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Robustly Learning a Gaussian: Getting Optimal Error, Efficiently | cs.DS cs.IT cs.LG math.IT math.ST stat.ML stat.TH | We study the fundamental problem of learning the parameters of a
high-dimensional Gaussian in the presence of noise -- where an
$\varepsilon$-fraction of our samples were chosen by an adversary. We give
robust estimators that achieve estimation error $O(\varepsilon)$ in the total
variation distance, which is optimal up to a universal constant that is
independent of the dimension.
In the case where just the mean is unknown, our robustness guarantee is
optimal up to a factor of $\sqrt{2}$ and the running time is polynomial in $d$
and $1/\epsilon$. When both the mean and covariance are unknown, the running
time is polynomial in $d$ and quasipolynomial in $1/\varepsilon$. Moreover all
of our algorithms require only a polynomial number of samples. Our work shows
that the same sorts of error guarantees that were established over fifty years
ago in the one-dimensional setting can also be achieved by efficient algorithms
in high-dimensional settings.
| Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur
Moitra, Alistair Stewart | null | 1704.03866 | null | null |
Value Directed Exploration in Multi-Armed Bandits with Structured Priors | cs.LG cs.AI stat.ML | Multi-armed bandits are a quintessential machine learning problem requiring
the balancing of exploration and exploitation. While there has been progress in
developing algorithms with strong theoretical guarantees, there has been less
focus on practical near-optimal finite-time performance. In this paper, we
propose an algorithm for Bayesian multi-armed bandits that utilizes
value-function-driven online planning techniques. Building on previous work on
UCB and Gittins index, we introduce linearly-separable value functions that
take both the expected return and the benefit of exploration into consideration
to perform n-step lookahead. The algorithm enjoys a sub-linear performance
guarantee and we present simulation results that confirm its strength in
problems with structured priors. The simplicity and generality of our approach
makes it a strong candidate for analyzing more complex multi-armed bandit
problems.
| Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml | null | 1704.03926 | null | null |
Convergence analysis of the information matrix in Gaussian belief
propagation | cs.LG | Gaussian belief propagation (BP) has been widely used for distributed
estimation in large-scale networks such as the smart grid, communication
networks, and social networks, where local measurements/observations are
scattered over a wide geographical area. However, the convergence of Gaus- sian
BP is still an open issue. In this paper, we consider the convergence of
Gaussian BP, focusing in particular on the convergence of the information
matrix. We show analytically that the exchanged message information matrix
converges for arbitrary positive semidefinite initial value, and its dis- tance
to the unique positive definite limit matrix decreases exponentially fast.
| Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar and Jos\'e M. F. Moura | null | 1704.03969 | null | null |
On the Effects of Batch and Weight Normalization in Generative
Adversarial Networks | stat.ML cs.CV cs.LG | Generative adversarial networks (GANs) are highly effective unsupervised
learning frameworks that can generate very sharp data, even for data such as
images with complex, highly multimodal distributions. However GANs are known to
be very hard to train, suffering from problems such as mode collapse and
disturbing visual artifacts. Batch normalization (BN) techniques have been
introduced to address the training. Though BN accelerates the training in the
beginning, our experiments show that the use of BN can be unstable and
negatively impact the quality of the trained model. The evaluation of BN and
numerous other recent schemes for improving GAN training is hindered by the
lack of an effective objective quality measure for GAN models. To address these
issues, we first introduce a weight normalization (WN) approach for GAN
training that significantly improves the stability, efficiency and the quality
of the generated samples. To allow a methodical evaluation, we introduce
squared Euclidean reconstruction error on a test set as a new objective
measure, to assess training performance in terms of speed, stability, and
quality of generated samples. Our experiments with a standard DCGAN
architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)
indicate that training using WN is generally superior to BN for GANs, achieving
10% lower mean squared loss for reconstruction and significantly better
qualitative results than BN. We further demonstrate the stability of WN on a
21-layer ResNet trained with the CelebA data set. The code for this paper is
available at https://github.com/stormraiser/gan-weightnorm-resnet
| Sitao Xiang, Hao Li | null | 1704.03971 | null | null |
Virtual Adversarial Training: A Regularization Method for Supervised and
Semi-Supervised Learning | stat.ML cs.LG | We propose a new regularization method based on virtual adversarial loss: a
new measure of local smoothness of the conditional label distribution given
input. Virtual adversarial loss is defined as the robustness of the conditional
label distribution around each input data point against local perturbation.
Unlike adversarial training, our method defines the adversarial direction
without label information and is hence applicable to semi-supervised learning.
Because the directions in which we smooth the model are only "virtually"
adversarial, we call our method virtual adversarial training (VAT). The
computational cost of VAT is relatively low. For neural networks, the
approximated gradient of virtual adversarial loss can be computed with no more
than two pairs of forward- and back-propagations. In our experiments, we
applied VAT to supervised and semi-supervised learning tasks on multiple
benchmark datasets. With a simple enhancement of the algorithm based on the
entropy minimization principle, our VAT achieves state-of-the-art performance
for semi-supervised learning tasks on SVHN and CIFAR-10.
| Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii | null | 1704.03976 | null | null |
Fully Distributed and Asynchronized Stochastic Gradient Descent for
Networked Systems | cs.LG cs.AI cs.PF | This paper considers a general data-fitting problem over a networked system,
in which many computing nodes are connected by an undirected graph. This kind
of problem can find many real-world applications and has been studied
extensively in the literature. However, existing solutions either need a
central controller for information sharing or requires slot synchronization
among different nodes, which increases the difficulty of practical
implementations, especially for a very large and heterogeneous system.
As a contrast, in this paper, we treat the data-fitting problem over the
network as a stochastic programming problem with many constraints. By adapting
the results in a recent paper, we design a fully distributed and asynchronized
stochastic gradient descent (SGD) algorithm. We show that our algorithm can
achieve global optimality and consensus asymptotically by only local
computations and communications. Additionally, we provide a sharp lower bound
for the convergence speed in the regular graph case. This result fits the
intuition and provides guidance to design a `good' network topology to speed up
the convergence. Also, the merit of our design is validated by experiments on
both synthetic and real-world datasets.
| Ying Zhang | null | 1704.03992 | null | null |
ZigZag: A new approach to adaptive online learning | cs.LG math.OC stat.ML | We develop a novel family of algorithms for the online learning setting with
regret against any data sequence bounded by the empirical Rademacher complexity
of that sequence. To develop a general theory of when this type of adaptive
regret bound is achievable we establish a connection to the theory of
decoupling inequalities for martingales in Banach spaces. When the hypothesis
class is a set of linear functions bounded in some norm, such a regret bound is
achievable if and only if the norm satisfies certain decoupling inequalities
for martingales. Donald Burkholder's celebrated geometric characterization of
decoupling inequalities (1984) states that such an inequality holds if and only
if there exists a special function called a Burkholder function satisfying
certain restricted concavity properties. Our online learning algorithms are
efficient in terms of queries to this function.
We realize our general theory by giving novel efficient algorithms for
classes including lp norms, Schatten p-norms, group norms, and reproducing
kernel Hilbert spaces. The empirical Rademacher complexity regret bound implies
--- when used in the i.i.d. setting --- a data-dependent complexity bound for
excess risk after online-to-batch conversion. To showcase the power of the
empirical Rademacher complexity regret bound, we derive improved rates for a
supervised learning generalization of the online learning with low rank experts
task and for the online matrix prediction task.
In addition to obtaining tight data-dependent regret bounds, our algorithms
enjoy improved efficiency over previous techniques based on Rademacher
complexity, automatically work in the infinite horizon setting, and are
scale-free. To obtain such adaptive methods, we introduce novel machinery, and
the resulting algorithms are not based on the standard tools of online convex
optimization.
| Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan | null | 1704.0401 | null | null |
3D Deep Learning for Biological Function Prediction from Physical Fields | q-bio.BM cs.LG q-bio.QM stat.ML | Predicting the biological function of molecules, be it proteins or drug-like
compounds, from their atomic structure is an important and long-standing
problem. Function is dictated by structure, since it is by spatial interactions
that molecules interact with each other, both in terms of steric
complementarity, as well as intermolecular forces. Thus, the electron density
field and electrostatic potential field of a molecule contain the "raw
fingerprint" of how this molecule can fit to binding partners. In this paper,
we show that deep learning can predict biological function of molecules
directly from their raw 3D approximated electron density and electrostatic
potential fields. Protein function based on EC numbers is predicted from the
approximated electron density field. In another experiment, the activity of
small molecules is predicted with quality comparable to state-of-the-art
descriptor-based methods. We propose several alternative computational models
for the GPU with different memory and runtime requirements for different sizes
of molecules and of databases. We also propose application-specific
multi-channel data representations. With future improvements of training
datasets and neural network settings in combination with complementary
information sources (sequence, genomic context, expression level), deep
learning can be expected to show its generalization power and revolutionize the
field of molecular function prediction.
| Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov,
Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler and Daniel Cremers | null | 1704.04039 | null | null |
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction
in Manifold Learning | stat.ME cs.LG stat.ML | Recently manifold learning algorithm for dimensionality reduction attracts
more and more interests, and various linear and nonlinear, global and local
algorithms are proposed. The key step of manifold learning algorithm is the
neighboring region selection. However, so far for the references we know, few
of which propose a generally accepted algorithm to well select the neighboring
region. So in this paper, we propose an adaptive neighboring selection
algorithm, which successfully applies the LLE and ISOMAP algorithms in the
test. It is an algorithm that can find the optimal K nearest neighbors of the
data points on the manifold. And the theoretical basis of the algorithm is the
approximated curvature of the data point on the manifold. Based on Riemann
Geometry, Jacob matrix is a proper mathematical concept to predict the
approximated curvature. By verifying the proposed algorithm on embedding Swiss
roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results
show that the proposed adaptive neighboring selection algorithm is feasible and
able to find the optimal value of K, making the residual variance relatively
small and better visualization of the results. By quantitative analysis, the
embedding quality measured by residual variance is increased 45.45% after using
the proposed algorithm in LLE.
| Lin Ma, Caifa Zhou, Xi Liu, Yubin Xu | null | 1704.0405 | null | null |
Land Cover Classification via Multi-temporal Spatial Data by Recurrent
Neural Networks | cs.CV cs.LG | Nowadays, modern earth observation programs produce huge volumes of satellite
images time series (SITS) that can be useful to monitor geographical areas
through time. How to efficiently analyze such kind of information is still an
open question in the remote sensing field. Recently, deep learning methods
proved suitable to deal with remote sensing data mainly for scene
classification (i.e. Convolutional Neural Networks - CNNs - on single images)
while only very few studies exist involving temporal deep learning approaches
(i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series.
In this letter we evaluate the ability of Recurrent Neural Networks, in
particular the Long-Short Term Memory (LSTM) model, to perform land cover
classification considering multi-temporal spatial data derived from a time
series of satellite images. We carried out experiments on two different
datasets considering both pixel-based and object-based classification. The
obtained results show that Recurrent Neural Networks are competitive compared
to state-of-the-art classifiers, and may outperform classical approaches in
presence of low represented and/or highly mixed classes. We also show that
using the alternative feature representation generated by LSTM can improve the
performances of standard classifiers.
| Dino Ienco, Raffaele Gaetano, Claire Dupaquier and Pierre Maurel | 10.1109/LGRS.2017.2728698 | 1704.04055 | null | null |
Training Neural Networks Based on Imperialist Competitive Algorithm for
Predicting Earthquake Intensity | cs.NE cs.LG | In this study we determined neural network weights and biases by Imperialist
Competitive Algorithm (ICA) in order to train network for predicting earthquake
intensity in Richter. For this reason, we used dependent parameters like
earthquake occurrence time, epicenter's latitude and longitude in degree, focal
depth in kilometer, and the seismological center distance from epicenter and
earthquake focal center in kilometer which has been provided by Berkeley data
base. The studied neural network has two hidden layer: its first layer has 16
neurons and the second layer has 24 neurons. By using ICA algorithm, average
error for testing data is 0.0007 with a variance equal to 0.318. The earthquake
prediction error in Richter by MSE criteria for ICA algorithm is 0.101, but by
using GA, the MSE value is 0.115.
| Mohsen Moradi | null | 1704.04095 | null | null |
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | cs.AI cs.LG stat.ML | Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this paper we propose DeepAR, a methodology for producing accurate
probabilistic forecasts, based on training an auto regressive recurrent network
model on a large number of related time series. We demonstrate how by applying
deep learning techniques to forecasting, one can overcome many of the
challenges faced by widely-used classical approaches to the problem. We show
through extensive empirical evaluation on several real-world forecasting data
sets accuracy improvements of around 15% compared to state-of-the-art methods.
| David Salinas, Valentin Flunkert, Jan Gasthaus | null | 1704.0411 | null | null |
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR)
Approach to Understanding Deep Neural Networks | cs.CV cs.AI cs.LG cs.MM | In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an
approach to visualize and understand the decisions made by deep neural networks
(DNNs) given a specific input. CLEAR facilitates the visualization of attentive
regions and levels of interest of DNNs during the decision-making process. It
also enables the visualization of the most dominant classes associated with
these attentive regions of interest. As such, CLEAR can mitigate some of the
shortcomings of heatmap-based methods associated with decision ambiguity, and
allows for better insights into the decision-making process of DNNs.
