' 'Jangho Lee' 'Yunheung Paek' 'Sungroh Yoon']"
] |
cs.PL cs.LG
| null |
1611.01752
| null | null |
http://arxiv.org/pdf/1611.01752v2
|
2017-06-25T16:32:21Z
|
2016-11-06T10:35:56Z
|
Learning a Static Analyzer from Data
|
To be practically useful, modern static analyzers must precisely model the
effect of both, statements in the programming language as well as frameworks
used by the program under analysis. While important, manually addressing these
challenges is difficult for at least two reasons: (i) the effects on the
overall analysis can be non-trivial, and (ii) as the size and complexity of
modern libraries increase, so is the number of cases the analysis must handle.
In this paper we present a new, automated approach for creating static
analyzers: instead of manually providing the various inference rules of the
analyzer, the key idea is to learn these rules from a dataset of programs. Our
method consists of two ingredients: (i) a synthesis algorithm capable of
learning a candidate analyzer from a given dataset, and (ii) a counter-example
guided learning procedure which generates new programs beyond those in the
initial dataset, critical for discovering corner cases and ensuring the learned
analysis generalizes to unseen programs.
We implemented and instantiated our approach to the task of learning
JavaScript static analysis rules for a subset of points-to analysis and for
allocation sites analysis. These are challenging yet important problems that
have received significant research attention. We show that our approach is
effective: our system automatically discovered practical and useful inference
rules for many cases that are tricky to manually identify and are missed by
state-of-the-art, manually tuned analyzers.
|
[
"Pavol Bielik, Veselin Raychev, Martin Vechev",
"['Pavol Bielik' 'Veselin Raychev' 'Martin Vechev']"
] |
cs.LG cs.AI cs.CV
| null |
1611.01779
| null | null |
http://arxiv.org/pdf/1611.01779v2
|
2017-02-14T19:47:46Z
|
2016-11-06T13:45:00Z
|
Learning to Act by Predicting the Future
|
We present an approach to sensorimotor control in immersive environments. Our
approach utilizes a high-dimensional sensory stream and a lower-dimensional
measurement stream. The cotemporal structure of these streams provides a rich
supervisory signal, which enables training a sensorimotor control model by
interacting with the environment. The model is trained using supervised
learning techniques, but without extraneous supervision. It learns to act based
on raw sensory input from a complex three-dimensional environment. The
presented formulation enables learning without a fixed goal at training time,
and pursuing dynamically changing goals at test time. We conduct extensive
experiments in three-dimensional simulations based on the classical
first-person game Doom. The results demonstrate that the presented approach
outperforms sophisticated prior formulations, particularly on challenging
tasks. The results also show that trained models successfully generalize across
environments and goals. A model trained using the presented approach won the
Full Deathmatch track of the Visual Doom AI Competition, which was held in
previously unseen environments.
|
[
"['Alexey Dosovitskiy' 'Vladlen Koltun']",
"Alexey Dosovitskiy and Vladlen Koltun"
] |
cs.LG
| null |
1611.01787
| null | null |
http://arxiv.org/pdf/1611.01787v3
|
2017-06-28T15:04:46Z
|
2016-11-06T14:35:38Z
|
Learning to superoptimize programs
|
Code super-optimization is the task of transforming any given program to a
more efficient version while preserving its input-output behaviour. In some
sense, it is similar to the paraphrase problem from natural language processing
where the intention is to change the syntax of an utterance without changing
its semantics. Code-optimization has been the subject of years of research that
has resulted in the development of rule-based transformation strategies that
are used by compilers. More recently, however, a class of stochastic search
based methods have been shown to outperform these strategies. This approach
involves repeated sampling of modifications to the program from a proposal
distribution, which are accepted or rejected based on whether they preserve
correctness, and the improvement they achieve. These methods, however, neither
learn from past behaviour nor do they try to leverage the semantics of the
program under consideration. Motivated by this observation, we present a novel
learning based approach for code super-optimization. Intuitively, our method
works by learning the proposal distribution using unbiased estimators of the
gradient of the expected improvement. Experiments on benchmarks comprising of
automatically generated as well as existing ("Hacker's Delight") programs show
that the proposed method is able to significantly outperform state of the art
approaches for code super-optimization.
|
[
"Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H.S. Torr and\n Pushmeet Kohli",
"['Rudy Bunel' 'Alban Desmaison' 'M. Pawan Kumar' 'Philip H. S. Torr'\n 'Pushmeet Kohli']"
] |
cs.LG cs.NE
| null |
1611.01796
| null | null |
http://arxiv.org/pdf/1611.01796v2
|
2017-06-17T01:49:12Z
|
2016-11-06T15:36:56Z
|
Modular Multitask Reinforcement Learning with Policy Sketches
|
We describe a framework for multitask deep reinforcement learning guided by
policy sketches. Sketches annotate tasks with sequences of named subtasks,
providing information about high-level structural relationships among tasks but
not how to implement them---specifically not providing the detailed guidance
used by much previous work on learning policy abstractions for RL (e.g.
intermediate rewards, subtask completion signals, or intrinsic motivations). To
learn from sketches, we present a model that associates every subtask with a
modular subpolicy, and jointly maximizes reward over full task-specific
policies by tying parameters across shared subpolicies. Optimization is
accomplished via a decoupled actor--critic training objective that facilitates
learning common behaviors from multiple dissimilar reward functions. We
evaluate the effectiveness of our approach in three environments featuring both
discrete and continuous control, and with sparse rewards that can be obtained
only after completing a number of high-level subgoals. Experiments show that
using our approach to learn policies guided by sketches gives better
performance than existing techniques for learning task-specific or shared
policies, while naturally inducing a library of interpretable primitive
behaviors that can be recombined to rapidly adapt to new tasks.
|
[
"['Jacob Andreas' 'Dan Klein' 'Sergey Levine']",
"Jacob Andreas and Dan Klein and Sergey Levine"
] |
cs.LG
| null |
1611.01799
| null | null |
http://arxiv.org/pdf/1611.01799v1
|
2016-11-06T16:04:48Z
|
2016-11-06T16:04:48Z
|
Generative Adversarial Networks as Variational Training of Energy Based
Models
|
In this paper, we study deep generative models for effective unsupervised
learning. We propose VGAN, which works by minimizing a variational lower bound
of the negative log likelihood (NLL) of an energy based model (EBM), where the
model density $p(\mathbf{x})$ is approximated by a variational distribution
$q(\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two
step procedure: given $p(\mathbf{x})$, $q(\mathbf{x})$ is updated to maximize
the lower bound; $p(\mathbf{x})$ is then updated one step with samples drawn
from $q(\mathbf{x})$ to decrease the lower bound. VGAN is inspired by the
generative adversarial networks (GANs), where $p(\mathbf{x})$ corresponds to
the discriminator and $q(\mathbf{x})$ corresponds to the generator, but with
several notable differences. We hence name our model variational GANs (VGANs).
VGAN provides a practical solution to training deep EBMs in high dimensional
space, by eliminating the need of MCMC sampling. From this view, we are also
able to identify causes to the difficulty of training GANs and propose viable
solutions. \footnote{Experimental code is available at
https://github.com/Shuangfei/vgan}
|
[
"['Shuangfei Zhai' 'Yu Cheng' 'Rogerio Feris' 'Zhongfei Zhang']",
"Shuangfei Zhai, Yu Cheng, Rogerio Feris, Zhongfei Zhang"
] |
cs.LG stat.ML
| null |
1611.01838
| null | null |
http://arxiv.org/pdf/1611.01838v5
|
2017-04-21T07:16:30Z
|
2016-11-06T20:22:49Z
|
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
|
This paper proposes a new optimization algorithm called Entropy-SGD for
training deep neural networks that is motivated by the local geometry of the
energy landscape. Local extrema with low generalization error have a large
proportion of almost-zero eigenvalues in the Hessian with very few positive or
negative eigenvalues. We leverage upon this observation to construct a
local-entropy-based objective function that favors well-generalizable solutions
lying in large flat regions of the energy landscape, while avoiding
poorly-generalizable solutions located in the sharp valleys. Conceptually, our
algorithm resembles two nested loops of SGD where we use Langevin dynamics in
the inner loop to compute the gradient of the local entropy before each update
of the weights. We show that the new objective has a smoother energy landscape
and show improved generalization over SGD using uniform stability, under
certain assumptions. Our experiments on convolutional and recurrent networks
demonstrate that Entropy-SGD compares favorably to state-of-the-art techniques
in terms of generalization error and training time.
|
[
"['Pratik Chaudhari' 'Anna Choromanska' 'Stefano Soatto' 'Yann LeCun'\n 'Carlo Baldassi' 'Christian Borgs' 'Jennifer Chayes' 'Levent Sagun'\n 'Riccardo Zecchina']",
"Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo\n Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, Riccardo Zecchina"
] |
stat.ML cs.AI cs.CV cs.LG cs.NE physics.soc-ph
| null |
1611.01843
| null | null |
http://arxiv.org/pdf/1611.01843v3
|
2017-08-17T19:51:29Z
|
2016-11-06T20:55:19Z
|
Learning to Perform Physics Experiments via Deep Reinforcement Learning
|
When encountering novel objects, humans are able to infer a wide range of
physical properties such as mass, friction and deformability by interacting
with them in a goal driven way. This process of active interaction is in the
same spirit as a scientist performing experiments to discover hidden facts.