Quantitative and qualitative experiments across three different datasets
demonstrate the efficacy of CLEAR for gaining a better understanding of the
inner workings of DNNs during the decision-making process.
| Devinder Kumar, Alexander Wong, Graham W. Taylor | null | 1704.04133 | null | null |
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and
Hardness | cs.DS cs.LG math.NA | Understanding the singular value spectrum of a matrix $A \in \mathbb{R}^{n
\times n}$ is a fundamental task in countless applications. In matrix
multiplication time, it is possible to perform a full SVD and directly compute
the singular values $\sigma_1,...,\sigma_n$. However, little is known about
algorithms that break this runtime barrier.
Using tools from stochastic trace estimation, polynomial approximation, and
fast system solvers, we show how to efficiently isolate different ranges of
$A$'s spectrum and approximate the number of singular values in these ranges.
We thus effectively compute a histogram of the spectrum, which can stand in for
the true singular values in many applications.
We use this primitive to give the first algorithms for approximating a wide
class of symmetric matrix norms in faster than matrix multiplication time. For
example, we give a $(1 + \epsilon)$ approximation algorithm for the
Schatten-$1$ norm (the nuclear norm) running in just $\tilde O((nnz(A)n^{1/3} +
n^2)\epsilon^{-3})$ time for $A$ with uniform row sparsity or $\tilde
O(n^{2.18} \epsilon^{-3})$ time for dense matrices. The runtime scales smoothly
for general Schatten-$p$ norms, notably becoming $\tilde O (p \cdot nnz(A)
\epsilon^{-3})$ for any $p \ge 2$.
At the same time, we show that the complexity of spectrum approximation is
inherently tied to fast matrix multiplication in the small $\epsilon$ regime.
We prove that achieving milder $\epsilon$ dependencies in our algorithms would
imply faster than matrix multiplication time triangle detection for general
graphs. This further implies that highly accurate algorithms running in
subcubic time yield subcubic time matrix multiplication. As an application of
our bounds, we show that precisely computing all effective resistances in a
graph in less than matrix multiplication time is likely difficult, barring a
major algorithmic breakthrough.
| Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru,
David P. Woodruff | null | 1704.04163 | null | null |
Learning Latent Representations for Speech Generation and Transformation | cs.CL cs.LG stat.ML | An ability to model a generative process and learn a latent representation
for speech in an unsupervised fashion will be crucial to process vast
quantities of unlabelled speech data. Recently, deep probabilistic generative
models such as Variational Autoencoders (VAEs) have achieved tremendous success
in modeling natural images. In this paper, we apply a convolutional VAE to
model the generative process of natural speech. We derive latent space
arithmetic operations to disentangle learned latent representations. We
demonstrate the capability of our model to modify the phonetic content or the
speaker identity for speech segments using the derived operations, without the
need for parallel supervisory data.
| Wei-Ning Hsu, Yu Zhang, James Glass | null | 1704.04222 | null | null |
Close Yet Distinctive Domain Adaptation | cs.LG cs.CV stat.ML | Domain adaptation is transfer learning which aims to generalize a learning
model across training and testing data with different distributions. Most
previous research tackle this problem in seeking a shared feature
representation between source and target domains while reducing the mismatch of
their data distributions. In this paper, we propose a close yet discriminative
domain adaptation method, namely CDDA, which generates a latent feature
representation with two interesting properties. First, the discrepancy between
the source and target domain, measured in terms of both marginal and
conditional probability distribution via Maximum Mean Discrepancy is minimized
so as to attract two domains close to each other. More importantly, we also
design a repulsive force term, which maximizes the distances between each label
dependent sub-domain to all others so as to drag different class dependent
sub-domains far away from each other and thereby increase the discriminative
power of the adapted domain. Moreover, given the fact that the underlying data
manifold could have complex geometric structure, we further propose the
constraints of label smoothness and geometric structure consistency for label
propagation. Extensive experiments are conducted on 36 cross-domain image
classification tasks over four public datasets. The comprehensive results show
that the proposed method consistently outperforms the state-of-the-art methods
with significant margins.
| Lingkun Luo, Xiaofang Wang, Shiqiang Hu, Chao Wang, Yuxing Tang,
Liming Chen | null | 1704.04235 | null | null |
A dynamic connectome supports the emergence of stable computational
function of neural circuits through reward-based learning | q-bio.NC cs.LG cs.NE | Synaptic connections between neurons in the brain are dynamic because of
continuously ongoing spine dynamics, axonal sprouting, and other processes. In
fact, it was recently shown that the spontaneous synapse-autonomous component
of spine dynamics is at least as large as the component that depends on the
history of pre- and postsynaptic neural activity. These data are inconsistent
with common models for network plasticity, and raise the questions how neural
circuits can maintain a stable computational function in spite of these
continuously ongoing processes, and what functional uses these ongoing
processes might have. Here, we present a rigorous theoretical framework for
these seemingly stochastic spine dynamics and rewiring processes in the context
of reward-based learning tasks. We show that spontaneous synapse-autonomous
processes, in combination with reward signals such as dopamine, can explain the
capability of networks of neurons in the brain to configure themselves for
specific computational tasks, and to compensate automatically for later changes
in the network or task. Furthermore we show theoretically and through computer
simulations that stable computational performance is compatible with
continuously ongoing synapse-autonomous changes. After reaching good
computational performance it causes primarily a slow drift of network
architecture and dynamics in task-irrelevant dimensions, as observed for neural
activity in motor cortex and other areas. On the more abstract level of
reinforcement learning the resulting model gives rise to an understanding of
reward-driven network plasticity as continuous sampling of network
configurations.
| David Kappel, Robert Legenstein, Stefan Habenschuss, Michael Hsieh and
Wolfgang Maass | null | 1704.04238 | null | null |
Stochastic Gradient Descent as Approximate Bayesian Inference | stat.ML cs.LG | Stochastic Gradient Descent with a constant learning rate (constant SGD)
simulates a Markov chain with a stationary distribution. With this perspective,
we derive several new results. (1) We show that constant SGD can be used as an
approximate Bayesian posterior inference algorithm. Specifically, we show how
to adjust the tuning parameters of constant SGD to best match the stationary
distribution to a posterior, minimizing the Kullback-Leibler divergence between
these two distributions. (2) We demonstrate that constant SGD gives rise to a
new variational EM algorithm that optimizes hyperparameters in complex
probabilistic models. (3) We also propose SGD with momentum for sampling and
show how to adjust the damping coefficient accordingly. (4) We analyze MCMC
algorithms. For Langevin Dynamics and Stochastic Gradient Fisher Scoring, we
quantify the approximation errors due to finite learning rates. Finally (5), we
use the stochastic process perspective to give a short proof of why Polyak
averaging is optimal. Based on this idea, we propose a scalable approximate
MCMC algorithm, the Averaged Stochastic Gradient Sampler.
| Stephan Mandt, Matthew D. Hoffman, and David M. Blei | null | 1704.04289 | null | null |
CBinfer: Change-Based Inference for Convolutional Neural Networks on
Video Data | cs.CV cs.AI cs.LG cs.PF eess.IV | Extracting per-frame features using convolutional neural networks for
real-time processing of video data is currently mainly performed on powerful
GPU-accelerated workstations and compute clusters. However, there are many
applications such as smart surveillance cameras that require or would benefit
from on-site processing. To this end, we propose and evaluate a novel algorithm
for change-based evaluation of CNNs for video data recorded with a static
camera setting, exploiting the spatio-temporal sparsity of pixel changes. We
achieve an average speed-up of 8.6x over a cuDNN baseline on a realistic
benchmark with a negligible accuracy loss of less than 0.1% and no retraining
of the network. The resulting energy efficiency is 10x higher than that of
per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1
platform.
| Lukas Cavigelli, Philippe Degen, Luca Benini | null | 1704.04313 | null | null |
Deep API Programmer: Learning to Program with APIs | cs.AI cs.LG | We present DAPIP, a Programming-By-Example system that learns to program with
APIs to perform data transformation tasks. We design a domain-specific language
(DSL) that allows for arbitrary concatenations of API outputs and constant
strings. The DSL consists of three family of APIs: regular expression-based
APIs, lookup APIs, and transformation APIs. We then present a novel neural
synthesis algorithm to search for programs in the DSL that are consistent with
a given set of examples. The search algorithm uses recently introduced neural
architectures to encode input-output examples and to model the program search
in the DSL. We show that synthesis algorithm outperforms baseline methods for
synthesizing programs on both synthetic and real-world benchmarks.
| Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli | null | 1704.04327 | null | null |
Cross-media Similarity Metric Learning with Unified Deep Networks | cs.MM cs.LG stat.ML | As a highlighting research topic in the multimedia area, cross-media
retrieval aims to capture the complex correlations among multiple media types.
Learning better shared representation and distance metric for multimedia data
is important to boost the cross-media retrieval. Motivated by the strong
ability of deep neural network in feature representation and comparison
functions learning, we propose the Unified Network for Cross-media Similarity
Metric (UNCSM) to associate cross-media shared representation learning with
distance metric in a unified framework. First, we design a two-pathway deep
network pretrained with contrastive loss, and employ double triplet similarity
loss for fine-tuning to learn the shared representation for each media type by
modeling the relative semantic similarity. Second, the metric network is
designed for effectively calculating the cross-media similarity of the shared
representation, by modeling the pairwise similar and dissimilar constraints.
Compared to the existing methods which mostly ignore the dissimilar constraints
and only use sample distance metric as Euclidean distance separately, our UNCSM
approach unifies the representation learning and distance metric to preserve
the relative similarity as well as embrace more complex similarity functions
for further improving the cross-media retrieval accuracy. The experimental
results show that our UNCSM approach outperforms 8 state-of-the-art methods on
4 widely-used cross-media datasets.
| Jinwei Qi, Xin Huang, and Yuxin Peng | null | 1704.04333 | null | null |
Learning a collaborative multiscale dictionary based on robust empirical
mode decomposition | cs.CV cs.LG | Dictionary learning is a challenge topic in many image processing areas. The
basic goal is to learn a sparse representation from an overcomplete basis set.
Due to combining the advantages of generic multiscale representations with
learning based adaptivity, multiscale dictionary representation approaches have
the power in capturing structural characteristics of natural images. However,
existing multiscale learning approaches still suffer from three main
weaknesses: inadaptability to diverse scales of image data, sensitivity to
noise and outliers, difficulty to determine optimal dictionary structure. In
this paper, we present a novel multiscale dictionary learning paradigm for
sparse image representations based on an improved empirical mode decomposition.
This powerful data-driven analysis tool for multi-dimensional signal can fully
adaptively decompose the image into multiscale oscillating components according
to intrinsic modes of data self. This treatment can obtain a robust and
effective sparse representation, and meanwhile generates a raw base dictionary
at multiple geometric scales and spatial frequency bands. This dictionary is
refined by selecting optimal oscillating atoms based on frequency clustering.