Recent advances in artificial intelligence have yielded machines that can
achieve superhuman performance in Go, Atari, natural language processing, and
complex control problems; however, it is not clear that these systems can rival
the scientific intuition of even a young child. In this work we introduce a
basic set of tasks that require agents to estimate properties such as mass and
cohesion of objects in an interactive simulated environment where they can
manipulate the objects and observe the consequences. We found that state of art
deep reinforcement learning methods can learn to perform the experiments
necessary to discover such hidden properties. By systematically manipulating
the problem difficulty and the cost incurred by the agent for performing
experiments, we found that agents learn different strategies that balance the
cost of gathering information against the cost of making mistakes in different
situations.
|
[
"Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter\n Battaglia, Nando de Freitas",
"['Misha Denil' 'Pulkit Agrawal' 'Tejas D Kulkarni' 'Tom Erez'\n 'Peter Battaglia' 'Nando de Freitas']"
] |
cs.LG
| null |
1611.01875
| null | null |
http://arxiv.org/pdf/1611.01875v1
|
2016-11-07T02:17:43Z
|
2016-11-07T02:17:43Z
|
Challenges of Feature Selection for Big Data Analytics
|
We are surrounded by huge amounts of large-scale high dimensional data. It is
desirable to reduce the dimensionality of data for many learning tasks due to
the curse of dimensionality. Feature selection has shown its effectiveness in
many applications by building simpler and more comprehensive model, improving
learning performance, and preparing clean, understandable data. Recently, some
unique characteristics of big data such as data velocity and data variety
present challenges to the feature selection problem. In this paper, we envision
these challenges of feature selection for big data analytics. In particular, we
first give a brief introduction about feature selection and then detail the
challenges of feature selection for structured, heterogeneous and streaming
data as well as its scalability and stability issues. At last, to facilitate
and promote the feature selection research, we present an open-source feature
selection repository (scikit-feature), which consists of most of current
popular feature selection algorithms.
|
[
"Jundong Li, Huan Liu",
"['Jundong Li' 'Huan Liu']"
] |
cs.LG cs.AI cs.IT math.IT q-bio.NC stat.ML
| null |
1611.01886
| null | null |
http://arxiv.org/pdf/1611.01886v4
|
2017-03-10T16:41:16Z
|
2016-11-07T04:17:28Z
|
An Information-Theoretic Framework for Fast and Robust Unsupervised
Learning via Neural Population Infomax
|
A framework is presented for unsupervised learning of representations based
on infomax principle for large-scale neural populations. We use an asymptotic
approximation to the Shannon's mutual information for a large neural population
to demonstrate that a good initial approximation to the global
information-theoretic optimum can be obtained by a hierarchical infomax method.
Starting from the initial solution, an efficient algorithm based on gradient
descent of the final objective function is proposed to learn representations
from the input datasets, and the method works for complete, overcomplete, and
undercomplete bases. As confirmed by numerical experiments, our method is
robust and highly efficient for extracting salient features from input
datasets. Compared with the main existing methods, our algorithm has a distinct
advantage in both the training speed and the robustness of unsupervised
representation learning. Furthermore, the proposed method is easily extended to
the supervised or unsupervised model for training deep structure networks.
|
[
"Wentao Huang and Kechen Zhang",
"['Wentao Huang' 'Kechen Zhang']"
] |
stat.ML cs.LG
| null |
1611.01891
| null | null |
http://arxiv.org/pdf/1611.01891v1
|
2016-11-07T04:45:05Z
|
2016-11-07T04:45:05Z
|
Joint Multimodal Learning with Deep Generative Models
|
We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. Recently, some studies handle multiple modalities on deep generative
models, such as variational autoencoders (VAEs). However, these models
typically assume that modalities are forced to have a conditioned relation,
i.e., we can only generate modalities in one direction. To achieve our
objective, we should extract a joint representation that captures high-level
concepts among all modalities and through which we can exchange them
bi-directionally. As described herein, we propose a joint multimodal
variational autoencoder (JMVAE), in which all modalities are independently
conditioned on joint representation. In other words, it models a joint
distribution of modalities. Furthermore, to be able to generate missing
modalities from the remaining modalities properly, we develop an additional
method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's
encoder and prepared networks of respective modalities. Our experiments show
that our proposed method can obtain appropriate joint representation from
multiple modalities and that it can generate and reconstruct them more properly
than conventional VAEs. We further demonstrate that JMVAE can generate multiple
modalities bi-directionally.
|
[
"Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo",
"['Masahiro Suzuki' 'Kotaro Nakayama' 'Yutaka Matsuo']"
] |
cs.DB cs.LG
| null |
1611.01919
| null | null |
http://arxiv.org/pdf/1611.01919v2
|
2019-05-23T19:32:43Z
|
2016-11-07T07:13:27Z
|
Decision Tree Classification with Differential Privacy: A Survey
|
Data mining information about people is becoming increasingly important in
the data-driven society of the 21st century. Unfortunately, sometimes there are
real-world considerations that conflict with the goals of data mining;
sometimes the privacy of the people being data mined needs to be considered.
This necessitates that the output of data mining algorithms be modified to
preserve privacy while simultaneously not ruining the predictive power of the
outputted model. Differential privacy is a strong, enforceable definition of
privacy that can be used in data mining algorithms, guaranteeing that nothing
will be learned about the people in the data that could not already be
discovered without their participation. In this survey, we focus on one
particular data mining algorithm -- decision trees -- and how differential
privacy interacts with each of the components that constitute decision tree
algorithms. We analyze both greedy and random decision trees, and the conflicts
that arise when trying to balance privacy requirements with the accuracy of the
model.
|
[
"Sam Fletcher, Md Zahidul Islam",
"['Sam Fletcher' 'Md Zahidul Islam']"
] |
cs.AI cs.LG stat.ML
| null |
1611.01929
| null | null |
http://arxiv.org/pdf/1611.01929v4
|
2017-03-10T09:52:52Z
|
2016-11-07T08:12:53Z
|
Averaged-DQN: Variance Reduction and Stabilization for Deep
Reinforcement Learning
|
Instability and variability of Deep Reinforcement Learning (DRL) algorithms
tend to adversely affect their performance. Averaged-DQN is a simple extension
to the DQN algorithm, based on averaging previously learned Q-values estimates,
which leads to a more stable training procedure and improved performance by
reducing approximation error variance in the target values. To understand the
effect of the algorithm, we examine the source of value function estimation
errors and provide an analytical comparison within a simplified model. We
further present experiments on the Arcade Learning Environment benchmark that
demonstrate significantly improved stability and performance due to the
proposed extension.
|
[
"['Oron Anschel' 'Nir Baram' 'Nahum Shimkin']",
"Oron Anschel, Nir Baram, Nahum Shimkin"
] |
cs.LG cs.NE cs.NI
| null |
1611.01942
| null | null |
http://arxiv.org/pdf/1611.01942v2
|
2017-07-02T22:02:21Z
|
2016-11-07T09:10:06Z
|
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile
Sensing Data Processing
|
Mobile sensing applications usually require time-series inputs from sensors.
Some applications, such as tracking, can use sensed acceleration and rate of
rotation to calculate displacement based on physical system models. Other
applications, such as activity recognition, extract manually designed features
from sensor inputs for classification. Such applications face two challenges.
On one hand, on-device sensor measurements are noisy. For many mobile
applications, it is hard to find a distribution that exactly describes the
noise in practice. Unfortunately, calculating target quantities based on
physical system and noise models is only as accurate as the noise assumptions.
Similarly, in classification applications, although manually designed features
have proven to be effective, it is not always straightforward to find the most
robust features to accommodate diverse sensor noise patterns and user
behaviors. To this end, we propose DeepSense, a deep learning framework that
directly addresses the aforementioned noise and feature customization
challenges in a unified manner. DeepSense integrates convolutional and
recurrent neural networks to exploit local interactions among similar mobile
sensors, merge local interactions of different sensory modalities into global
interactions, and extract temporal relationships to model signal dynamics.
DeepSense thus provides a general signal estimation and classification
framework that accommodates a wide range of applications. We demonstrate the
effectiveness of DeepSense using three representative and challenging tasks:
car tracking with motion sensors, heterogeneous human activity recognition, and
user identification with biometric motion analysis. DeepSense significantly
outperforms the state-of-the-art methods for all three tasks. In addition,
DeepSense is feasible to implement on smartphones due to its moderate energy
consumption and low latency
|
[
"['Shuochao Yao' 'Shaohan Hu' 'Yiran Zhao' 'Aston Zhang' 'Tarek Abdelzaher']",
"Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, Tarek Abdelzaher"
] |
stat.ML cs.LG
| null |
1611.01957
| null | null |
http://arxiv.org/pdf/1611.01957v3
|
2017-07-27T06:34:45Z
|
2016-11-07T09:38:12Z
|
Linear Convergence of SVRG in Statistical Estimation
|
SVRG and its variants are among the state of art optimization algorithms for
large scale machine learning problems. It is well known that SVRG converges
linearly when the objective function is strongly convex. However this setup can
be restrictive, and does not include several important formulations such as
Lasso, group Lasso, logistic regression, and some non-convex models including
corrected Lasso and SCAD. In this paper, we prove that, for a class of
statistical M-estimators covering examples mentioned above, SVRG solves the
formulation with {\em a linear convergence rate} without strong convexity or
even convexity. Our analysis makes use of {\em restricted strong convexity},
under which we show that SVRG converges linearly to the fundamental statistical
precision of the model, i.e., the difference between true unknown parameter
$\theta^*$ and the optimal solution $\hat{\theta}$ of the model.
|
[
"['Chao Qu' 'Yan Li' 'Huan Xu']",
"Chao Qu, Yan Li, Huan Xu"
] |
cs.LG
| null |
1611.01964
| null | null |
http://arxiv.org/pdf/1611.01964v1
|
2016-11-07T10:10:43Z
|
2016-11-07T10:10:43Z
|
Log-time and Log-space Extreme Classification
|
We present LTLS, a technique for multiclass and multilabel prediction that
can perform training and inference in logarithmic time and space. LTLS embeds
large classification problems into simple structured prediction problems and
relies on efficient dynamic programming algorithms for inference. We train LTLS
with stochastic gradient descent on a number of multiclass and multilabel
datasets and show that despite its small memory footprint it is often
competitive with existing approaches.
|
[
"['Kalina Jasinska' 'Nikos Karampatziakis']",
"Kalina Jasinska, Nikos Karampatziakis"
] |
cs.LG cs.NE
| null |
1611.01967
| null | null |
http://arxiv.org/pdf/1611.01967v2
|
2017-03-15T08:18:28Z
|
2016-11-07T10:15:40Z
|
Regularizing CNNs with Locally Constrained Decorrelations
|
Regularization is key for deep learning since it allows training more complex
models while keeping lower levels of overfitting. However, the most prevalent
regularizations do not leverage all the capacity of the models since they rely
on reducing the effective number of parameters. Feature decorrelation is an
alternative for using the full capacity of the models but the overfitting
reduction margins are too narrow given the overhead it introduces. In this
paper, we show that regularizing negatively correlated features is an obstacle
for effective decorrelation and present OrthoReg, a novel regularization
technique that locally enforces feature orthogonality. As a result, imposing
locality constraints in feature decorrelation removes interferences between
negatively correlated feature weights, allowing the regularizer to reach higher
decorrelation bounds, and reducing the overfitting more effectively. In
particular, we show that the models regularized with OrthoReg have higher
accuracy bounds even when batch normalization and dropout are present.