In order to further enhance sparsity and generalization, a tolerance dictionary
is learned using a coherence regularized model. A fast proximal scheme is
developed to optimize this model. The multiscale dictionary is considered as
the product of oscillating dictionary and tolerance dictionary. Experimental
results demonstrate that the proposed learning approach has the superior
performance in sparse image representations as compared with several competing
methods. We also show the promising results in image denoising application.
| Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao | null | 1704.04422 | null | null |
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics | cs.CL cs.AI cs.LG | Coreference evaluation metrics are hard to optimize directly as they are
non-differentiable functions, not easily decomposable into elementary
decisions. Consequently, most approaches optimize objectives only indirectly
related to the end goal, resulting in suboptimal performance. Instead, we
propose a differentiable relaxation that lends itself to gradient-based
optimisation, thus bypassing the need for reinforcement learning or heuristic
modification of cross-entropy. We show that by modifying the training objective
of a competitive neural coreference system, we obtain a substantial gain in
performance. This suggests that our approach can be regarded as a viable
alternative to using reinforcement learning or more computationally expensive
imitation learning.
| Phong Le and Ivan Titov | null | 1704.04451 | null | null |
Liquid Splash Modeling with Neural Networks | cs.GR cs.LG | This paper proposes a new data-driven approach to model detailed splashes for
liquid simulations with neural networks. Our model learns to generate
small-scale splash detail for the fluid-implicit-particle method using training
data acquired from physically parametrized, high resolution simulations. We use
neural networks to model the regression of splash formation using a classifier
together with a velocity modifier. For the velocity modification, we employ a
heteroscedastic model. We evaluate our method for different spatial scales,
simulation setups, and solvers. Our simulation results demonstrate that our
model significantly improves visual fidelity with a large amount of realistic
droplet formation and yields splash detail much more efficiently than finer
discretizations.
| Kiwon Um, Xiangyu Hu, Nils Thuerey | null | 1704.04456 | null | null |
On Generalized Bellman Equations and Temporal-Difference Learning | cs.LG math.OC | We consider off-policy temporal-difference (TD) learning in discounted Markov
decision processes, where the goal is to evaluate a policy in a model-free way
by using observations of a state process generated without executing the
policy. To curb the high variance issue in off-policy TD learning, we propose a
new scheme of setting the $\lambda$-parameters of TD, based on generalized
Bellman equations. Our scheme is to set $\lambda$ according to the eligibility
trace iterates calculated in TD, thereby easily keeping these traces in a
desired bounded range. Compared with prior work, this scheme is more direct and
flexible, and allows much larger $\lambda$ values for off-policy TD learning
with bounded traces. As to its soundness, using Markov chain theory, we prove
the ergodicity of the joint state-trace process under nonrestrictive
conditions, and we show that associated with our scheme is a generalized
Bellman equation (for the policy to be evaluated) that depends on both the
evolution of $\lambda$ and the unique invariant probability measure of the
state-trace process. These results not only lead immediately to a
characterization of the convergence behavior of least-squares based
implementation of our scheme, but also prepare the ground for further analysis
of gradient-based implementations.
| Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton | null | 1704.04463 | null | null |
Lean From Thy Neighbor: Stochastic & Adversarial Bandits in a Network | cs.LG cs.SI | An individual's decisions are often guided by those of his or her peers,
i.e., neighbors in a social network. Presumably, being privy to the experiences
of others aids in learning and decision making, but how much advantage does an
individual gain by observing her neighbors? Such problems make appearances in
sociology and economics and, in this paper, we present a novel model to capture
such decision-making processes and appeal to the classical multi-armed bandit
framework to analyze it. Each individual, in addition to her own actions, can
observe the actions and rewards obtained by her neighbors, and can use all of
this information in order to minimize her own regret. We provide algorithms for
this setting, both for stochastic and adversarial bandits, and show that their
regret smoothly interpolates between the regret in the classical bandit setting
and that of the full-information setting as a function of the neighbors'
exploration. In the stochastic setting the additional information must simply
be incorporated into the usual estimation of the rewards, while in the
adversarial setting this is attained by constructing a new unbiased estimator
for the rewards and appropriately bounding the amount of additional information
provided by the neighbors. These algorithms are optimal up to log factors;
despite the fact that the agents act independently and selfishly, this implies
that it is an approximate Nash equilibria for all agents to use our algorithms.
Further, we show via empirical simulations that our algorithms, often
significantly, outperform existing algorithms that one could apply to this
setting.
| L. Elisa Celis and Farnood Salehi | null | 1704.0447 | null | null |
Hierarchic Kernel Recursive Least-Squares | cs.LG | We present a new kernel-based algorithm for modeling evenly distributed
multidimensional datasets that does not rely on input space sparsification. The
presented method reorganizes the typical single-layer kernel-based model into a
deep hierarchical structure, such that the weights of a kernel model over each
dimension are modeled over its adjacent dimension. We show that modeling
weights in the suggested structure leads to significant computational speedup
and improved modeling accuracy.
| Hossein Mohamadipanah, Mahdi Heydari, Girish Chowdhary | null | 1704.04522 | null | null |
On the Gap Between Strict-Saddles and True Convexity: An Omega(log d)
Lower Bound for Eigenvector Approximation | cs.LG cs.DS cs.IT math.CO math.IT stat.ML | We prove a \emph{query complexity} lower bound on rank-one principal
component analysis (PCA). We consider an oracle model where, given a symmetric
matrix $M \in \mathbb{R}^{d \times d}$, an algorithm is allowed to make $T$
\emph{exact} queries of the form $w^{(i)} = Mv^{(i)}$ for $i \in
\{1,\dots,T\}$, where $v^{(i)}$ is drawn from a distribution which depends
arbitrarily on the past queries and measurements $\{v^{(j)},w^{(j)}\}_{1 \le j
\le i-1}$. We show that for a small constant $\epsilon$, any adaptive,
randomized algorithm which can find a unit vector $\widehat{v}$ for which
$\widehat{v}^{\top}M\widehat{v} \ge (1-\epsilon)\|M\|$, with even small
probability, must make $T = \Omega(\log d)$ queries. In addition to settling a
widely-held folk conjecture, this bound demonstrates a fundamental gap between
convex optimization and "strict-saddle" non-convex optimization of which PCA is
a canonical example: in the former, first-order methods can have dimension-free
iteration complexity, whereas in PCA, the iteration complexity of
gradient-based methods must necessarily grow with the dimension. Our argument
proceeds via a reduction to estimating the rank-one spike in a deformed Wigner
model. We establish lower bounds for this model by developing a "truncated"
analogue of the $\chi^2$ Bayes-risk lower bound of Chen et al.
| Max Simchowitz, Ahmed El Alaoui, Benjamin Recht | null | 1704.04548 | null | null |
Asynchronous Parallel Empirical Variance Guided Algorithms for the
Thresholding Bandit Problem | stat.ML cs.LG | This paper considers the multi-armed thresholding bandit problem --
identifying all arms whose expected rewards are above a predefined threshold
via as few pulls (or rounds) as possible -- proposed by Locatelli et al. [2016]
recently. Although the proposed algorithm in Locatelli et al. [2016] achieves
the optimal round complexity in a certain sense, there still remain unsolved
issues. This paper proposes an asynchronous parallel thresholding algorithm and
its parameter-free version to improve the efficiency and the applicability. On
one hand, the proposed two algorithms use the empirical variance to guide the
pull decision at each round, and significantly improve the round complexity of
the "optimal" algorithm when all arms have bounded high order moments. The
proposed algorithms can be proven to be optimal. On the other hand, most bandit
algorithms assume that the reward can be observed immediately after the pull or
the next decision would not be made before all rewards are observed. Our
proposed asynchronous parallel algorithms allow making the choice of the next
pull with unobserved rewards from earlier pulls, which avoids such an
unrealistic assumption and significantly improves the identification process.
Our theoretical analysis justifies the effectiveness and the efficiency of
proposed asynchronous parallel algorithms.
| Jie Zhong, Yijun Huang, Ji Liu | null | 1704.04567 | null | null |
Deep Learning for Photoacoustic Tomography from Sparse Data | cs.CV cs.LG | The development of fast and accurate image reconstruction algorithms is a
central aspect of computed tomography. In this paper, we investigate this issue
for the sparse data problem in photoacoustic tomography (PAT). We develop a
direct and highly efficient reconstruction algorithm based on deep learning. In
our approach image reconstruction is performed with a deep convolutional neural
network (CNN), whose weights are adjusted prior to the actual image
reconstruction based on a set of training data. The proposed reconstruction
approach can be interpreted as a network that uses the PAT filtered
backprojection algorithm for the first layer, followed by the U-net
architecture for the remaining layers. Actual image reconstruction with deep
learning consists in one evaluation of the trained CNN, which does not require
time consuming solution of the forward and adjoint problems. At the same time,
our numerical results demonstrate that the proposed deep learning approach
reconstructs images with a quality comparable to state of the art iterative
approaches for PAT from sparse data.
| Stephan Antholzer, Markus Haltmeier, and Johannes Schwab | null | 1704.04587 | null | null |
RACE: Large-scale ReAding Comprehension Dataset From Examinations | cs.CL cs.AI cs.LG | We present RACE, a new dataset for benchmark evaluation of methods in the
reading comprehension task. Collected from the English exams for middle and
high school Chinese students in the age range between 12 to 18, RACE consists
of near 28,000 passages and near 100,000 questions generated by human experts
(English instructors), and covers a variety of topics which are carefully
designed for evaluating the students' ability in understanding and reasoning.
In particular, the proportion of questions that requires reasoning is much
larger in RACE than that in other benchmark datasets for reading comprehension,
and there is a significant gap between the performance of the state-of-the-art
models (43%) and the ceiling human performance (95%). We hope this new dataset
can serve as a valuable resource for research and evaluation in machine
comprehension. The dataset is freely available at
http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at
https://github.com/qizhex/RACE_AR_baselines.
| Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, Eduard Hovy | null | 1704.04683 | null | null |
Machine Learning and the Future of Realism | stat.ML cs.LG | The preceding three decades have seen the emergence, rise, and proliferation
of machine learning (ML). From half-recognised beginnings in perceptrons,
neural nets, and decision trees, algorithms that extract correlations (that is,
patterns) from a set of data points have broken free from their origin in
computational cognition to embrace all forms of problem solving, from voice
recognition to medical diagnosis to automated scientific research and
driverless cars, and it is now widely opined that the real industrial
revolution lies less in mobile phone and similar than in the maturation and
universal application of ML. Among the consequences just might be the triumph
of anti-realism over realism.
| Giles Hooker and Cliff Hooker | null | 1704.04688 | null | null |
Deep Learning Based Regression and Multi-class Models for Acute Oral
Toxicity Prediction with Automatic Chemical Feature Extraction | stat.ML cs.LG q-bio.QM | For quantitative structure-property relationship (QSPR) studies in
chemoinformatics, it is important to get interpretable relationship between
chemical properties and chemical features. However, the predictive power and
interpretability of QSPR models are usually two different objectives that are
difficult to achieve simultaneously. A deep learning architecture using
molecular graph encoding convolutional neural networks (MGE-CNN) provided a
universal strategy to construct interpretable QSPR models with high predictive
power. Instead of using application-specific preset molecular descriptors or
fingerprints, the models can be resolved using raw and pertinent features
without manual intervention or selection. In this study, we developed acute
oral toxicity (AOT) models of compounds using the MGE-CNN architecture as a
case study. Three types of high-level predictive models: regression model
(deepAOT-R), multi-classification model (deepAOT-C) and multi-task model
(deepAOT-CR) for AOT evaluation were constructed. These models highly
outperformed previously reported models. For the two external datasets
containing 1673 (test set I) and 375 (test set II) compounds, the R2 and mean
absolute error (MAE) of deepAOT-R on the test set I were 0.864 and 0.195, and
the prediction accuracy of deepAOT-C was 95.5% and 96.3% on the test set I and
II, respectively. The two external prediction accuracy of deepAOT-CR is 95.0%
and 94.1%, while the R2 and MAE are 0.861 and 0.204 for test set I,
respectively.
| Youjun Xu, Jianfeng Pei, Luhua Lai | null | 1704.04718 | null | null |
In-Datacenter Performance Analysis of a Tensor Processing Unit | cs.AR cs.LG cs.NE | Many architects believe that major improvements in cost-energy-performance
must now come from domain-specific hardware. This paper evaluates a custom
ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since
2015 that accelerates the inference phase of neural networks (NN). The heart of
the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak
throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed
on-chip memory. The TPU's deterministic execution model is a better match to
the 99th-percentile response-time requirement of our NN applications than are
the time-varying optimizations of CPUs and GPUs (caches, out-of-order
execution, multithreading, multiprocessing, prefetching, ...) that help average
throughput more than guaranteed latency. The lack of such features helps
explain why, despite having myriad MACs and a big memory, the TPU is relatively
small and low power. We compare the TPU to a server-class Intel Haswell CPU and
an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters.