Moreover, since our regularization is directly performed on the weights, it is
especially suitable for fully convolutional neural networks, where the weight
space is constant compared to the feature map space. As a result, we are able
to reduce the overfitting of state-of-the-art CNNs on CIFAR-10, CIFAR-100, and
SVHN.
|
[
"Pau Rodr\\'iguez, Jordi Gonz\\`alez, Guillem Cucurull, Josep M. Gonfaus,\n Xavier Roca",
"['Pau Rodríguez' 'Jordi Gonzàlez' 'Guillem Cucurull' 'Josep M. Gonfaus'\n 'Xavier Roca']"
] |
stat.ML cs.LG
| null |
1611.01971
| null | null |
http://arxiv.org/pdf/1611.01971v3
|
2016-11-21T08:54:54Z
|
2016-11-07T10:25:15Z
|
One Class Splitting Criteria for Random Forests
|
Random Forests (RFs) are strong machine learning tools for classification and
regression. However, they remain supervised algorithms, and no extension of RFs
to the one-class setting has been proposed, except for techniques based on
second-class sampling. This work fills this gap by proposing a natural
methodology to extend standard splitting criteria to the one-class setting,
structurally generalizing RFs to one-class classification. An extensive
benchmark of seven state-of-the-art anomaly detection algorithms is also
presented. This empirically demonstrates the relevance of our approach.
|
[
"Nicolas Goix (LTCI), Nicolas Drougard (ISAE), Romain Brault (LTCI),\n Ma\\\"el Chiapino (LTCI)",
"['Nicolas Goix' 'Nicolas Drougard' 'Romain Brault' 'Maël Chiapino']"
] |
cs.CV cs.LG
| null |
1611.01972
| null | null |
http://arxiv.org/pdf/1611.01972v2
|
2017-08-29T09:46:41Z
|
2016-11-07T10:26:41Z
|
Fixed-point Factorized Networks
|
In recent years, Deep Neural Networks (DNN) based methods have achieved
remarkable performance in a wide range of tasks and have been among the most
powerful and widely used techniques in computer vision. However, DNN-based
methods are both computational-intensive and resource-consuming, which hinders
the application of these methods on embedded systems like smart phones. To
alleviate this problem, we introduce a novel Fixed-point Factorized Networks
(FFN) for pretrained models to reduce the computational complexity as well as
the storage requirement of networks. The resulting networks have only weights
of -1, 0 and 1, which significantly eliminates the most resource-consuming
multiply-accumulate operations (MACs). Extensive experiments on large-scale
ImageNet classification task show the proposed FFN only requires one-thousandth
of multiply operations with comparable accuracy.
|
[
"Peisong Wang and Jian Cheng",
"['Peisong Wang' 'Jian Cheng']"
] |
cs.PL cs.LG
| null |
1611.01988
| null | null |
http://arxiv.org/pdf/1611.01988v2
|
2017-03-02T13:26:11Z
|
2016-11-07T11:09:19Z
|
Differentiable Functional Program Interpreters
|
Programming by Example (PBE) is the task of inducing computer programs from
input-output examples. It can be seen as a type of machine learning where the
hypothesis space is the set of legal programs in some programming language.
Recent work on differentiable interpreters relaxes the discrete space of
programs into a continuous space so that search over programs can be performed
using gradient-based optimization. While conceptually powerful, so far
differentiable interpreter-based program synthesis has only been capable of
solving very simple problems. In this work, we study modeling choices that
arise when constructing a differentiable programming language and their impact
on the success of synthesis. The main motivation for the modeling choices comes
from functional programming: we study the effect of memory allocation schemes,
immutable data, type systems, and built-in control-flow structures. Empirically
we show that incorporating functional programming ideas into differentiable
programming languages allows us to learn much more complex programs than is
possible with existing differentiable languages.
|
[
"['John K. Feser' 'Marc Brockschmidt' 'Alexander L. Gaunt' 'Daniel Tarlow']",
"John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow"
] |
cs.LG
| null |
1611.01989
| null | null |
http://arxiv.org/pdf/1611.01989v2
|
2017-03-08T11:50:33Z
|
2016-11-07T11:09:45Z
|
DeepCoder: Learning to Write Programs
|
We develop a first line of attack for solving programming competition-style
problems from input-output examples using deep learning. The approach is to
train a neural network to predict properties of the program that generated the
outputs from the inputs. We use the neural network's predictions to augment
search techniques from the programming languages community, including
enumerative search and an SMT-based solver. Empirically, we show that our
approach leads to an order of magnitude speedup over the strong non-augmented
baselines and a Recurrent Neural Network approach, and that we are able to
solve problems of difficulty comparable to the simplest problems on programming
competition websites.
|
[
"Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin,\n Daniel Tarlow",
"['Matej Balog' 'Alexander L. Gaunt' 'Marc Brockschmidt'\n 'Sebastian Nowozin' 'Daniel Tarlow']"
] |
cs.LG
|
10.1007/978-3-319-71246-8_11
|
1611.02019
| null | null |
http://arxiv.org/abs/1611.02019v2
|
2019-04-17T11:38:13Z
|
2016-11-07T12:29:19Z
|
Multi-view Generative Adversarial Networks
|
Learning over multi-view data is a challenging problem with strong practical
applications. Most related studies focus on the classification point of view
and assume that all the views are available at any time. We consider an
extension of this framework in two directions. First, based on the BiGAN model,
the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from
multi-view inputs. Second, it can deal with missing views and is able to update
its prediction when additional views are provided. We illustrate these
properties on a set of experiments over different datasets.
|
[
"Micka\\\"el Chen and Ludovic Denoyer",
"['Mickaël Chen' 'Ludovic Denoyer']"
] |
stat.ML cs.LG
| null |
1611.02041
| null | null |
http://arxiv.org/pdf/1611.02041v6
|
2018-07-22T07:49:28Z
|
2016-11-07T13:19:45Z
|
Does Distributionally Robust Supervised Learning Give Robust
Classifiers?
|
Distributionally Robust Supervised Learning (DRSL) is necessary for building
reliable machine learning systems. When machine learning is deployed in the
real world, its performance can be significantly degraded because test data may
follow a different distribution from training data. DRSL with f-divergences
explicitly considers the worst-case distribution shift by minimizing the
adversarially reweighted training loss. In this paper, we analyze this DRSL,
focusing on the classification scenario. Since the DRSL is explicitly
formulated for a distribution shift scenario, we naturally expect it to give a
robust classifier that can aggressively handle shifted distributions. However,
surprisingly, we prove that the DRSL just ends up giving a classifier that
exactly fits the given training distribution, which is too pessimistic. This
pessimism comes from two sources: the particular losses used in classification
and the fact that the variety of distributions to which the DRSL tries to be
robust is too wide. Motivated by our analysis, we propose simple DRSL that
overcomes this pessimism and empirically demonstrate its effectiveness.
|
[
"Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama",
"['Weihua Hu' 'Gang Niu' 'Issei Sato' 'Masashi Sugiyama']"
] |
cs.LG cs.AI stat.ML
| null |
1611.02047
| null | null |
http://arxiv.org/pdf/1611.02047v1
|
2016-11-07T13:43:38Z
|
2016-11-07T13:43:38Z
|
Reinforcement Learning Approach for Parallelization in Filters
Aggregation Based Feature Selection Algorithms
|
One of the classical problems in machine learning and data mining is feature
selection. A feature selection algorithm is expected to be quick, and at the
same time it should show high performance. MeLiF algorithm effectively solves
this problem using ensembles of ranking filters. This article describes two
different ways to improve MeLiF algorithm performance with parallelization.
Experiments show that proposed schemes significantly improves algorithm
performance and increase feature selection quality.
|
[
"Ivan Smetannikov, Ilya Isaev, Andrey Filchenkov",
"['Ivan Smetannikov' 'Ilya Isaev' 'Andrey Filchenkov']"
] |
cs.LG cs.AI stat.ML
| null |
1611.02053
| null | null |
http://arxiv.org/pdf/1611.02053v1
|
2016-11-07T13:55:00Z
|
2016-11-07T13:55:00Z
|
Reinforcement-based Simultaneous Algorithm and its Hyperparameters
Selection
|
Many algorithms for data analysis exist, especially for classification
problems. To solve a data analysis problem, a proper algorithm should be
chosen, and also its hyperparameters should be selected. In this paper, we
present a new method for the simultaneous selection of an algorithm and its
hyperparameters. In order to do so, we reduced this problem to the multi-armed
bandit problem. We consider an algorithm as an arm and algorithm
hyperparameters search during a fixed time as the corresponding arm play. We
also suggest a problem-specific reward function. We performed the experiments
on 10 real datasets and compare the suggested method with the existing one
implemented in Auto-WEKA. The results show that our method is significantly
better in most of the cases and never worse than the Auto-WEKA.
|
[
"Valeria Efimova, Andrey Filchenkov, Anatoly Shalyto",
"['Valeria Efimova' 'Andrey Filchenkov' 'Anatoly Shalyto']"
] |
stat.ML cs.DC cs.LG
| null |
1611.02101
| null | null |
http://arxiv.org/pdf/1611.02101v2
|
2017-06-26T13:35:23Z
|
2016-11-07T15:19:54Z
|
Distributed Coordinate Descent for Generalized Linear Models with
Regularization
|
Generalized linear model with $L_1$ and $L_2$ regularization is a widely used
technique for solving classification, class probability estimation and
regression problems. With the numbers of both features and examples growing
rapidly in the fields like text mining and clickstream data analysis
parallelization and the use of cluster architectures becomes important. We
present a novel algorithm for fitting regularized generalized linear models in
the distributed environment. The algorithm splits data between nodes by
features, uses coordinate descent on each node and line search to merge results
globally. Convergence proof is provided. A modifications of the algorithm
addresses slow node problem. For an important particular case of logistic
regression we empirically compare our program with several state-of-the art
approaches that rely on different algorithmic and data spitting methods.
Experiments demonstrate that our approach is scalable and superior when
training on large and sparse datasets.
|
[
"['Ilya Trofimov' 'Alexander Genkin']",
"Ilya Trofimov, Alexander Genkin"
] |
cs.LG
| null |
1611.02109
| null | null |
http://arxiv.org/pdf/1611.02109v2
|
2017-03-02T13:34:48Z
|
2016-11-07T15:25:53Z
|
Differentiable Programs with Neural Libraries
|
We develop a framework for combining differentiable programming languages
with neural networks. Using this framework we create end-to-end trainable
systems that learn to write interpretable algorithms with perceptual
components. We explore the benefits of inductive biases for strong
generalization and modularity that come from the program-like structure of our
models. In particular, modularity allows us to learn a library of (neural)
functions which grows and improves as more tasks are solved. Empirically, we
show that this leads to lifelong learning systems that transfer knowledge to
new tasks more effectively than baselines.
|
[
"Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow",
"['Alexander L. Gaunt' 'Marc Brockschmidt' 'Nate Kushman' 'Daniel Tarlow']"
] |
cs.NE cs.LG
| null |
1611.0212
| null | null | null | null | null |
Neural Networks Designing Neural Networks: Multi-Objective
Hyper-Parameter Optimization
|
Artificial neural networks have gone through a recent rise in popularity,
achieving state-of-the-art results in various fields, including image
classification, speech recognition, and automated control. Both the performance
and computational complexity of such models are heavily dependant on the design
of characteristic hyper-parameters (e.g., number of hidden layers, nodes per
layer, or choice of activation functions), which have traditionally been
optimized manually. With machine learning penetrating low-power mobile and
embedded areas, the need to optimize not only for performance (accuracy), but
also for implementation complexity, becomes paramount. In this work, we present
a multi-objective design space exploration method that reduces the number of
solution networks trained and evaluated through response surface modelling.
Given spaces which can easily exceed 1020 solutions, manually designing a
near-optimal architecture is unlikely as opportunities to reduce network
complexity, while maintaining performance, may be overlooked. This problem is
exacerbated by the fact that hyper-parameters which perform well on specific
datasets may yield sub-par results on others, and must therefore be designed on
a per-application basis. In our work, machine learning is leveraged by training
an artificial neural network to predict the performance of future candidate
networks. The method is evaluated on the MNIST and CIFAR-10 image datasets,
optimizing for both recognition accuracy and computational complexity.
Experimental results demonstrate that the proposed method can closely
approximate the Pareto-optimal front, while only exploring a small fraction of
the design space.
|
[
"Sean C. Smithson and Guang Yang and Warren J. Gross and Brett H. Meyer"
] |
null | null |
1611.02120
| null | null |
http://arxiv.org/pdf/1611.02120v1
|
2016-11-07T15:38:39Z
|
2016-11-07T15:38:39Z
|
Neural Networks Designing Neural Networks: Multi-Objective
Hyper-Parameter Optimization
|
Artificial neural networks have gone through a recent rise in popularity, achieving state-of-the-art results in various fields, including image classification, speech recognition, and automated control. Both the performance and computational complexity of such models are heavily dependant on the design of characteristic hyper-parameters (e.g., number of hidden layers, nodes per layer, or choice of activation functions), which have traditionally been optimized manually. With machine learning penetrating low-power mobile and embedded areas, the need to optimize not only for performance (accuracy), but also for implementation complexity, becomes paramount. In this work, we present a multi-objective design space exploration method that reduces the number of solution networks trained and evaluated through response surface modelling. Given spaces which can easily exceed 1020 solutions, manually designing a near-optimal architecture is unlikely as opportunities to reduce network complexity, while maintaining performance, may be overlooked. This problem is exacerbated by the fact that hyper-parameters which perform well on specific datasets may yield sub-par results on others, and must therefore be designed on a per-application basis. In our work, machine learning is leveraged by training an artificial neural network to predict the performance of future candidate networks. The method is evaluated on the MNIST and CIFAR-10 image datasets, optimizing for both recognition accuracy and computational complexity. Experimental results demonstrate that the proposed method can closely approximate the Pareto-optimal front, while only exploring a small fraction of the design space.
|
[
"['Sean C. Smithson' 'Guang Yang' 'Warren J. Gross' 'Brett H. Meyer']"
] |
cs.LG stat.ML
| null |
1611.02163
| null | null |
http://arxiv.org/pdf/1611.02163v4
|
2017-05-12T23:52:12Z
|
2016-11-07T16:42:09Z
|
Unrolled Generative Adversarial Networks
|
We introduce a method to stabilize Generative Adversarial Networks (GANs) by
defining the generator objective with respect to an unrolled optimization of
the discriminator. This allows training to be adjusted between using the
optimal discriminator in the generator's objective, which is ideal but
infeasible in practice, and using the current value of the discriminator, which
is often unstable and leads to poor solutions. We show how this technique
solves the common problem of mode collapse, stabilizes training of GANs with
complex recurrent generators, and increases diversity and coverage of the data
distribution by the generator.
|
[
"['Luke Metz' 'Ben Poole' 'David Pfau' 'Jascha Sohl-Dickstein']",
"Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein"
] |
cs.LG
| null |
1611.02167
| null | null |
http://arxiv.org/pdf/1611.02167v3
|
2017-03-22T20:08:30Z
|
2016-11-07T16:49:43Z
|
Designing Neural Network Architectures using Reinforcement Learning
|
At present, designing convolutional neural network (CNN) architectures
requires both human expertise and labor. New architectures are handcrafted by
careful experimentation or modified from a handful of existing networks. We
introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to
automatically generate high-performing CNN architectures for a given learning
task. The learning agent is trained to sequentially choose CNN layers using
$Q$-learning with an $\epsilon$-greedy exploration strategy and experience
replay. The agent explores a large but finite space of possible architectures
and iteratively discovers designs with improved performance on the learning
task. On image classification benchmarks, the agent-designed networks
(consisting of only standard convolution, pooling, and fully-connected layers)
beat existing networks designed with the same layer types and are competitive
against the state-of-the-art methods that use more complex layer types. We also
outperform existing meta-modeling approaches for network design on image
classification tasks.
|
[
"['Bowen Baker' 'Otkrist Gupta' 'Nikhil Naik' 'Ramesh Raskar']",
"Bowen Baker, Otkrist Gupta, Nikhil Naik and Ramesh Raskar"
] |
stat.ML cs.LG
| null |
1611.02181
| null | null |
http://arxiv.org/pdf/1611.02181v1
|
2016-11-07T17:29:51Z
|
2016-11-07T17:29:51Z
|
Using Social Dynamics to Make Individual Predictions: Variational
Inference with a Stochastic Kinetic Model
|
Social dynamics is concerned primarily with interactions among individuals
and the resulting group behaviors, modeling the temporal evolution of social
systems via the interactions of individuals within these systems. In
particular, the availability of large-scale data from social networks and
sensor networks offers an unprecedented opportunity to predict state-changing
events at the individual level. Examples of such events include disease
transmission, opinion transition in elections, and rumor propagation. Unlike
previous research focusing on the collective effects of social systems, this
study makes efficient inferences at the individual level. In order to cope with
dynamic interactions among a large number of individuals, we introduce the
stochastic kinetic model to capture adaptive transition probabilities and
propose an efficient variational inference algorithm the complexity of which
grows linearly --- rather than exponentially --- with the number of
individuals. To validate this method, we have performed epidemic-dynamics
experiments on wireless sensor network data collected from more than ten
thousand people over three years. The proposed algorithm was used to track
disease transmission and predict the probability of infection for each
individual. Our results demonstrate that this method is more efficient than
sampling while nonetheless achieving high accuracy.
|
[
"['Zhen Xu' 'Wen Dong' 'Sargur Srihari']",
"Zhen Xu, Wen Dong and Sargur Srihari"
] |
cs.LG
| null |
1611.02185
| null | null |
http://arxiv.org/pdf/1611.02185v5
|
2017-03-06T16:21:35Z
|
2016-11-07T17:41:20Z
|
Trusting SVM for Piecewise Linear CNNs
|
We present a novel layerwise optimization algorithm for the learning
objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large
class of convolutional neural networks. Specifically, PL-CNNs employ piecewise
linear non-linearities such as the commonly used ReLU and max-pool, and an SVM
classifier as the final layer. The key observation of our approach is that the
problem corresponding to the parameter estimation of a layer can be formulated
as a difference-of-convex (DC) program, which happens to be a latent structured
SVM. We optimize the DC program using the concave-convex procedure, which
requires us to iteratively solve a structured SVM problem. This allows to
design an optimization algorithm with an optimal learning rate that does not
require any tuning. Using the MNIST, CIFAR and ImageNet data sets, we show that
our approach always improves over the state of the art variants of
backpropagation and scales to large data and large network settings.
|
[
"Leonard Berrada, Andrew Zisserman, M. Pawan Kumar",
"['Leonard Berrada' 'Andrew Zisserman' 'M. Pawan Kumar']"
] |
cs.LG
| null |
1611.02189
| null | null |
http://arxiv.org/pdf/1611.02189v2
|
2018-10-10T00:23:51Z
|
2016-11-07T17:49:49Z
|
CoCoA: A General Framework for Communication-Efficient Distributed
Optimization
|
The scale of modern datasets necessitates the development of efficient
distributed optimization methods for machine learning. We present a
general-purpose framework for distributed computing environments, CoCoA, that
has an efficient communication scheme and is applicable to a wide variety of
problems in machine learning and signal processing. We extend the framework to
cover general non-strongly-convex regularizers, including L1-regularized
problems like lasso, sparse logistic regression, and elastic net
regularization, and show how earlier work can be derived as a special case. We
provide convergence guarantees for the class of convex regularized loss
minimization objectives, leveraging a novel approach in handling
non-strongly-convex regularizers and non-smooth loss functions. The resulting
framework has markedly improved performance over state-of-the-art methods, as
we illustrate with an extensive set of experiments on real distributed
datasets.