Our workload, written in the high-level TensorFlow framework, uses production
NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters'
NN inference demand. Despite low utilization for some applications, the TPU is
on average about 15X - 30X faster than its contemporary GPU or CPU, with
TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the
TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and
200X the CPU.
| Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav
Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers,
Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell,
Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami,
Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug
Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek
Jaworski, Alexander Kaplan, Harshit Khaitan, Andy Koch, Naveen Kumar, Steve
Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle
Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran
Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas
Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt
Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew
Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory
Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter,
Walter Wang, Eric Wilcox, and Doe Hyun Yoon | null | 1704.0476 | null | null |
Random Walk Sampling for Big Data over Networks | stat.ML cs.LG | It has been shown recently that graph signals with small total variation can
be accurately recovered from only few samples if the sampling set satisfies a
certain condition, referred to as the network nullspace property. Based on this
recovery condition, we propose a sampling strategy for smooth graph signals
based on random walks. Numerical experiments demonstrate the effectiveness of
this approach for graph signals obtained from a synthetic random graph model as
well as a real-world dataset.
| Saeed Basirian and Alexander Jung | null | 1704.04799 | null | null |
A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular
Communications | cs.ET cs.AI cs.IT cs.LG math.IT | This work presents a new multi-chemical experimental platform for molecular
communication where the transmitter can release different chemicals. This
platform is designed to be inexpensive and accessible, and it can be expanded
to simulate different environments including the cardiovascular system and
complex network of pipes in industrial complexes and city infrastructures. To
demonstrate the capabilities of the platform, we implement a time-slotted
binary communication system where a bit-0 is represented by an acid pulse, a
bit-1 by a base pulse, and information is carried via pH signals. The channel
model for this system, which is nonlinear and has long memories, is unknown.
Therefore, we devise novel detection algorithms that use techniques from
machine learning and deep learning to train a maximum-likelihood detector.
Using these algorithms the bit error rate improves by an order of magnitude
relative to the approach used in previous works. Moreover, our system achieves
a data rate that is an order of magnitude higher than any of the previous
molecular communication platforms.
| Nariman Farsad, David Pan, Andrea Goldsmith | null | 1704.0481 | null | null |
Differential Evolution and Bayesian Optimisation for Hyper-Parameter
Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle
Avoidance | cs.NE cs.LG math.OC | The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the
locust that detects looming objects and triggers its escape responses.
Understanding the neural principles and networks that lead to these fast and
robust responses can lead to the design of efficient facilitate obstacle
avoidance strategies in robotic applications. Here we present a neuromorphic
spiking neural network model of the LGMD driven by the output of a neuromorphic
Dynamic Vision Sensor (DVS), which has been optimised to produce robust and
reliable responses in the face of the constraints and variability of its mixed
signal analogue-digital circuits. As this LGMD model has many parameters, we
use the Differential Evolution (DE) algorithm to optimise its parameter space.
We also investigate the use of Self-Adaptive Differential Evolution (SADE)
which has been shown to ameliorate the difficulties of finding appropriate
input parameters for DE. We explore the use of two biological mechanisms:
synaptic plasticity and membrane adaptivity in the LGMD. We apply DE and SADE
to find parameters best suited for an obstacle avoidance system on an unmanned
aerial vehicle (UAV), and show how it outperforms state-of-the-art Bayesian
optimisation used for comparison.
| Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya | 10.1109/TNNLS.2019.2941506 | 1704.04853 | null | null |
Gang of GANs: Generative Adversarial Networks with Maximum Margin
Ranking | cs.CV cs.LG | Traditional generative adversarial networks (GAN) and many of its variants
are trained by minimizing the KL or JS-divergence loss that measures how close
the generated data distribution is from the true data distribution. A recent
advance called the WGAN based on Wasserstein distance can improve on the KL and
JS-divergence based GANs, and alleviate the gradient vanishing, instability,
and mode collapse issues that are common in the GAN training. In this work, we
aim at improving on the WGAN by first generalizing its discriminator loss to a
margin-based one, which leads to a better discriminator, and in turn a better
generator, and then carrying out a progressive training paradigm involving
multiple GANs to contribute to the maximum margin ranking loss so that the GAN
at later stages will improve upon early stages. We call this method Gang of
GANs (GoGAN). We have shown theoretically that the proposed GoGAN can reduce
the gap between the true data distribution and the generated data distribution
by at least half in an optimally trained WGAN. We have also proposed a new way
of measuring GAN quality which is based on image completion tasks. We have
evaluated our method on four visual datasets: CelebA, LSUN Bedroom, CIFAR-10,
and 50K-SSFF, and have seen both visual and quantitative improvement over
baseline WGAN.
| Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides | null | 1704.04865 | null | null |
Effective Warm Start for the Online Actor-Critic Reinforcement Learning
based mHealth Intervention | cs.LG cs.AI | Online reinforcement learning (RL) is increasingly popular for the
personalized mobile health (mHealth) intervention. It is able to personalize
the type and dose of interventions according to user's ongoing statuses and
changing needs. However, at the beginning of online learning, there are usually
too few samples to support the RL updating, which leads to poor performances. A
delay in good performance of the online learning algorithms can be especially
detrimental in the mHealth, where users tend to quickly disengage with the
mHealth app. To address this problem, we propose a new online RL methodology
that focuses on an effective warm start. The main idea is to make full use of
the data accumulated and the decision rule achieved in a former study. As a
result, we can greatly enrich the data size at the beginning of online learning
in our method. Such case accelerates the online learning process for new users
to achieve good performances not only at the beginning of online learning but
also through the whole online learning process. Besides, we use the decision
rules achieved in a previous study to initialize the parameter in our online RL
model for new users. It provides a good initialization for the proposed online
RL algorithm. Experiment results show that promising improvements have been
achieved by our method compared with the state-of-the-art method.
| Feiyun Zhu and Peng Liao | null | 1704.04866 | null | null |
Deep Relaxation: partial differential equations for optimizing deep
neural networks | cs.LG math.AP math.OC | In this paper we establish a connection between non-convex optimization
methods for training deep neural networks and nonlinear partial differential
equations (PDEs). Relaxation techniques arising in statistical physics which
have already been used successfully in this context are reinterpreted as
solutions of a viscous Hamilton-Jacobi PDE. Using a stochastic control
interpretation allows we prove that the modified algorithm performs better in
expectation that stochastic gradient descent. Well-known PDE regularity results
allow us to analyze the geometry of the relaxed energy landscape, confirming
empirical evidence. The PDE is derived from a stochastic homogenization
problem, which arises in the implementation of the algorithm. The algorithms
scale well in practice and can effectively tackle the high dimensionality of
modern neural networks.
| Pratik Chaudhari, Adam Oberman, Stanley Osher, Stefano Soatto,
Guillaume Carlier | null | 1704.04932 | null | null |
Introspection: Accelerating Neural Network Training By Learning Weight
Evolution | cs.LG | Neural Networks are function approximators that have achieved
state-of-the-art accuracy in numerous machine learning tasks. In spite of their
great success in terms of accuracy, their large training time makes it
difficult to use them for various tasks. In this paper, we explore the idea of
learning weight evolution pattern from a simple network for accelerating
training of novel neural networks. We use a neural network to learn the
training pattern from MNIST classification and utilize it to accelerate
training of neural networks used for CIFAR-10 and ImageNet classification. Our
method has a low memory footprint and is computationally efficient. This method
can also be used with other optimizers to give faster convergence. The results
indicate a general trend in the weight evolution during training of neural
networks.
| Abhishek Sinha, Mausoom Sarkar, Aahitagni Mukherjee, Balaji
Krishnamurthy | null | 1704.04959 | null | null |
Adversarial and Clean Data Are Not Twins | cs.LG cs.NE | Adversarial attack has cast a shadow on the massive success of deep neural
networks. Despite being almost visually identical to the clean data, the
adversarial images can fool deep neural networks into wrong predictions with
very high confidence. In this paper, however, we show that we can build a
simple binary classifier separating the adversarial apart from the clean data
with accuracy over 99%. We also empirically show that the binary classifier is
robust to a second-round adversarial attack. In other words, it is difficult to
disguise adversarial samples to bypass the binary classifier. Further more, we
empirically investigate the generalization limitation which lingers on all
current defensive methods, including the binary classifier approach. And we
hypothesize that this is the result of intrinsic property of adversarial
crafting algorithms.
| Zhitao Gong, Wenlu Wang, Wei-Shinn Ku | null | 1704.0496 | null | null |
Bayesian Hybrid Matrix Factorisation for Data Integration | stat.ML cs.LG | We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for
data integration, based on combining multiple matrix factorisation methods,
that can be used for in- and out-of-matrix prediction of missing values. The
model is very general and can be used to integrate many datasets across
different entity types, including repeated experiments, similarity matrices,
and very sparse datasets. We apply our method on two biological applications,
and extensively compare it to state-of-the-art machine learning and matrix
factorisation models. For in-matrix predictions on drug sensitivity datasets we
obtain consistently better performances than existing methods. This is
especially the case when we increase the sparsity of the datasets. Furthermore,
we perform out-of-matrix predictions on methylation and gene expression
datasets, and obtain the best results on two of the three datasets, especially
when the predictivity of datasets is high.
| Thomas Brouwer, Pietro Li\'o | null | 1704.04962 | null | null |
Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic
Optimization with Progressive Variance Reduction | cs.LG cs.AI math.OC stat.ML | In this paper, we propose a simple variant of the original stochastic
variance reduction gradient (SVRG), where hereafter we refer to as the variance
reduced stochastic gradient descent (VR-SGD). Different from the choices of the
snapshot point and starting point in SVRG and its proximal variant, Prox-SVRG,
the two vectors of each epoch in VR-SGD are set to the average and last iterate
of the previous epoch, respectively. This setting allows us to use much larger
learning rates or step sizes than SVRG, e.g., 3/(7L) for VR-SGD vs 1/(10L) for
SVRG, and also makes our convergence analysis more challenging. In fact, a
larger learning rate enjoyed by VR-SGD means that the variance of its
stochastic gradient estimator asymptotically approaches zero more rapidly.
Unlike common stochastic methods such as SVRG and proximal stochastic methods
such as Prox-SVRG, we design two different update rules for smooth and
non-smooth objective functions, respectively. In other words, VR-SGD can tackle
non-smooth and/or non-strongly convex problems directly without using any
reduction techniques such as quadratic regularizers. Moreover, we analyze the
convergence properties of VR-SGD for strongly convex problems, which show that
VR-SGD attains a linear convergence rate. We also provide the convergence
guarantees of VR-SGD for non-strongly convex problems. Experimental results
show that the performance of VR-SGD is significantly better than its
counterparts, SVRG and Prox-SVRG, and it is also much better than the best
known stochastic method, Katyusha.
| Fanhua Shang | null | 1704.04966 | null | null |
Multimodal Prediction and Personalization of Photo Edits with Deep
Generative Models | stat.ML cs.LG | Professional-grade software applications are powerful but
complicated$-$expert users can achieve impressive results, but novices often
struggle to complete even basic tasks. Photo editing is a prime example: after
loading a photo, the user is confronted with an array of cryptic sliders like
"clarity", "temp", and "highlights". An automatically generated suggestion
could help, but there is no single "correct" edit for a given image$-$different
experts may make very different aesthetic decisions when faced with the same
image, and a single expert may make different choices depending on the intended
use of the image (or on a whim). We therefore want a system that can propose
multiple diverse, high-quality edits while also learning from and adapting to a
user's aesthetic preferences. In this work, we develop a statistical model that
meets these objectives. Our model builds on recent advances in neural network
generative modeling and scalable inference, and uses hierarchical structure to
learn editing patterns across many diverse users. Empirically, we find that our
model outperforms other approaches on this challenging multimodal prediction
task.
| Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma
Ghandeharioun, Matthew J. Johnson, Ryan P. Adams | null | 1704.04997 | null | null |
Sparse Communication for Distributed Gradient Descent | cs.CL cs.DC cs.LG | We make distributed stochastic gradient descent faster by exchanging sparse
updates instead of dense updates. Gradient updates are positively skewed as
most updates are near zero, so we map the 99% smallest updates (by absolute
value) to zero then exchange sparse matrices. This method can be combined with
quantization to further improve the compression. We explore different
configurations and apply them to neural machine translation and MNIST image
classification tasks. Most configurations work on MNIST, whereas different
configurations reduce convergence rate on the more complex translation task.
Our experiments show that we can achieve up to 49% speed up on MNIST and 22% on
NMT without damaging the final accuracy or BLEU.
| Alham Fikri Aji and Kenneth Heafield | 10.18653/v1/D17-1045 | 1704.05021 | null | null |
Fast multi-output relevance vector regression | cs.LG stat.ML | This paper aims to decrease the time complexity of multi-output relevance
vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output
dimensions, M is the number of basis functions, and V<M. The experimental
results demonstrate that the proposed method is more competitive than the
existing method, with regard to computation time. MATLAB codes are available at
http://www.mathworks.com/matlabcentral/fileexchange/49131.
| Youngmin Ha | null | 1704.05041 | null | null |
Google's Cloud Vision API Is Not Robust To Noise | cs.CV cs.LG | Google has recently introduced the Cloud Vision API for image analysis.
According to the demonstration website, the API "quickly classifies images into
thousands of categories, detects individual objects and faces within images,
and finds and reads printed words contained within images." It can be also used
to "detect different types of inappropriate content from adult to violent
content."
In this paper, we evaluate the robustness of Google Cloud Vision API to input
perturbation. In particular, we show that by adding sufficient noise to the
image, the API generates completely different outputs for the noisy image,
while a human observer would perceive its original content. We show that the
attack is consistently successful, by performing extensive experiments on
different image types, including natural images, images containing faces and
images with texts. For instance, using images from ImageNet dataset, we found
that adding an average of 14.25% impulse noise is enough to deceive the API.
Our findings indicate the vulnerability of the API in adversarial environments.
For example, an adversary can bypass an image filtering system by adding noise
to inappropriate images. We then show that when a noise filter is applied on
input images, the API generates mostly the same outputs for restored images as
for original images. This observation suggests that cloud vision API can
readily benefit from noise filtering, without the need for updating image
analysis algorithms.
| Hossein Hosseini, Baicen Xiao and Radha Poovendran | null | 1704.05051 | null | null |
Exploring Sparsity in Recurrent Neural Networks | cs.LG cs.CL | Recurrent Neural Networks (RNN) are widely used to solve a variety of
problems and as the quantity of data and the amount of available compute have
increased, so have model sizes. The number of parameters in recent
state-of-the-art networks makes them hard to deploy, especially on mobile
phones and embedded devices. The challenge is due to both the size of the model
and the time it takes to evaluate it. In order to deploy these RNNs
efficiently, we propose a technique to reduce the parameters of a network by
pruning weights during the initial training of the network. At the end of
training, the parameters of the network are sparse while accuracy is still
close to the original dense neural network. The network size is reduced by 8x
and the time required to train the model remains constant. Additionally, we can
prune a larger dense network to achieve better than baseline performance while
still reducing the total number of parameters significantly. Pruning RNNs
reduces the size of the model and can also help achieve significant inference
time speed-up using sparse matrix multiply. Benchmarks show that using our
technique model size can be reduced by 90% and speed-up is around 2x to 7x.
| Sharan Narang, Erich Elsen, Gregory Diamos, Shubho Sengupta | null | 1704.05119 | null | null |
Does robustness imply tractability? A lower bound for planted clique in
the semi-random model | cs.CC cs.IT cs.LG math.IT math.ST stat.TH | We consider a robust analog of the planted clique problem. In this analog, a
set $S$ of vertices is chosen and all edges in $S$ are included; then, edges
between $S$ and the rest of the graph are included with probability
$\frac{1}{2}$, while edges not touching $S$ are allowed to vary arbitrarily.
For this semi-random model, we show that the information-theoretic threshold
for recovery is $\tilde{\Theta}(\sqrt{n})$, in sharp contrast to the classical
information-theoretic threshold of $\Theta(\log(n))$. This matches the
conjectured computational threshold for the classical planted clique problem,
and thus raises the intriguing possibility that, once we require robustness,
there is no computational-statistical gap for planted clique. Our lower bound
involves establishing a result regarding the KL divergence of a family of
perturbed Bernoulli distributions, which may be of independent interest.
| Jacob Steinhardt | null | 1704.0512 | null | null |
Does Neural Machine Translation Benefit from Larger Context? | stat.ML cs.CL cs.LG | We propose a neural machine translation architecture that models the
surrounding text in addition to the source sentence. These models lead to
better performance, both in terms of general translation quality and pronoun
prediction, when trained on small corpora, although this improvement largely
disappears when trained with a larger corpus. We also discover that
attention-based neural machine translation is well suited for pronoun
prediction and compares favorably with other approaches that were specifically
designed for this task.
| Sebastien Jean, Stanislas Lauly, Orhan Firat, Kyunghyun Cho | null | 1704.05135 | null | null |
O$^2$TD: (Near)-Optimal Off-Policy TD Learning | cs.LG stat.ML | Temporal difference learning and Residual Gradient methods are the most
widely used temporal difference based learning algorithms; however, it has been
shown that none of their objective functions is optimal w.r.t approximating the
true value function $V$. Two novel algorithms are proposed to approximate the
true value function $V$. This paper makes the following contributions: (1) A
batch algorithm that can help find the approximate optimal off-policy
prediction of the true value function $V$. (2) A linear computational cost (per
step) near-optimal algorithm that can learn from a collection of off-policy
samples. (3) A new perspective of the emphatic temporal difference learning
which bridges the gap between off-policy optimality and off-policy stability.
| Bo Liu, Daoming Lyu, Wen Dong, Saad Biaz | null | 1704.05147 | null | null |
VAE Learning via Stein Variational Gradient Descent | cs.LG | A new method for learning variational autoencoders (VAEs) is developed, based
on Stein variational gradient descent. A key advantage of this approach is that
one need not make parametric assumptions about the form of the encoder
distribution. Performance is further enhanced by integrating the proposed
encoder with importance sampling. Excellent performance is demonstrated across
multiple unsupervised and semi-supervised problems, including semi-supervised
analysis of the ImageNet data, demonstrating the scalability of the model to
large datasets.
| Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence
Carin | null | 1704.05155 | null | null |
Learning Piece-wise Linear Models from Large Scale Data for Ad Click
Prediction | stat.ML cs.LG | CTR prediction in real-world business is a difficult machine learning problem
with large scale nonlinear sparse data. In this paper, we introduce an
industrial strength solution with model named Large Scale Piece-wise Linear
Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$
regularizers, leading to a non-convex and non-smooth optimization problem.
Then, we propose a novel algorithm to solve it efficiently, based on
directional derivatives and quasi-Newton method. In addition, we design a
distributed system which can run on hundreds of machines parallel and provides
us with the industrial scalability. LS-PLM model can capture nonlinear patterns
from massive sparse data, saving us from heavy feature engineering jobs. Since
2012, LS-PLM has become the main CTR prediction model in Alibaba's online
display advertising system, serving hundreds of millions users every day.
| Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, Zhe Wang | null | 1704.05194 | null | null |
HPSLPred: An Ensemble Multi-label Classifier for Human Protein
Subcellular Location Prediction with Imbalanced Source | cs.LG | Predicting the subcellular localization of proteins is an important and
challenging problem. Traditional experimental approaches are often expensive
and time-consuming. Consequently, a growing number of research efforts employ a
series of machine learning approaches to predict the subcellular location of
proteins. There are two main challenges among the state-of-the-art prediction
methods. First, most of the existing techniques are designed to deal with
multi-class rather than multi-label classification, which ignores connections
between multiple labels. In reality, multiple locations of particular proteins
implies that there are vital and unique biological significances that deserve
special focus and cannot be ignored. Second, techniques for handling imbalanced
data in multi-label classification problems are necessary, but never employed.
For solving these two issues, we have developed an ensemble multi-label
classifier called HPSLPred, which can be applied for multi-label classification
with an imbalanced protein source. For convenience, a user-friendly webserver
has been established at http://server.malab.cn/HPSLPred.
| Shixiang Wan and Quan Zou | null | 1704.05204 | null | null |
Know Your Master: Driver Profiling-based Anti-theft Method | cs.CR cs.LG | Although many anti-theft technologies are implemented, auto-theft is still
increasing. Also, security vulnerabilities of cars can be used for auto-theft
by neutralizing anti-theft system. This keyless auto-theft attack will be
increased as cars adopt computerized electronic devices more. To detect
auto-theft efficiently, we propose the driver verification method that analyzes
driving patterns using measurements from the sensor in the vehicle. In our
model, we add mechanical features of automotive parts that are excluded in
previous works, but can be differentiated by drivers' driving behaviors. We
design the model that uses significant features through feature selection to
reduce the time cost of feature processing and improve the detection
performance. Further, we enrich the feature set by deriving statistical
features such as mean, median, and standard deviation. This minimizes the
effect of fluctuation of feature values per driver and finally generates the
reliable model. We also analyze the effect of the size of sliding window on
performance to detect the time point when the detection becomes reliable and to
inform owners the theft event as soon as possible. We apply our model with real
driving and show the contribution of our work to the literature of driver
identification.
| Byung Il Kwak, JiYoung Woo and Huy Kang Kim | null | 1704.05223 | null | null |
Hot or not? Forecasting cellular network hot spots using sector
performance indicators | cs.LG cs.NI cs.SY | To manage and maintain large-scale cellular networks, operators need to know
which sectors underperform at any given time. For this purpose, they use the
so-called hot spot score, which is the result of a combination of multiple
network measurements and reflects the instantaneous overall performance of
individual sectors. While operators have a good understanding of the current
performance of a network and its overall trend, forecasting the performance of
each sector over time is a challenging task, as it is affected by both regular
and non-regular events, triggered by human behavior and hardware failures. In
this paper, we study the spatio-temporal patterns of the hot spot score and
uncover its regularities. Based on our observations, we then explore the
possibility to use recent measurements' history to predict future hot spots. To
this end, we consider tree-based machine learning models, and study their
performance as a function of time, amount of past data, and prediction horizon.