|
[
"['Virginia Smith' 'Simone Forte' 'Chenxin Ma' 'Martin Takac'\n 'Michael I. Jordan' 'Martin Jaggi']",
"Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I.\n Jordan, Martin Jaggi"
] |
cs.LG cs.AI
| null |
1611.02205
| null | null |
http://arxiv.org/pdf/1611.02205v2
|
2017-02-07T18:50:50Z
|
2016-11-07T18:33:38Z
|
Playing SNES in the Retro Learning Environment
|
Mastering a video game requires skill, tactics and strategy. While these
attributes may be acquired naturally by human players, teaching them to a
computer program is a far more challenging task. In recent years, extensive
research was carried out in the field of reinforcement learning and numerous
algorithms were introduced, aiming to learn how to perform human tasks such as
playing video games. As a result, the Arcade Learning Environment (ALE)
(Bellemare et al., 2013) has become a commonly used benchmark environment
allowing algorithms to train on various Atari 2600 games. In many games the
state-of-the-art algorithms outperform humans. In this paper we introduce a new
learning environment, the Retro Learning Environment --- RLE, that can run
games on the Super Nintendo Entertainment System (SNES), Sega Genesis and
several other gaming consoles. The environment is expandable, allowing for more
video games and consoles to be easily added to the environment, while
maintaining the same interface as ALE. Moreover, RLE is compatible with Python
and Torch. SNES games pose a significant challenge to current algorithms due to
their higher level of complexity and versatility.
|
[
"Nadav Bhonker, Shai Rozenberg and Itay Hubara",
"['Nadav Bhonker' 'Shai Rozenberg' 'Itay Hubara']"
] |
stat.ML cs.LG
| null |
1611.02221
| null | null |
http://arxiv.org/pdf/1611.02221v2
|
2017-03-06T19:13:07Z
|
2016-11-07T19:26:15Z
|
Minimax-optimal semi-supervised regression on unknown manifolds
|
We consider semi-supervised regression when the predictor variables are drawn
from an unknown manifold. A simple two step approach to this problem is to: (i)
estimate the manifold geodesic distance between any pair of points using both
the labeled and unlabeled instances; and (ii) apply a k nearest neighbor
regressor based on these distance estimates. We prove that given sufficiently
many unlabeled points, this simple method of geodesic kNN regression achieves
the optimal finite-sample minimax bound on the mean squared error, as if the
manifold were known. Furthermore, we show how this approach can be efficiently
implemented, requiring only O(k N log N) operations to estimate the regression
function at all N labeled and unlabeled points. We illustrate this approach on
two datasets with a manifold structure: indoor localization using WiFi
fingerprints and facial pose estimation. In both cases, geodesic kNN is more
accurate and much faster than the popular Laplacian eigenvector regressor.
|
[
"Amit Moscovich, Ariel Jaffe, Boaz Nadler",
"['Amit Moscovich' 'Ariel Jaffe' 'Boaz Nadler']"
] |
cs.LG
| null |
1611.02247
| null | null |
http://arxiv.org/pdf/1611.02247v3
|
2017-02-27T21:48:25Z
|
2016-11-07T20:09:16Z
|
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
|
Model-free deep reinforcement learning (RL) methods have been successful in a
wide variety of simulated domains. However, a major obstacle facing deep RL in
the real world is their high sample complexity. Batch policy gradient methods
offer stable learning, but at the cost of high variance, which often requires
large batches. TD-style methods, such as off-policy actor-critic and
Q-learning, are more sample-efficient but biased, and often require costly
hyperparameter sweeps to stabilize. In this work, we aim to develop methods
that combine the stability of policy gradients with the efficiency of
off-policy RL. We present Q-Prop, a policy gradient method that uses a Taylor
expansion of the off-policy critic as a control variate. Q-Prop is both sample
efficient and stable, and effectively combines the benefits of on-policy and
off-policy methods. We analyze the connection between Q-Prop and existing
model-free algorithms, and use control variate theory to derive two variants of
Q-Prop with conservative and aggressive adaptation. We show that conservative
Q-Prop provides substantial gains in sample efficiency over trust region policy
optimization (TRPO) with generalized advantage estimation (GAE), and improves
stability over deep deterministic policy gradient (DDPG), the state-of-the-art
on-policy and off-policy methods, on OpenAI Gym's MuJoCo continuous control
environments.
|
[
"['Shixiang Gu' 'Timothy Lillicrap' 'Zoubin Ghahramani' 'Richard E. Turner'\n 'Sergey Levine']",
"Shixiang Gu and Timothy Lillicrap and Zoubin Ghahramani and Richard E.\n Turner and Sergey Levine"
] |
cs.LG cs.AI stat.ML
| null |
1611.02252
| null | null |
http://arxiv.org/pdf/1611.02252v2
|
2017-10-26T01:23:40Z
|
2016-11-07T20:25:08Z
|
Hierarchical compositional feature learning
|
We introduce the hierarchical compositional network (HCN), a directed
generative model able to discover and disentangle, without supervision, the
building blocks of a set of binary images. The building blocks are binary
features defined hierarchically as a composition of some of the features in the
layer immediately below, arranged in a particular manner. At a high level, HCN
is similar to a sigmoid belief network with pooling. Inference and learning in
HCN are very challenging and existing variational approximations do not work
satisfactorily. A main contribution of this work is to show that both can be
addressed using max-product message passing (MPMP) with a particular schedule
(no EM required). Also, using MPMP as an inference engine for HCN makes new
tasks simple: adding supervision information, classifying images, or performing
inpainting all correspond to clamping some variables of the model to their
known values and running MPMP on the rest. When used for classification, fast
inference with HCN has exactly the same functional form as a convolutional
neural network (CNN) with linear activations and binary weights. However, HCN's
features are qualitatively very different.
|
[
"Miguel L\\'azaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George",
"['Miguel Lázaro-Gredilla' 'Yi Liu' 'D. Scott Phoenix' 'Dileep George']"
] |
stat.ML cs.LG
| null |
1611.02258
| null | null |
http://arxiv.org/pdf/1611.02258v2
|
2017-04-13T17:36:23Z
|
2016-11-07T20:36:56Z
|
Learning Time Series Detection Models from Temporally Imprecise Labels
|
In this paper, we consider a new low-quality label learning problem: learning
time series detection models from temporally imprecise labels. In this problem,
the data consist of a set of input time series, and supervision is provided by
a sequence of noisy time stamps corresponding to the occurrence of positive
class events. Such temporally imprecise labels commonly occur in areas like
mobile health research where human annotators are tasked with labeling the
occurrence of very short duration events. We propose a general learning
framework for this problem that can accommodate different base classifiers and
noise models. We present results on real mobile health data showing that the
proposed framework significantly outperforms a number of alternatives including
assuming that the label time stamps are noise-free, transforming the problem
into the multiple instance learning framework, and learning on labels that were
manually re-aligned.
|
[
"['Roy J. Adams' 'Benjamin M. Marlin']",
"Roy J. Adams, Benjamin M. Marlin"
] |
cs.CV cs.LG cs.NE
| null |
1611.02261
| null | null |
http://arxiv.org/pdf/1611.02261v4
|
2017-04-24T07:26:01Z
|
2016-11-07T20:50:08Z
|
Memory-augmented Attention Modelling for Videos
|
We present a method to improve video description generation by modeling
higher-order interactions between video frames and described concepts. By
storing past visual attention in the video associated to previously generated
words, the system is able to decide what to look at and describe in light of
what it has already looked at and described. This enables not only more
effective local attention, but tractable consideration of the video sequence
while generating each word. Evaluation on the challenging and popular MSVD and
Charades datasets demonstrates that the proposed architecture outperforms
previous video description approaches without requiring external temporal video
features.
|
[
"['Rasool Fakoor' 'Abdel-rahman Mohamed' 'Margaret Mitchell'\n 'Sing Bing Kang' 'Pushmeet Kohli']",
"Rasool Fakoor, Abdel-rahman Mohamed, Margaret Mitchell, Sing Bing\n Kang, Pushmeet Kohli"
] |
stat.ML cs.AI cs.CL cs.LG
| null |
1611.02266
| null | null |
http://arxiv.org/pdf/1611.02266v2
|
2016-11-30T16:44:17Z
|
2016-11-07T20:57:24Z
|
Gaussian Attention Model and Its Application to Knowledge Base Embedding
and Question Answering
|
We propose the Gaussian attention model for content-based neural memory
access. With the proposed attention model, a neural network has the additional
degree of freedom to control the focus of its attention from a laser sharp
attention to a broad attention. It is applicable whenever we can assume that
the distance in the latent space reflects some notion of semantics. We use the
proposed attention model as a scoring function for the embedding of a knowledge
base into a continuous vector space and then train a model that performs
question answering about the entities in the knowledge base. The proposed
attention model can handle both the propagation of uncertainty when following a
series of relations and also the conjunction of conditions in a natural way. On
a dataset of soccer players who participated in the FIFA World Cup 2014, we
demonstrate that our model can handle both path queries and conjunctive queries
well.
|
[
"['Liwen Zhang' 'John Winn' 'Ryota Tomioka']",
"Liwen Zhang and John Winn and Ryota Tomioka"
] |
cs.LG cs.AI stat.ML
| null |
1611.02268
| null | null |
http://arxiv.org/pdf/1611.02268v1
|
2016-11-07T20:58:58Z
|
2016-11-07T20:58:58Z
|
Optimal Binary Autoencoding with Pairwise Correlations
|
We formulate learning of a binary autoencoder as a biconvex optimization
problem which learns from the pairwise correlations between encoded and decoded
bits. Among all possible algorithms that use this information, ours finds the
autoencoder that reconstructs its inputs with worst-case optimal loss. The
optimal decoder is a single layer of artificial neurons, emerging entirely from
the minimax loss minimization, and with weights learned by convex optimization.
All this is reflected in competitive experimental results, demonstrating that
binary autoencoding can be done efficiently by conveying information in
pairwise correlations in an optimal fashion.
|
[
"Akshay Balsubramani",
"['Akshay Balsubramani']"
] |
cs.SI cs.LG stat.ML
| null |
1611.02305
| null | null |
http://arxiv.org/pdf/1611.02305v1
|
2016-11-07T21:28:40Z
|
2016-11-07T21:28:40Z
|
Learning Influence Functions from Incomplete Observations
|
We study the problem of learning influence functions under incomplete
observations of node activations. Incomplete observations are a major concern
as most (online and real-world) social networks are not fully observable. We
establish both proper and improper PAC learnability of influence functions
under randomly missing observations. Proper PAC learnability under the
Discrete-Time Linear Threshold (DLT) and Discrete-Time Independent Cascade
(DIC) models is established by reducing incomplete observations to complete
observations in a modified graph. Our improper PAC learnability result applies
for the DLT and DIC models as well as the Continuous-Time Independent Cascade
(CIC) model. It is based on a parametrization in terms of reachability
features, and also gives rise to an efficient and practical heuristic.
Experiments on synthetic and real-world datasets demonstrate the ability of our
method to compensate even for a fairly large fraction of missing observations.
|
[
"['Xinran He' 'Ke Xu' 'David Kempe' 'Yan Liu']",
"Xinran He, Ke Xu, David Kempe and Yan Liu"
] |
cs.LG cs.AI cs.CC cs.CR math.ST stat.TH
| null |
1611.02315
| null | null |
http://arxiv.org/pdf/1611.02315v2
|
2017-06-11T17:48:31Z
|
2016-11-07T21:43:39Z
|
Learning from Untrusted Data
|
The vast majority of theoretical results in machine learning and statistics
assume that the available training data is a reasonably reliable reflection of
the phenomena to be learned or estimated. Similarly, the majority of machine
learning and statistical techniques used in practice are brittle to the
presence of large amounts of biased or malicious data. In this work we consider
two frameworks in which to study estimation, learning, and optimization in the
presence of significant fractions of arbitrary data.
The first framework, list-decodable learning, asks whether it is possible to
return a list of answers, with the guarantee that at least one of them is
accurate. For example, given a dataset of $n$ points for which an unknown
subset of $\alpha n$ points are drawn from a distribution of interest, and no
assumptions are made about the remaining $(1-\alpha)n$ points, is it possible
to return a list of $\operatorname{poly}(1/\alpha)$ answers, one of which is
correct? The second framework, which we term the semi-verified learning model,
considers the extent to which a small dataset of trusted data (drawn from the
distribution in question) can be leveraged to enable the accurate extraction of
information from a much larger but untrusted dataset (of which only an
$\alpha$-fraction is drawn from the distribution).
We show strong positive results in both settings, and provide an algorithm
for robust learning in a very general stochastic optimization setting. This
general result has immediate implications for robust estimation in a number of
settings, including for robustly estimating the mean of distributions with
bounded second moments, robustly learning mixtures of such distributions, and
robustly finding planted partitions in random graphs in which significant
portions of the graph have been perturbed by an adversary.
|
[
"['Moses Charikar' 'Jacob Steinhardt' 'Gregory Valiant']",
"Moses Charikar and Jacob Steinhardt and Gregory Valiant"
] |
cs.NE cs.LG stat.ML
| null |
1611.0232
| null | null | null | null | null |
Adversarial Ladder Networks
|
The use of unsupervised data in addition to supervised data in training
discriminative neural networks has improved the performance of this clas-
sification scheme. However, the best results were achieved with a training
process that is divided in two parts: first an unsupervised pre-training step
is done for initializing the weights of the network and after these weights are
refined with the use of supervised data. On the other hand adversarial noise
has improved the results of clas- sical supervised learning. Recently, a new
neural network topology called Ladder Network, where the key idea is based in
some properties of hierar- chichal latent variable models, has been proposed as
a technique to train a neural network using supervised and unsupervised data at
the same time with what is called semi-supervised learning. This technique has
reached state of the art classification. In this work we add adversarial noise
to the ladder network and get state of the art classification, with several
important conclusions on how adversarial noise can help in addition with new
possible lines of investi- gation. We also propose an alternative to add
adversarial noise to unsu- pervised data.
|
[
"Juan Maro\\~nas Molano, Alberto Albiol Colomer, Roberto Paredes\n Palacios"
] |
null | null |
1611.02320
| null | null |
http://arxiv.org/pdf/1611.02320v3
|
2018-04-27T08:16:36Z
|
2016-11-07T22:03:43Z
|
Adversarial Ladder Networks
|
The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializing the weights of the network and after these weights are refined with the use of supervised data. On the other hand adversarial noise has improved the results of clas- sical supervised learning. Recently, a new neural network topology called Ladder Network, where the key idea is based in some properties of hierar- chichal latent variable models, has been proposed as a technique to train a neural network using supervised and unsupervised data at the same time with what is called semi-supervised learning. This technique has reached state of the art classification. In this work we add adversarial noise to the ladder network and get state of the art classification, with several important conclusions on how adversarial noise can help in addition with new possible lines of investi- gation. We also propose an alternative to add adversarial noise to unsu- pervised data.
|
[
"['Juan Maroñas Molano' 'Alberto Albiol Colomer' 'Roberto Paredes Palacios']"
] |
cs.LG cs.NE stat.ML
| null |
1611.02345
| null | null |
http://arxiv.org/pdf/1611.02345v3
|
2018-06-06T12:41:26Z
|
2016-11-07T23:47:05Z
|
Neural Taylor Approximations: Convergence and Exploration in Rectifier
Networks
|
Modern convolutional networks, incorporating rectifiers and max-pooling, are
neither smooth nor convex; standard guarantees therefore do not apply.
Nevertheless, methods from convex optimization such as gradient descent and
Adam are widely used as building blocks for deep learning algorithms. This
paper provides the first convergence guarantee applicable to modern convnets,
which furthermore matches a lower bound for convex nonsmooth functions. The key
technical tool is the neural Taylor approximation -- a straightforward
application of Taylor expansions to neural networks -- and the associated
Taylor loss. Experiments on a range of optimizers, layers, and tasks provide
evidence that the analysis accurately captures the dynamics of neural
optimization. The second half of the paper applies the Taylor approximation to
isolate the main difficulty in training rectifier nets -- that gradients are
shattered -- and investigates the hypothesis that, by exploring the space of
activation configurations more thoroughly, adaptive optimizers such as RMSProp
and Adam are able to converge to better solutions.
|
[
"David Balduzzi, Brian McWilliams, Tony Butler-Yeoman",
"['David Balduzzi' 'Brian McWilliams' 'Tony Butler-Yeoman']"
] |
stat.ML cs.LG
| null |
1611.02365
| null | null |
http://arxiv.org/pdf/1611.02365v4
|
2018-08-26T19:23:06Z
|
2016-11-08T02:20:46Z
|
NonSTOP: A NonSTationary Online Prediction Method for Time Series
|
We present online prediction methods for time series that let us explicitly
handle nonstationary artifacts (e.g. trend and seasonality) present in most
real time series. Specifically, we show that applying appropriate
transformations to such time series before prediction can lead to improved
theoretical and empirical prediction performance. Moreover, since these
transformations are usually unknown, we employ the learning with experts
setting to develop a fully online method (NonSTOP-NonSTationary Online
Prediction) for predicting nonstationary time series. This framework allows for
seasonality and/or other trends in univariate time series and cointegration in
multivariate time series. Our algorithms and regret analysis subsume recent
related work while significantly expanding the applicability of such methods.
For all the methods, we provide sub-linear regret bounds using relaxed
assumptions. The theoretical guarantees do not fully capture the benefits of
the transformations, thus we provide a data-dependent analysis of the
follow-the-leader algorithm that provides insight into the success of using
such transformations. We support all of our results with experiments on
simulated and real data.
|
[
"['Christopher Xie' 'Avleen Bijral' 'Juan Lavista Ferres']",
"Christopher Xie, Avleen Bijral, Juan Lavista Ferres"
] |
cs.LG stat.ML
| null |
1611.02401
| null | null |
http://arxiv.org/pdf/1611.02401v7
|
2018-10-14T18:11:39Z
|
2016-11-08T06:07:25Z
|
Divide and Conquer Networks
|
We consider the learning of algorithmic tasks by mere observation of
input-output pairs. Rather than studying this as a black-box discrete
regression problem with no assumption whatsoever on the input-output mapping,
we concentrate on tasks that are amenable to the principle of divide and
conquer, and study what are its implications in terms of learning. This
principle creates a powerful inductive bias that we leverage with neural
architectures that are defined recursively and dynamically, by learning two
scale-invariant atomic operations: how to split a given input into smaller
sets, and how to merge two partially solved tasks into a larger partial
solution. Our model can be trained in weakly supervised environments, namely by
just observing input-output pairs, and in even weaker environments, using a
non-differentiable reward signal. Moreover, thanks to the dynamic aspect of our
architecture, we can incorporate the computational complexity as a
regularization term that can be optimized by backpropagation. We demonstrate
the flexibility and efficiency of the Divide-and-Conquer Network on several
combinatorial and geometric tasks: convex hull, clustering, knapsack and
euclidean TSP. Thanks to the dynamic programming nature of our model, we show
significant improvements in terms of generalization error and computational
complexity.
|
[
"['Alex Nowak-Vila' 'David Folqué' 'Joan Bruna']",
"Alex Nowak-Vila, David Folqu\\'e and Joan Bruna"
] |
cs.LG cs.NE
| null |
1611.02416
| null | null |
http://arxiv.org/pdf/1611.02416v2
|
2019-02-27T09:24:09Z
|
2016-11-08T07:41:54Z
|
An Efficient Approach to Boosting Performance of Deep Spiking Network
Training
|
Nowadays deep learning is dominating the field of machine learning with
state-of-the-art performance in various application areas. Recently, spiking
neural networks (SNNs) have been attracting a great deal of attention, notably
owning to their power efficiency, which can potentially allow us to implement a
low-power deep learning engine suitable for real-time/mobile applications.