Our results indicate that, compared to the best baseline, tree-based models can
deliver up to 14% better forecasts for regular hot spots and 153% better
forecasts for non-regular hot spots. The latter brings strong evidence that,
for moderate horizons, forecasts can be made even for sectors exhibiting
isolated, non-regular behavior. Overall, our work provides insight into the
dynamics of cellular sectors and their predictability. It also paves the way
for more proactive network operations with greater forecasting horizons.
| Joan Serr\`a, Ilias Leontiadis, Alexandros Karatzoglou, Konstantina
Papagiannaki | null | 1704.05249 | null | null |
Large-Scale Online Semantic Indexing of Biomedical Articles via an
Ensemble of Multi-Label Classification Models | stat.ML cs.LG | Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts.
| Yannis Papanikolaou, Grigorios Tsoumakas, Manos Laliotis, Nikos
Markantonatos and Ioannis Vlahavas | null | 1704.05271 | null | null |
Unsupervised Learning by Predicting Noise | stat.ML cs.CV cs.LG | Convolutional neural networks provide visual features that perform remarkably
well in many computer vision applications. However, training these networks
requires significant amounts of supervision. This paper introduces a generic
framework to train deep networks, end-to-end, with no supervision. We propose
to fix a set of target representations, called Noise As Targets (NAT), and to
constrain the deep features to align to them. This domain agnostic approach
avoids the standard unsupervised learning issues of trivial solutions and
collapsing of features. Thanks to a stochastic batch reassignment strategy and
a separable square loss function, it scales to millions of images. The proposed
approach produces representations that perform on par with state-of-the-art
unsupervised methods on ImageNet and Pascal VOC.
| Piotr Bojanowski, Armand Joulin | null | 1704.0531 | null | null |
A Study of Deep Learning Robustness Against Computation Failures | cs.NE cs.LG | For many types of integrated circuits, accepting larger failure rates in
computations can be used to improve energy efficiency. We study the performance
of faulty implementations of certain deep neural networks based on pessimistic
and optimistic models of the effect of hardware faults. After identifying the
impact of hyperparameters such as the number of layers on robustness, we study
the ability of the network to compensate for computational failures through an
increase of the network size. We show that some networks can achieve equivalent
performance under faulty implementations, and quantify the required increase in
computational complexity.
| Jean-Charles Vialatte and Fran\c{c}ois Leduc-Primeau | null | 1704.05396 | null | null |
Ranking to Learn: Feature Ranking and Selection via Eigenvector
Centrality | cs.CV cs.LG stat.ML | In an era where accumulating data is easy and storing it inexpensive, feature
selection plays a central role in helping to reduce the high-dimensionality of
huge amounts of otherwise meaningless data. In this paper, we propose a
graph-based method for feature selection that ranks features by identifying the
most important ones into arbitrary set of cues. Mapping the problem on an
affinity graph-where features are the nodes-the solution is given by assessing
the importance of nodes through some indicators of centrality, in particular,
the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance
of a feature as a function of the importance of its neighbors. Ranking central
nodes individuates candidate features, which turn out to be effective from a
classification point of view, as proved by a thoroughly experimental section.
Our approach has been tested on 7 diverse datasets from recent literature
(e.g., biological data and object recognition, among others), and compared
against filter, embedded and wrappers methods. The results are remarkable in
terms of accuracy, stability and low execution time.
| Giorgio Roffo and Simone Melzi | null | 1704.05409 | null | null |
Diagonal RNNs in Symbolic Music Modeling | cs.NE cs.LG stat.ML | In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
The novelty is simple: We use diagonal recurrent matrices instead of full. This
results in better test likelihood and faster convergence compared to regular
full RNNs in most of our experiments. We show the benefits of using diagonal
recurrent matrices with popularly used LSTM and GRU architectures as well as
with the vanilla RNN architecture, on four standard symbolic music datasets.
| Y. Cem Subakan, Paris Smaragdis | null | 1704.0542 | null | null |
Investigating Recurrence and Eligibility Traces in Deep Q-Networks | cs.AI cs.LG | Eligibility traces in reinforcement learning are used as a bias-variance
trade-off and can often speed up training time by propagating knowledge back
over time-steps in a single update. We investigate the use of eligibility
traces in combination with recurrent networks in the Atari domain. We
illustrate the benefits of both recurrent nets and eligibility traces in some
Atari games, and highlight also the importance of the optimization used in the
training.
| Jean Harb and Doina Precup | null | 1704.05495 | null | null |
Simultaneous Policy Learning and Latent State Inference for Imitating
Driver Behavior | cs.LG cs.AI stat.ML | In this work, we propose a method for learning driver models that account for
variables that cannot be observed directly. When trained on a synthetic
dataset, our models are able to learn encodings for vehicle trajectories that
distinguish between four distinct classes of driver behavior. Such encodings
are learned without any knowledge of the number of driver classes or any
objective that directly requires the models to learn encodings for each class.
We show that driving policies trained with knowledge of latent variables are
more effective than baseline methods at imitating the driver behavior that they
are trained to replicate. Furthermore, we demonstrate that the actions chosen
by our policy are heavily influenced by the latent variable settings that are
provided to them.
| Jeremy Morton and Mykel J. Kochenderfer | null | 1704.05566 | null | null |
A Large Self-Annotated Corpus for Sarcasm | cs.CL cs.AI cs.LG | We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
sarcasm research and for training and evaluating systems for sarcasm detection.
The corpus has 1.3 million sarcastic statements -- 10 times more than any
previous dataset -- and many times more instances of non-sarcastic statements,
allowing for learning in both balanced and unbalanced label regimes. Each
statement is furthermore self-annotated -- sarcasm is labeled by the author,
not an independent annotator -- and provided with user, topic, and conversation
context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm
detection, and evaluate baseline methods.
| Mikhail Khodak, Nikunj Saunshi and Kiran Vodrahalli | null | 1704.05579 | null | null |
Learning to Fly by Crashing | cs.RO cs.CV cs.LG | How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKUo
| Dhiraj Gandhi, Lerrel Pinto, Abhinav Gupta | null | 1704.05588 | null | null |
Insensitive Stochastic Gradient Twin Support Vector Machine for Large
Scale Problems | cs.LG cs.CV | Stochastic gradient descent algorithm has been successfully applied on
support vector machines (called PEGASOS) for many classification problems. In
this paper, stochastic gradient descent algorithm is investigated to twin
support vector machines for classification. Compared with PEGASOS, the proposed
stochastic gradient twin support vector machines (SGTSVM) is insensitive on
stochastic sampling for stochastic gradient descent algorithm. In theory, we
prove the convergence of SGTSVM instead of almost sure convergence of PEGASOS.
For uniformly sampling, the approximation between SGTSVM and twin support
vector machines is also given, while PEGASOS only has an opportunity to obtain
an approximation of support vector machines. In addition, the nonlinear SGTSVM
is derived directly from its linear case. Experimental results on both
artificial datasets and large scale problems show the stable performance of
SGTSVM with a fast learning speed.
| Zhen Wang, Yuan-Hai Shao, Lan Bai, Li-Ming Liu, Nai-Yang Deng | 10.1016/j.ins.2018.06.007 | 1704.05596 | null | null |
Effects of the optimisation of the margin distribution on generalisation
in deep architectures | cs.LG | Despite being so vital to success of Support Vector Machines, the principle
of separating margin maximisation is not used in deep learning. We show that
minimisation of margin variance and not maximisation of the margin is more
suitable for improving generalisation in deep architectures. We propose the
Halfway loss function that minimises the Normalised Margin Variance (NMV) at
the output of a deep learning models and evaluate its performance against the
Softmax Cross-Entropy loss on the MNIST, smallNORB and CIFAR-10 datasets.
| Lech Szymanski, Brendan McCane, Wei Gao, Zhi-Hua Zhou | null | 1704.05646 | null | null |
CNN based music emotion classification | cs.MM cs.LG | Music emotion recognition (MER) is usually regarded as a multi-label tagging
task, and each segment of music can inspire specific emotion tags. Most
researchers extract acoustic features from music and explore the relations
between these features and their corresponding emotion tags. Considering the
inconsistency of emotions inspired by the same music segment for human beings,
seeking for the key acoustic features that really affect on emotions is really
a challenging task. In this paper, we propose a novel MER method by using deep
convolutional neural network (CNN) on the music spectrograms that contains both
the original time and frequency domain information. By the proposed method, no
additional effort on extracting specific features required, which is left to
the training procedure of the CNN model. Experiments are conducted on the
standard CAL500 and CAL500exp dataset. Results show that, for both datasets,
the proposed method outperforms state-of-the-art methods.
| Xin Liu, Qingcai Chen, Xiangping Wu, Yan Liu, Yang Liu | null | 1704.05665 | null | null |
Unsupervised Creation of Parameterized Avatars | cs.CV cs.LG | We study the problem of mapping an input image to a tied pair consisting of a
vector of parameters and an image that is created using a graphical engine from
the vector of parameters. The mapping's objective is to have the output image
as similar as possible to the input image. During training, no supervision is
given in the form of matching inputs and outputs.
This learning problem extends two literature problems: unsupervised domain
adaptation and cross domain transfer. We define a generalization bound that is
based on discrepancy, and employ a GAN to implement a network solution that
corresponds to this bound. Experimentally, our method is shown to solve the
problem of automatically creating avatars.
| Lior Wolf, Yaniv Taigman, Adam Polyak | null | 1704.05693 | null | null |
A Deep Learning Framework using Passive WiFi Sensing for Respiration
Monitoring | cs.CV cs.LG | This paper presents an end-to-end deep learning framework using passive WiFi
sensing to classify and estimate human respiration activity. A passive radar
test-bed is used with two channels where the first channel provides the
reference WiFi signal, whereas the other channel provides a surveillance signal
that contains reflections from the human target. Adaptive filtering is
performed to make the surveillance signal source-data invariant by eliminating
the echoes of the direct transmitted signal. We propose a novel convolutional
neural network to classify the complex time series data and determine if it
corresponds to a breathing activity, followed by a random forest estimator to
determine breathing rate. We collect an extensive dataset to train the learning
models and develop reference benchmarks for the future studies in the field.
Based on the results, we conclude that deep learning techniques coupled with
passive radars offer great potential for end-to-end human activity recognition.
| U. M. Khan, Z. Kabir, S. A. Hassan, S. H. Ahmed | null | 1704.05708 | null | null |
Universal Adversarial Perturbations Against Semantic Image Segmentation | stat.ML cs.AI cs.CV cs.LG cs.NE | While deep learning is remarkably successful on perceptual tasks, it was also
shown to be vulnerable to adversarial perturbations of the input. These
perturbations denote noise added to the input that was generated specifically
to fool the system while being quasi-imperceptible for humans. More severely,
there even exist universal perturbations that are input-agnostic but fool the
network on the majority of inputs. While recent work has focused on image
classification, this work proposes attacks against semantic image segmentation:
we present an approach for generating (universal) adversarial perturbations
that make the network yield a desired target segmentation as output. We show
empirically that there exist barely perceptible universal noise patterns which
result in nearly the same predicted segmentation for arbitrary inputs.
Furthermore, we also show the existence of universal noise which removes a
target class (e.g., all pedestrians) from the segmentation while leaving the
segmentation mostly unchanged otherwise.
| Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, Volker
Fischer | null | 1704.05712 | null | null |
Maximum Likelihood Estimation based on Random Subspace EDA: Application
to Extrasolar Planet Detection | stat.ME astro-ph.IM cs.LG cs.NE | This paper addresses maximum likelihood (ML) estimation based model fitting
in the context of extrasolar planet detection. This problem is featured by the
following properties: 1) the candidate models under consideration are highly
nonlinear; 2) the likelihood surface has a huge number of peaks; 3) the
parameter space ranges in size from a few to dozens of dimensions. These
properties make the ML search a very challenging problem, as it lacks any
analytical or gradient based searching solution to explore the parameter space.
A population based searching method, called estimation of distribution
algorithm (EDA), is adopted to explore the model parameter space starting from
a batch of random locations. EDA is featured by its ability to reveal and
utilize problem structures. This property is desirable for characterizing the
detections. However, it is well recognized that EDAs can not scale well to
large scale problems, as it consists of iterative random sampling and model
fitting procedures, which results in the well-known dilemma curse of
dimensionality. A novel mechanism to perform EDAs in interactive random
subspaces spanned by correlated variables is proposed and the hope is to
alleviate the curse of dimensionality for EDAs by performing the operations of
sampling and model fitting in lower dimensional subspaces. The effectiveness of
the proposed algorithm is verified via both benchmark numerical studies and
real data analysis.
| Bin Liu, Ke-Jia Chen | null | 1704.05761 | null | null |
End-to-End Multi-View Networks for Text Classification | cs.CL cs.LG cs.NE | We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks.
| Hongyu Guo and Colin Cherry and Jiang Su | null | 1704.05907 | null | null |
An Interpretable Knowledge Transfer Model for Knowledge Base Completion | cs.CL cs.AI cs.LG | Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information.
| Qizhe Xie, Xuezhe Ma, Zihang Dai, Eduard Hovy | null | 1704.05908 | null | null |
Semi-supervised classification for dynamic Android malware detection | cs.CR cs.LG stat.ML | A growing number of threats to Android phones creates challenges for malware
detection. Manually labeling the samples into benign or different malicious
families requires tremendous human efforts, while it is comparably easy and
cheap to obtain a large amount of unlabeled APKs from various sources.
Moreover, the fast-paced evolution of Android malware continuously generates
derivative malware families. These families often contain new signatures, which
can escape detection when using static analysis. These practical challenges can
also cause traditional supervised machine learning algorithms to degrade in
performance.
In this paper, we propose a framework that uses model-based semi-supervised
(MBSS) classification scheme on the dynamic Android API call logs. The
semi-supervised approach efficiently uses the labeled and unlabeled APKs to
estimate a finite mixture model of Gaussian distributions via conditional
expectation-maximization and efficiently detects malwares during out-of-sample
testing. We compare MBSS with the popular malware detection classifiers such as
support vector machine (SVM), $k$-nearest neighbor (kNN) and linear
discriminant analysis (LDA). Under the ideal classification setting, MBSS has
competitive performance with 98\% accuracy and very low false positive rate for
in-sample classification. For out-of-sample testing, the out-of-sample test
data exhibit similar behavior of retrieving phone information and sending to
the network, compared with in-sample training set. When this similarity is
strong, MBSS and SVM with linear kernel maintain 90\% detection rate while
$k$NN and LDA suffer great performance degradation. When this similarity is
slightly weaker, all classifiers degrade in performance, but MBSS still
performs significantly better than other classifiers.
| Li Chen, Mingwei Zhang, Chih-Yuan Yang, Ravi Sahita | null | 1704.05948 | null | null |
SAFS: A Deep Feature Selection Approach for Precision Medicine | cs.LG stat.ML | In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others.
| Milad Zafar Nezhad, Dongxiao Zhu, Xiangrui Li, Kai Yang, Phillip Levy | 10.1109/BIBM.2016.7822569 | 1704.0596 | null | null |
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds | math.OC cs.AI cs.LG | Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
intelligence (e.g., the game of Go), is a well-known strategy for constructing
approximate solutions to sequential decision problems. Its primary innovation
is the use of a heuristic, known as a default policy, to obtain Monte Carlo
estimates of downstream values for states in a decision tree. This information
is used to iteratively expand the tree towards regions of states and actions
that an optimal policy might visit. However, to guarantee convergence to the
optimal action, MCTS requires the entire tree to be expanded asymptotically. In
this paper, we propose a new technique called Primal-Dual MCTS that utilizes
sampled information relaxation upper bounds on potential actions, creating the
possibility of "ignoring" parts of the tree that stem from highly suboptimal
choices. This allows us to prove that despite converging to a partial decision
tree in the limit, the recommended action from Primal-Dual MCTS is optimal. The
new approach shows significant promise when used to optimize the behavior of a
single driver navigating a graph while operating on a ride-sharing platform.
Numerical experiments on a real dataset of 7,000 trips in New Jersey suggest
that Primal-Dual MCTS improves upon standard MCTS by producing deeper decision
trees and exhibits a reduced sensitivity to the size of the action space.
| Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell | null | 1704.05963 | null | null |
Retrospective Higher-Order Markov Processes for User Trails | cs.SI cs.LG stat.ML | Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such sequences of data. First-order Markov chains are easy to
estimate, but lack accuracy when history matters. Higher-order Markov chains,
in contrast, have too many parameters and suffer from overfitting the training
data. Fitting these parameters with regularization and smoothing only offers
mild improvements. In this paper we propose the retrospective higher-order
Markov process (RHOMP) as a low-parameter model for such sequences. This model
is a special case of a higher-order Markov chain where the transitions depend
retrospectively on a single history state instead of an arbitrary combination
of history states. There are two immediate computational advantages: the number
of parameters is linear in the order of the Markov chain and the model can be
fit to large state spaces. Furthermore, by providing a specific structure to
the higher-order chain, RHOMPs improve the model accuracy by efficiently
utilizing history states without risks of overfitting the data. We demonstrate
how to estimate a RHOMP from data and we demonstrate the effectiveness of our
method on various real application datasets spanning geolocation data, review
sequences, and business locations. The RHOMP model uniformly outperforms
higher-order Markov chains, Kneser-Ney regularization, and tensor
factorizations in terms of prediction accuracy.
| Tao Wu and David Gleich | null | 1704.05982 | null | null |
Fast Generation for Convolutional Autoregressive Models | cs.LG cs.CV stat.ML | Convolutional autoregressive models have recently demonstrated
state-of-the-art performance on a number of generation tasks. While fast,
parallel training methods have been crucial for their success, generation is
typically implemented in a na\"{i}ve fashion where redundant computations are
unnecessarily repeated. This results in slow generation, making such models
infeasible for production environments. In this work, we describe a method to
speed up generation in convolutional autoregressive models. The key idea is to
cache hidden states to avoid redundant computation. We apply our fast
generation method to the Wavenet and PixelCNN++ models and achieve up to
$21\times$ and $183\times$ speedups respectively.
| Prajit Ramachandran, Tom Le Paine, Pooya Khorrami, Mohammad
Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H.
Campbell, Thomas S. Huang | null | 1704.06001 | null | null |
End-to-end representation learning for Correlation Filter based tracking | cs.CV cs.LG | The Correlation Filter is an algorithm that trains a linear template to
discriminate between images and their translations. It is well suited to object
tracking because its formulation in the Fourier domain provides a fast
solution, enabling the detector to be re-trained once per frame. Previous works
that use the Correlation Filter, however, have adopted features that were
either manually designed or trained for a different task. This work is the
first to overcome this limitation by interpreting the Correlation Filter
learner, which has a closed-form solution, as a differentiable layer in a deep
neural network. This enables learning deep features that are tightly coupled to
the Correlation Filter. Experiments illustrate that our method has the
important practical benefit of allowing lightweight architectures to achieve
state-of-the-art performance at high framerates.
| Jack Valmadre, Luca Bertinetto, Jo\~ao F. Henriques, Andrea Vedaldi,
Philip H. S. Torr | null | 1704.06036 | null | null |
Multi-view (Joint) Probability Linear Discrimination Analysis for
Multi-view Feature Verification | cs.LG | Multi-view feature has been proved to be very effective in many multimedia
applications. However, the current back-end classifiers cannot make full use of
such features. In this paper, we propose a method to model the multi-faceted
information in the multi-view features explicitly and jointly. In our approach,
the feature was modeled as a result derived by a generative multi-view
(joint\footnotemark[1]) Probability Linear Discriminant Analysis (PLDA) model,
which contains multiple kinds of latent variables. The usual PLDA model only
considers one single label. However, in practical use, when using multi-task
learned network as feature extractor, the extracted feature are always attached
to several labels. This type of feature is called multi-view feature. With
multi-view (joint) PLDA, we are able to explicitly build a model that can
combine multiple heterogeneous information from the multi-view features. In
verification step, we calculated the likelihood to describe whether the two
features having consistent labels or not. This likelihood are used in the
following decision-making. Experiments have been conducted on large scale
verification task. On the public RSR2015 data corpus, the results showed that
our approach can achieve 0.02\% EER and 0.09\% EER for impostor wrong and
impostor correct cases respectively.
| Ziqiang Shi and Liu Liu and Mengjiao Wang and Rujie Liu | null | 1704.06061 | null | null |
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with
the Log Bilinear Loss | cs.LG stat.ML | Deep learning has become the method of choice in many application domains of
machine learning in recent years, especially for multi-class classification
tasks. The most common loss function used in this context is the cross-entropy
loss, which reduces to the log loss in the typical case when there is a single
correct response label. While this loss is insensitive to the identity of the
assigned class in the case of misclassification, in practice it is often the
case that some errors may be more detrimental than others. Here we present the
bilinear-loss (and related log-bilinear-loss) which differentially penalizes
the different wrong assignments of the model. We thoroughly test this method
using standard models and benchmark image datasets. As one application, we show
the ability of this method to better contain error within the correct
super-class, in the hierarchically labeled CIFAR100 dataset, without affecting
the overall performance of the classifier.
| Yehezkel S. Resheff, Amit Mandelbaum, Daphna Weinshall | null | 1704.06062 | null | null |
Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images | cs.AI cs.LG stat.ML | We present a baseline approach for cross-modal knowledge fusion. Different
basic fusion methods are evaluated on existing embedding approaches to show the
potential of joining knowledge about certain concepts across modalities in a
fused concept representation.
| Steffen Thoma, Achim Rettinger, Fabian Both | null | 1704.06084 | null | null |
Learning to Acquire Information | cs.AI cs.LG stat.ML | We consider the problem of diagnosis where a set of simple observations are
used to infer a potentially complex hidden hypothesis. Finding the optimal
subset of observations is intractable in general, thus we focus on the problem
of active diagnosis, where the agent selects the next most-informative
observation based on the results of previous observations. We show that under
the assumption of uniform observation entropy, one can build an implication
model which directly predicts the outcome of the potential next observation
conditioned on the results of past observations, and selects the observation
with the maximum entropy. This approach enjoys reduced computation complexity
by bypassing the complicated hypothesis space, and can be trained on
observation data alone, learning how to query without knowledge of the hidden
hypothesis.
| Yewen Pu, Leslie P Kaelbling, Armando Solar-Lezama | null | 1704.06131 | null | null |
Segmentation of the Proximal Femur from MR Images using Deep
Convolutional Neural Networks | cs.CV cs.LG stat.ML | Magnetic resonance imaging (MRI) has been proposed as a complimentary method
to measure bone quality and assess fracture risk. However, manual segmentation
of MR images of bone is time-consuming, limiting the use of MRI measurements in
the clinical practice. The purpose of this paper is to present an automatic
proximal femur segmentation method that is based on deep convolutional neural
networks (CNNs). This study had institutional review board approval and written
informed consent was obtained from all subjects. A dataset of volumetric
structural MR images of the proximal femur from 86 subject were
manually-segmented by an expert. We performed experiments by training two
different CNN architectures with multiple number of initial feature maps and
layers, and tested their segmentation performance against the gold standard of
manual segmentations using four-fold cross-validation. Automatic segmentation
of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
architecture based on 3D convolution exceeding the performance of 2D CNNs. The
high segmentation accuracy provided by CNNs has the potential to help bring the
use of structural MRI measurements of bone quality into clinical practice for
management of osteoporosis.
| Cem M. Deniz, Siyuan Xiang, Spencer Hallyburton, Arakua Welbeck, James
S. Babb, Stephen Honig, Kyunghyun Cho, and Gregory Chang | 10.1038/s41598-018-34817-6 | 1704.06176 | null | null |
Softmax GAN | cs.LG cs.NE | Softmax GAN is a novel variant of Generative Adversarial Network (GAN). The
key idea of Softmax GAN is to replace the classification loss in the original
GAN with a softmax cross-entropy loss in the sample space of one single batch.