However, implementing SNN-based deep learning remains challenging, especially
gradient-based training of SNNs by error backpropagation. We cannot simply
propagate errors through SNNs in conventional way because of the property of
SNNs that process discrete data in the form of a series. Consequently, most of
the previous studies employ a workaround technique, which first trains a
conventional weighted-sum deep neural network and then maps the learning
weights to the SNN under training, instead of training SNN parameters directly.
In order to eliminate this workaround, recently proposed is a new class of SNN
named deep spiking networks (DSNs), which can be trained directly (without a
mapping from conventional deep networks) by error backpropagation with
stochastic gradient descent. In this paper, we show that the initialization of
the membrane potential on the backward path is an important step in DSN
training, through diverse experiments performed under various conditions.
Furthermore, we propose a simple and efficient method that can improve DSN
training by controlling the initial membrane potential on the backward path. In
our experiments, adopting the proposed approach allowed us to boost the
performance of DSN training in terms of converging time and accuracy.
|
[
"Seongsik Park, Sang-gil Lee, Hyunha Nam, Sungroh Yoon",
"['Seongsik Park' 'Sang-gil Lee' 'Hyunha Nam' 'Sungroh Yoon']"
] |
cs.CV cs.LG
| null |
1611.02443
| null | null |
http://arxiv.org/pdf/1611.02443v2
|
2018-02-02T09:01:20Z
|
2016-11-08T09:29:17Z
|
Domain Adaptation with L2 constraints for classifying images from
different endoscope systems
|
This paper proposes a method for domain adaptation that extends the maximum
margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2
distance constraints between samples of different domains; thus, our method is
denoted as MMDTL2. Motivated by the differences between the images taken by
narrow band imaging (NBI) endoscopic devices, we utilize different NBI devices
as different domains and estimate the transformations between samples of
different domains, i.e., image samples taken by different NBI endoscope
systems. We first formulate the problem in the primal form, and then derive the
dual form with much lesser computational costs as compared to the naive
approach. From our experimental results using NBI image datasets from two
different NBI endoscopic devices, we find that MMDTL2 is better than MMDT and
also support vector machines without adaptation, especially when NBI image
features are high-dimensional and the per-class training samples are greater
than 20.
|
[
"['Toru Tamaki' 'Shoji Sonoyama' 'Takio Kurita' 'Tsubasa Hirakawa'\n 'Bisser Raytchev' 'Kazufumi Kaneda' 'Tetsushi Koide' 'Shigeto Yoshida'\n 'Hiroshi Mieno' 'Shinji Tanaka' 'Kazuaki Chayama']",
"Toru Tamaki, Shoji Sonoyama, Takio Kurita, Tsubasa Hirakawa, Bisser\n Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno,\n Shinji Tanaka, Kazuaki Chayama"
] |
cs.AI cs.LG cs.NE
| null |
1611.02512
| null | null |
http://arxiv.org/pdf/1611.02512v1
|
2016-11-08T13:26:32Z
|
2016-11-08T13:26:32Z
|
Cognitive Discriminative Mappings for Rapid Learning
|
Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input from sensory memory by leveraging the information retrieved from
long-term memory. We present a simple and intuitive technique called cognitive
discriminative mappings (CDM) to explore the cognitive problem. First, CDM
separates and clusters the data instances retrieved from long-term memory into
distinct classes with a discrimination method in working memory when a sensory
input triggers the algorithm. CDM then maps each sensory data instance to be as
close as possible to the median point of the data group with the same class.
The experimental results demonstrate that the CDM approach is effective for
learning the discriminative features of supervised classifications with few
training sensory input instances.
|
[
"['Wen-Chieh Fang' 'Yi-ting Chiang']",
"Wen-Chieh Fang and Yi-ting Chiang"
] |
cs.LG
| null |
1611.02568
| null | null |
http://arxiv.org/pdf/1611.02568v3
|
2017-03-03T06:28:34Z
|
2016-11-08T15:50:27Z
|
PixelSNE: Visualizing Fast with Just Enough Precision via Pixel-Aligned
Stochastic Neighbor Embedding
|
Embedding and visualizing large-scale high-dimensional data in a
two-dimensional space is an important problem since such visualization can
reveal deep insights out of complex data. Most of the existing embedding
approaches, however, run on an excessively high precision, ignoring the fact
that at the end, embedding outputs are converted into coarse-grained discrete
pixel coordinates in a screen space. Motivated by such an observation and
directly considering pixel coordinates in an embedding optimization process, we
accelerate Barnes-Hut tree-based t-distributed stochastic neighbor embedding
(BH-SNE), known as a state-of-the-art 2D embedding method, and propose a novel
method called PixelSNE, a highly-efficient, screen resolution-driven 2D
embedding method with a linear computational complexity in terms of the number
of data items. Our experimental results show the significantly fast running
time of PixelSNE by a large margin against BH-SNE, while maintaining the
minimal degradation in the embedding quality. Finally, the source code of our
method is publicly available at https://github.com/awesome-davian/PixelSNE
|
[
"Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park,\n Jaegul Choo",
"['Minjeong Kim' 'Minsuk Choi' 'Sunwoong Lee' 'Jian Tang' 'Haesun Park'\n 'Jaegul Choo']"
] |
cs.LG cs.CV
| null |
1611.02639
| null | null |
http://arxiv.org/pdf/1611.02639v2
|
2016-11-15T19:55:26Z
|
2016-11-08T18:10:44Z
|
Gradients of Counterfactuals
|
Gradients have been used to quantify feature importance in machine learning
models. Unfortunately, in nonlinear deep networks, not only individual neurons
but also the whole network can saturate, and as a result an important input
feature can have a tiny gradient. We study various networks, and observe that
this phenomena is indeed widespread, across many inputs.
We propose to examine interior gradients, which are gradients of
counterfactual inputs constructed by scaling down the original input. We apply
our method to the GoogleNet architecture for object recognition in images, as
well as a ligand-based virtual screening network with categorical features and
an LSTM based language model for the Penn Treebank dataset. We visualize how
interior gradients better capture feature importance. Furthermore, interior
gradients are applicable to a wide variety of deep networks, and have the
attribution property that the feature importance scores sum to the the
prediction score.
Best of all, interior gradients can be computed just as easily as gradients.
In contrast, previous methods are complex to implement, which hinders practical
adoption.
|
[
"Mukund Sundararajan, Ankur Taly, Qiqi Yan",
"['Mukund Sundararajan' 'Ankur Taly' 'Qiqi Yan']"
] |
cs.LG cs.NE stat.ML
| null |
1611.02648
| null | null |
http://arxiv.org/pdf/1611.02648v2
|
2017-01-13T17:53:10Z
|
2016-11-08T18:36:36Z
|
Deep Unsupervised Clustering with Gaussian Mixture Variational
Autoencoders
|
We study a variant of the variational autoencoder model (VAE) with a Gaussian
mixture as a prior distribution, with the goal of performing unsupervised
clustering through deep generative models. We observe that the known problem of
over-regularisation that has been shown to arise in regular VAEs also manifests
itself in our model and leads to cluster degeneracy. We show that a heuristic
called minimum information constraint that has been shown to mitigate this
effect in VAEs can also be applied to improve unsupervised clustering
performance with our model. Furthermore we analyse the effect of this heuristic
and provide an intuition of the various processes with the help of
visualizations. Finally, we demonstrate the performance of our model on
synthetic data, MNIST and SVHN, showing that the obtained clusters are
distinct, interpretable and result in achieving competitive performance on
unsupervised clustering to the state-of-the-art results.
|
[
"['Nat Dilokthanakul' 'Pedro A. M. Mediano' 'Marta Garnelo'\n 'Matthew C. H. Lee' 'Hugh Salimbeni' 'Kai Arulkumaran' 'Murray Shanahan']",
"Nat Dilokthanakul, Pedro A.M. Mediano, Marta Garnelo, Matthew C.H.\n Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan"
] |
cs.CL cs.AI cs.LG
| null |
1611.02654
| null | null |
http://arxiv.org/pdf/1611.02654v2
|
2017-12-22T02:36:08Z
|
2016-11-08T19:04:09Z
|
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
|
Modeling the structure of coherent texts is a key NLP problem. The task of
coherently organizing a given set of sentences has been commonly used to build
and evaluate models that understand such structure. We propose an end-to-end
unsupervised deep learning approach based on the set-to-sequence framework to
address this problem. Our model strongly outperforms prior methods in the order
discrimination task and a novel task of ordering abstracts from scientific
articles. Furthermore, our work shows that useful text representations can be
obtained by learning to order sentences. Visualizing the learned sentence
representations shows that the model captures high-level logical structure in
paragraphs. Our representations perform comparably to state-of-the-art
pre-training methods on sentence similarity and paraphrase detection tasks.
|
[
"['Lajanugen Logeswaran' 'Honglak Lee' 'Dragomir Radev']",
"Lajanugen Logeswaran, Honglak Lee, Dragomir Radev"
] |
cs.CL cs.LG cs.NE
| null |
1611.02683
| null | null |
http://arxiv.org/pdf/1611.02683v2
|
2018-02-22T01:57:27Z
|
2016-11-08T20:42:26Z
|
Unsupervised Pretraining for Sequence to Sequence Learning
|
This work presents a general unsupervised learning method to improve the
accuracy of sequence to sequence (seq2seq) models. In our method, the weights
of the encoder and decoder of a seq2seq model are initialized with the
pretrained weights of two language models and then fine-tuned with labeled
data. We apply this method to challenging benchmarks in machine translation and
abstractive summarization and find that it significantly improves the
subsequent supervised models. Our main result is that pretraining improves the
generalization of seq2seq models. We achieve state-of-the art results on the
WMT English$\rightarrow$German task, surpassing a range of methods using both
phrase-based machine translation and neural machine translation. Our method
achieves a significant improvement of 1.3 BLEU from the previous best models on
both WMT'14 and WMT'15 English$\rightarrow$German. We also conduct human
evaluations on abstractive summarization and find that our method outperforms a
purely supervised learning baseline in a statistically significant manner.
|
[
"Prajit Ramachandran, Peter J. Liu, Quoc V. Le",
"['Prajit Ramachandran' 'Peter J. Liu' 'Quoc V. Le']"
] |
cs.LG stat.ML
| null |
1611.02731
| null | null |
http://arxiv.org/pdf/1611.02731v2
|
2017-03-04T06:19:22Z
|
2016-11-08T21:43:34Z
|
Variational Lossy Autoencoder
|
Representation learning seeks to expose certain aspects of observed data in a
learned representation that's amenable to downstream tasks like classification.