In the adversarial learning of $N$ real training samples and $M$ generated
samples, the target of discriminator training is to distribute all the
probability mass to the real samples, each with probability $\frac{1}{M}$, and
distribute zero probability to generated data. In the generator training phase,
the target is to assign equal probability to all data points in the batch, each
with probability $\frac{1}{M+N}$. While the original GAN is closely related to
Noise Contrastive Estimation (NCE), we show that Softmax GAN is the Importance
Sampling version of GAN. We futher demonstrate with experiments that this
simple change stabilizes GAN training.
| Min Lin | null | 1704.06191 | null | null |
Dynamic Graph Convolutional Networks | cs.LG stat.ML | Many different classification tasks need to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that
the vertices/edges of each graph may change during time. Our goal is to jointly
exploit structured data and temporal information through the use of a neural
network model. To the best of our knowledge, this task has not been addressed
using these kind of architectures. For this reason, we propose two novel
approaches, which combine Long Short-Term Memory networks and Graph
Convolutional Networks to learn long short-term dependencies together with
graph structure. The quality of our methods is confirmed by the promising
results achieved.
| Franco Manessi and Alessandro Rozza and Mario Manzo | 10.1016/j.patcog.2019.107000 | 1704.06199 | null | null |
ADMM Penalty Parameter Selection by Residual Balancing | math.OC cs.LG eess.SP | Appropriate selection of the penalty parameter is crucial to obtaining good
performance from the Alternating Direction Method of Multipliers (ADMM). While
analytic results for optimal selection of this parameter are very limited,
there is a heuristic method that appears to be relatively successful in a
number of different problems. The contribution of this paper is to demonstrate
that their is a potentially serious flaw in this heuristic approach, and to
propose a modification that at least partially addresses it.
| Brendt Wohlberg | null | 1704.06209 | null | null |
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption | stat.ML cs.LG | We consider the robust phase retrieval problem of recovering the unknown
signal from the magnitude-only measurements, where the measurements can be
contaminated by both sparse arbitrary corruption and bounded random noise. We
propose a new nonconvex algorithm for robust phase retrieval, namely Robust
Wirtinger Flow to jointly estimate the unknown signal and the sparse
corruption. We show that our proposed algorithm is guaranteed to converge
linearly to the unknown true signal up to a minimax optimal statistical
precision in such a challenging setting. Compared with existing robust phase
retrieval methods, we achieve an optimal sample complexity of $O(n)$ in both
noisy and noise-free settings. Thorough experiments on both synthetic and real
datasets corroborate our theory.
| Jinghui Chen and Lingxiao Wang and Xiao Zhang and Quanquan Gu | null | 1704.06256 | null | null |
Mutual Information, Neural Networks and the Renormalization Group | cond-mat.dis-nn cond-mat.stat-mech cs.IT cs.LG math.IT stat.ML | Physical systems differring in their microscopic details often display
strikingly similar behaviour when probed at macroscopic scales. Those universal
properties, largely determining their physical characteristics, are revealed by
the powerful renormalization group (RG) procedure, which systematically retains
"slow" degrees of freedom and integrates out the rest. However, the important
degrees of freedom may be difficult to identify. Here we demonstrate a machine
learning algorithm capable of identifying the relevant degrees of freedom and
executing RG steps iteratively without any prior knowledge about the system. We
introduce an artificial neural network based on a model-independent,
information-theoretic characterization of a real-space RG procedure, performing
this task. We apply the algorithm to classical statistical physics problems in
one and two dimensions. We demonstrate RG flow and extract the Ising critical
exponent. Our results demonstrate that machine learning techniques can extract
abstract physical concepts and consequently become an integral part of theory-
and model-building.
| Maciej Koch-Janusz and Zohar Ringel | 10.1038/s41567-018-0081-4 | 1704.06279 | null | null |
Deep Clustering via Joint Convolutional Autoencoder Embedding and
Relative Entropy Minimization | cs.LG | Image clustering is one of the most important computer vision applications,
which has been extensively studied in literature. However, current clustering
methods mostly suffer from lack of efficiency and scalability when dealing with
large-scale and high-dimensional data. In this paper, we propose a new
clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which
efficiently maps data into a discriminative embedding subspace and precisely
predicts cluster assignments. DEPICT generally consists of a multinomial
logistic regression function stacked on top of a multi-layer convolutional
autoencoder. We define a clustering objective function using relative entropy
(KL divergence) minimization, regularized by a prior for the frequency of
cluster assignments. An alternating strategy is then derived to optimize the
objective by updating parameters and estimating cluster assignments.
Furthermore, we employ the reconstruction loss functions in our autoencoder, as
a data-dependent regularization term, to prevent the deep embedding function
from overfitting. In order to benefit from end-to-end optimization and
eliminate the necessity for layer-wise pretraining, we introduce a joint
learning framework to minimize the unified clustering and reconstruction loss
functions together and train all network layers simultaneously. Experimental
results indicate the superiority and faster running time of DEPICT in
real-world clustering tasks, where no labeled data is available for
hyper-parameter tuning.
| Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai,
Heng Huang | null | 1704.06327 | null | null |
Equivalence Between Policy Gradients and Soft Q-Learning | cs.LG | Two of the leading approaches for model-free reinforcement learning are
policy gradient methods and $Q$-learning methods. $Q$-learning methods can be
effective and sample-efficient when they work, however, it is not
well-understood why they work, since empirically, the $Q$-values they estimate
are very inaccurate. A partial explanation may be that $Q$-learning methods are
secretly implementing policy gradient updates: we show that there is a precise
equivalence between $Q$-learning and policy gradient methods in the setting of
entropy-regularized reinforcement learning, that "soft" (entropy-regularized)
$Q$-learning is exactly equivalent to a policy gradient method. We also point
out a connection between $Q$-learning methods and natural policy gradient
methods. Experimentally, we explore the entropy-regularized versions of
$Q$-learning and policy gradients, and we find them to perform as well as (or
slightly better than) the standard variants on the Atari benchmark. We also
show that the equivalence holds in practical settings by constructing a
$Q$-learning method that closely matches the learning dynamics of A3C without
using a target network or $\epsilon$-greedy exploration schedule.
| John Schulman and Xi Chen and Pieter Abbeel | null | 1704.0644 | null | null |
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning | stat.ML cs.CL cs.LG | Bandit structured prediction describes a stochastic optimization framework
where learning is performed from partial feedback. This feedback is received in
the form of a task loss evaluation to a predicted output structure, without
having access to gold standard structures. We advance this framework by lifting
linear bandit learning to neural sequence-to-sequence learning problems using
attention-based recurrent neural networks. Furthermore, we show how to
incorporate control variates into our learning algorithms for variance
reduction and improved generalization. We present an evaluation on a neural
machine translation task that shows improvements of up to 5.89 BLEU points for
domain adaptation from simulated bandit feedback.
| Julia Kreutzer, Artem Sokolov, Stefan Riezler | null | 1704.06497 | null | null |
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces | cs.AI cs.LG | Graph models are relevant in many fields, such as distributed computing,
intelligent tutoring systems or social network analysis. In many cases, such
models need to take changes in the graph structure into account, i.e. a varying
number of nodes or edges. Predicting such changes within graphs can be expected
to yield important insight with respect to the underlying dynamics, e.g. with
respect to user behaviour. However, predictive techniques in the past have
almost exclusively focused on single edges or nodes. In this contribution, we
attempt to predict the future state of a graph as a whole. We propose to phrase
time series prediction as a regression problem and apply dissimilarity- or
kernel-based regression techniques, such as 1-nearest neighbor, kernel
regression and Gaussian process regression, which can be applied to graphs via
graph kernels. The output of the regression is a point embedded in a
pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity-
or kernel-based processing methods. We discuss strategies to speed up Gaussian
Processes regression from cubic to linear time and evaluate our approach on two
well-established theoretical models of graph evolution as well as two real data
sets from the domain of intelligent tutoring systems. We find that simple
regression methods, such as kernel regression, are sufficient to capture the
dynamics in the theoretical models, but that Gaussian process regression
significantly improves the prediction error for real-world data.
| Benjamin Paa{\ss}en and Christina G\"opfert and Barbara Hammer | 10.1007/s11063-017-9684-5 | 1704.06498 | null | null |
Making Neural Programming Architectures Generalize via Recursion | cs.LG cs.NE cs.PL | Empirically, neural networks that attempt to learn programs from data have
exhibited poor generalizability. Moreover, it has traditionally been difficult
to reason about the behavior of these models beyond a certain level of input
complexity. In order to address these issues, we propose augmenting neural
architectures with a key abstraction: recursion. As an application, we
implement recursion in the Neural Programmer-Interpreter framework on four
tasks: grade-school addition, bubble sort, topological sort, and quicksort. We
demonstrate superior generalizability and interpretability with small amounts
of training data. Recursion divides the problem into smaller pieces and
drastically reduces the domain of each neural network component, making it
tractable to prove guarantees about the overall system's behavior. Our
experience suggests that in order for neural architectures to robustly learn
program semantics, it is necessary to incorporate a concept like recursion.
| Jonathon Cai, Richard Shin, Dawn Song | null | 1704.06611 | null | null |
Learned D-AMP: Principled Neural Network based Compressive Image
Recovery | stat.ML cs.LG | Compressive image recovery is a challenging problem that requires fast and
accurate algorithms. Recently, neural networks have been applied to this
problem with promising results. By exploiting massively parallel GPU processing
architectures and oodles of training data, they can run orders of magnitude
faster than existing techniques. However, these methods are largely
unprincipled black boxes that are difficult to train and often-times specific
to a single measurement matrix.
It was recently demonstrated that iterative sparse-signal-recovery algorithms
can be "unrolled" to form interpretable deep networks. Taking inspiration from
this work, we develop a novel neural network architecture that mimics the
behavior of the denoising-based approximate message passing (D-AMP) algorithm.
We call this new network Learned D-AMP (LDAMP).
The LDAMP network is easy to train, can be applied to a variety of different
measurement matrices, and comes with a state-evolution heuristic that
accurately predicts its performance. Most importantly, it outperforms the
state-of-the-art BM3D-AMP and NLR-CS algorithms in terms of both accuracy and
run time. At high resolutions, and when used with sensing matrices that have
fast implementations, LDAMP runs over $50\times$ faster than BM3D-AMP and
hundreds of times faster than NLR-CS.
| Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk | null | 1704.06625 | null | null |
Feature selection algorithm based on Catastrophe model to improve the
performance of regression analysis | cs.LG stat.ML | In this paper we introduce a new feature selection algorithm to remove the
irrelevant or redundant features in the data sets. In this algorithm the
importance of a feature is based on its fitting to the Catastrophe model.
Akaike information crite- rion value is used for ranking the features in the
data set. The proposed algorithm is compared with well-known RELIEF feature
selection algorithm. Breast Cancer, Parkinson Telemonitoring data and Slice
locality data sets are used to evaluate the model.
| Mahdi Zarei | null | 1704.06656 | null | null |
Batch-Expansion Training: An Efficient Optimization Framework | cs.LG | We propose Batch-Expansion Training (BET), a framework for running a batch
optimizer on a gradually expanding dataset. As opposed to stochastic
approaches, batches do not need to be resampled i.i.d. at every iteration, thus
making BET more resource efficient in a distributed setting, and when
disk-access is constrained. Moreover, BET can be easily paired with most batch
optimizers, does not require any parameter-tuning, and compares favorably to
existing stochastic and batch methods. We show that when the batch size grows
exponentially with the number of outer iterations, BET achieves optimal
$O(1/\epsilon)$ data-access convergence rate for strongly convex objectives.
Experiments in parallel and distributed settings show that BET performs better
than standard batch and stochastic approaches.
| Micha{\l} Derezi\'nski and Dhruv Mahajan and S. Sathiya Keerthi and S.
V. N. Vishwanathan and Markus Weimer | null | 1704.06731 | null | null |
Robust, Deep and Inductive Anomaly Detection | cs.LG cs.CV stat.ML | PCA is a classical statistical technique whose simplicity and maturity has
seen it find widespread use as an anomaly detection technique. However, it is
limited in this regard by being sensitive to gross perturbations of the input,
and by seeking a linear subspace that captures normal behaviour. The first
issue has been dealt with by robust PCA, a variant of PCA that explicitly
allows for some data points to be arbitrarily corrupted, however, this does not
resolve the second issue, and indeed introduces the new issue that one can no
longer inductively find anomalies on a test set. This paper addresses both
issues in a single model, the robust autoencoder. This method learns a
nonlinear subspace that captures the majority of data points, while allowing
for some data to have arbitrary corruption. The model is simple to train and
leverages recent advances in the optimisation of deep neural networks.
Experiments on a range of real-world datasets highlight the model's
effectiveness.
| Raghavendra Chalapathy (University of Sydney and Capital Markets
Cooperative Research Centre (CMCRC)), Aditya Krishna Menon (Data61/CSIRO and
the Australian National University), and Sanjay Chawla (Qatar Computing
Research Institute (QCRI), HBKU) | null | 1704.06743 | null | null |
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