For instance, a good representation for 2D images might be one that describes
only global structure and discards information about detailed texture. In this
paper, we present a simple but principled method to learn such global
representations by combining Variational Autoencoder (VAE) with neural
autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE
model allows us to have control over what the global latent code can learn and
, by designing the architecture accordingly, we can force the global latent
code to discard irrelevant information such as texture in 2D images, and hence
the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging
autoregressive models as both prior distribution $p(z)$ and decoding
distribution $p(x|z)$, we can greatly improve generative modeling performance
of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and
Caltech-101 Silhouettes density estimation tasks.
|
[
"['Xi Chen' 'Diederik P. Kingma' 'Tim Salimans' 'Yan Duan'\n 'Prafulla Dhariwal' 'John Schulman' 'Ilya Sutskever' 'Pieter Abbeel']",
"Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla\n Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel"
] |
cs.LG math.DS
| null |
1611.02739
| null | null |
http://arxiv.org/pdf/1611.02739v4
|
2017-03-23T18:40:46Z
|
2016-11-08T22:09:22Z
|
Recursive Regression with Neural Networks: Approximating the HJI PDE
Solution
|
The majority of methods used to compute approximations to the
Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) rely on the
discretization of the state space to perform dynamic programming updates. This
type of approach is known to suffer from the curse of dimensionality due to the
exponential growth in grid points with the state dimension. In this work we
present an approximate dynamic programming algorithm that computes an
approximation of the solution of the HJI PDE by alternating between solving a
regression problem and solving a minimax problem using a feedforward neural
network as the function approximator. We find that this method requires less
memory to run and to store the approximation than traditional gridding methods,
and we test it on a few systems of two, three and six dimensions.
|
[
"['Vicenç Rubies-Royo' 'Claire Tomlin']",
"Vicen\\c{c} Rubies-Royo, Claire Tomlin"
] |
cs.AI cs.LG stat.ML
| null |
1611.02755
| null | null |
http://arxiv.org/pdf/1611.02755v1
|
2016-11-08T22:52:08Z
|
2016-11-08T22:52:08Z
|
Recursive Decomposition for Nonconvex Optimization
|
Continuous optimization is an important problem in many areas of AI,
including vision, robotics, probabilistic inference, and machine learning.
Unfortunately, most real-world optimization problems are nonconvex, causing
standard convex techniques to find only local optima, even with extensions like
random restarts and simulated annealing. We observe that, in many cases, the
local modes of the objective function have combinatorial structure, and thus
ideas from combinatorial optimization can be brought to bear. Based on this, we
propose a problem-decomposition approach to nonconvex optimization. Similarly
to DPLL-style SAT solvers and recursive conditioning in probabilistic
inference, our algorithm, RDIS, recursively sets variables so as to simplify
and decompose the objective function into approximately independent
sub-functions, until the remaining functions are simple enough to be optimized
by standard techniques like gradient descent. The variables to set are chosen
by graph partitioning, ensuring decomposition whenever possible. We show
analytically that RDIS can solve a broad class of nonconvex optimization
problems exponentially faster than gradient descent with random restarts.
Experimentally, RDIS outperforms standard techniques on problems like structure
from motion and protein folding.
|
[
"['Abram L. Friesen' 'Pedro Domingos']",
"Abram L. Friesen and Pedro Domingos"
] |
cs.LG
| null |
1611.0277
| null | null | null | null | null |
Delving into Transferable Adversarial Examples and Black-box Attacks
|
An intriguing property of deep neural networks is the existence of
adversarial examples, which can transfer among different architectures. These
transferable adversarial examples may severely hinder deep neural network-based
applications. Previous works mostly study the transferability using small scale
datasets. In this work, we are the first to conduct an extensive study of the
transferability over large models and a large scale dataset, and we are also
the first to study the transferability of targeted adversarial examples with
their target labels. We study both non-targeted and targeted adversarial
examples, and show that while transferable non-targeted adversarial examples
are easy to find, targeted adversarial examples generated using existing
approaches almost never transfer with their target labels. Therefore, we
propose novel ensemble-based approaches to generating transferable adversarial
examples. Using such approaches, we observe a large proportion of targeted
adversarial examples that are able to transfer with their target labels for the
first time. We also present some geometric studies to help understanding the
transferable adversarial examples. Finally, we show that the adversarial
examples generated using ensemble-based approaches can successfully attack
Clarifai.com, which is a black-box image classification system.
|
[
"Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song"
] |
null | null |
1611.02770
| null | null |
http://arxiv.org/pdf/1611.02770v3
|
2017-02-07T14:24:44Z
|
2016-11-08T23:25:00Z
|
Delving into Transferable Adversarial Examples and Black-box Attacks
|
An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, which is a black-box image classification system.
|
[
"['Yanpei Liu' 'Xinyun Chen' 'Chang Liu' 'Dawn Song']"
] |
cs.AI cs.LG cs.NE stat.ML
| null |
1611.02779
| null | null |
http://arxiv.org/pdf/1611.02779v2
|
2016-11-10T01:17:36Z
|
2016-11-09T00:13:29Z
|
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
|
Deep reinforcement learning (deep RL) has been successful in learning
sophisticated behaviors automatically; however, the learning process requires a
huge number of trials. In contrast, animals can learn new tasks in just a few
trials, benefiting from their prior knowledge about the world. This paper seeks
to bridge this gap. Rather than designing a "fast" reinforcement learning
algorithm, we propose to represent it as a recurrent neural network (RNN) and
learn it from data. In our proposed method, RL$^2$, the algorithm is encoded in
the weights of the RNN, which are learned slowly through a general-purpose
("slow") RL algorithm. The RNN receives all information a typical RL algorithm
would receive, including observations, actions, rewards, and termination flags;
and it retains its state across episodes in a given Markov Decision Process
(MDP). The activations of the RNN store the state of the "fast" RL algorithm on
the current (previously unseen) MDP. We evaluate RL$^2$ experimentally on both
small-scale and large-scale problems. On the small-scale side, we train it to
solve randomly generated multi-arm bandit problems and finite MDPs. After
RL$^2$ is trained, its performance on new MDPs is close to human-designed
algorithms with optimality guarantees. On the large-scale side, we test RL$^2$
on a vision-based navigation task and show that it scales up to
high-dimensional problems.
|
[
"Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever,\n Pieter Abbeel",
"['Yan Duan' 'John Schulman' 'Xi Chen' 'Peter L. Bartlett' 'Ilya Sutskever'\n 'Pieter Abbeel']"
] |
cs.LG cs.AI
| null |
1611.02796
| null | null |
http://arxiv.org/pdf/1611.02796v9
|
2017-10-16T21:31:31Z
|
2016-11-09T01:46:32Z
|
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models
with KL-control
|
This paper proposes a general method for improving the structure and quality
of sequences generated by a recurrent neural network (RNN), while maintaining
information originally learned from data, as well as sample diversity. An RNN
is first pre-trained on data using maximum likelihood estimation (MLE), and the
probability distribution over the next token in the sequence learned by this
model is treated as a prior policy. Another RNN is then trained using
reinforcement learning (RL) to generate higher-quality outputs that account for
domain-specific incentives while retaining proximity to the prior policy of the
MLE RNN. To formalize this objective, we derive novel off-policy RL methods for
RNNs from KL-control. The effectiveness of the approach is demonstrated on two
applications; 1) generating novel musical melodies, and 2) computational
molecular generation. For both problems, we show that the proposed method
improves the desired properties and structure of the generated sequences, while
maintaining information learned from data.
|
[
"Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, Jos\\'e Miguel\n Hern\\'andez-Lobato, Richard E. Turner, Douglas Eck",
"['Natasha Jaques' 'Shixiang Gu' 'Dzmitry Bahdanau'\n 'José Miguel Hernández-Lobato' 'Richard E. Turner' 'Douglas Eck']"
] |
cs.LG
| null |
1611.0283
| null | null | null | null | null |
Online Learning for Wireless Distributed Computing
|
There has been a growing interest for Wireless Distributed Computing (WDC),
which leverages collaborative computing over multiple wireless devices. WDC
enables complex applications that a single device cannot support individually.
However, the problem of assigning tasks over multiple devices becomes
challenging in the dynamic environments encountered in real-world settings,
considering that the resource availability and channel conditions change over
time in unpredictable ways due to mobility and other factors. In this paper, we
formulate a task assignment problem as an online learning problem using an
adversarial multi-armed bandit framework. We propose MABSTA, a novel online
learning algorithm that learns the performance of unknown devices and channel
qualities continually through exploratory probing and makes task assignment
decisions by exploiting the gained knowledge. For maximal adaptability, MABSTA
is designed to make no stochastic assumption about the environment. We analyze
it mathematically and provide a worst-case performance guarantee for any
dynamic environment. We also compare it with the optimal offline policy as well
as other baselines via emulations on trace-data obtained from a wireless IoT
testbed, and show that it offers competitive and robust performance in all
cases. To the best of our knowledge, MABSTA is the first online algorithm in
this domain of task assignment problems and provides provable performance
guarantee.
|
[
"Yi-Hsuan Kao, Kwame Wright, Bhaskar Krishnamachari, Fan Bai"
] |
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