pwc-archive/papers-with-abstracts huggingface dataset:
Successive Ray Refinement and Its Application to Coordinate Descent for LASSO
Authors: Jun Liu, Zheng Zhao, Ruiwen Zhang
Date: 2015-12-17 00:00:00
Tasks: Computational Efficiency

Coordinate descent is one of the most popular approaches for solving Lasso
and its extensions due to its simplicity and efficiency. When applying
coordinate descent to solving Lasso, we update one coordinate at a time while
fixing the remaining coordinates. Such an update, which is usually easy to
compute, greedily decreases the objective function value. In this paper, we aim
to improve its computational efficiency by reducing the number of coordinate
descent iterations. To this end, we propose a novel technique called Successive
Ray Refinement (SRR). SRR makes use of the following ray continuation property
on the successive iterations: for a particular coordinate, the value obtained
in the next iteration almost always lies on a ray that starts at its previous
iteration and passes through the current iteration. Motivated by this
ray-continuation property, we propose that coordinate descent be performed not
directly on the previous iteration but on a refined search point that has the
following properties: on one hand, it lies on a ray that starts at a history
solution and passes through the previous iteration, and on the other hand, it
achieves the minimum objective function value among all the points on the ray.
We propose two schemes for defining the search point and show that the refined
search point can be efficiently obtained. Empirical results for real and
synthetic data sets show that the proposed SRR can significantly reduce the
number of coordinate descent iterations, especially for small Lasso
regularization parameters.
Macau: Scalable Bayesian Multi-relational Factorization with Side Information using MCMC
Authors: Jaak Simm, Adam Arany, Pooya Zakeri, Tom Haber, Jörg K. Wegner, Vladimir Chupakhin, Hugo Ceulemans, Yves Moreau
Date: 2015-09-15 00:00:00
Tasks: Activity Prediction, Relation

We propose Macau, a powerful and flexible Bayesian factorization method for
heterogeneous data. Our model can factorize any set of entities and relations
that can be represented by a relational model, including tensors and also
multiple relations for each entity. Macau can also incorporate side
information, specifically entity and relation features, which are crucial for
predicting sparsely observed relations. Macau scales to millions of entity
instances, hundred millions of observations, and sparse entity features with
millions of dimensions. To achieve the scale up, we specially designed sampling
procedure for entity and relation features that relies primarily on noise
injection in linear regressions. We show performance and advanced features of
Macau in a set of experiments, including challenging drug-protein activity
prediction task.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
Authors: Hongyuan Mei, Mohit Bansal, Matthew R. Walter
Date: 2015-06-12 00:00:00
Tasks: Decoder, Natural Language Understanding, Sentence

We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a representation of the observable world state. We
introduce a multi-level aligner that empowers our model to focus on sentence
"regions" salient to the current world state by using multiple abstractions of
the input sentence. In contrast to existing methods, our model uses no
specialized linguistic resources (e.g., parsers) or task-specific annotations
(e.g., seed lexicons). It is therefore generalizable, yet still achieves the
best results reported to-date on a benchmark single-sentence dataset and
competitive results for the limited-training multi-sentence setting. We analyze
our model through a series of ablations that elucidate the contributions of the
primary components of our model.
Oracle inequalities for ranking and U-processes with Lasso penalty
Authors: Wojciech Rejchel
Date: 2015-12-17 00:00:00
Tasks: BIG-bench Machine Learning

We investigate properties of estimators obtained by minimization of
U-processes with the Lasso penalty in high-dimensional settings. Our attention
is focused on the ranking problem that is popular in machine learning. It is
related to guessing the ordering between objects on the basis of their observed
predictors. We prove the oracle inequality for the excess risk of the
considered estimator as well as the bound for the l1 distance between the
estimator and the oracle.
Asymptotic Behavior of Minimal-Exploration Allocation Policies: Almost Sure, Arbitrarily Slow Growing Regret
Authors: Wesley Cowan, Michael N. Katehakis
Date: 2015-05-12 00:00:00

The purpose of this paper is to provide further understanding into the
structure of the sequential allocation ("stochastic multi-armed bandit", or
MAB) problem by establishing probability one finite horizon bounds and
convergence rates for the sample (or "pseudo") regret associated with two
simple classes of allocation policies $\pi$.
  For any slowly increasing function $g$, subject to mild regularity
constraints, we construct two policies (the $g$-Forcing, and the $g$-Inflated
Sample Mean) that achieve a measure of regret of order $ O(g(n))$ almost surely
as $n \to \infty$, bound from above and below. Additionally, almost sure upper
and lower bounds on the remainder term are established. In the constructions
herein, the function $g$ effectively controls the "exploration" of the
classical "exploration/exploitation" tradeoff.
rnn : Recurrent Library for Torch
Authors: Nicholas Léonard, Sagar Waghmare, Yang Wang, Jin-Hwa Kim
Date: 2015-11-24 00:00:00

The rnn package provides components for implementing a wide range of
Recurrent Neural Networks. It is built withing the framework of the Torch
distribution for use with the nn package. The components have evolved from 3
iterations, each adding to the flexibility and capability of the package. All
component modules inherit either the AbstractRecurrent or AbstractSequencer
classes. Strong unit testing, continued backwards compatibility and access to
supporting material are the principles followed during its development. The
package is compared against existing implementations of two published papers.
Asymptotically Optimal Sequential Experimentation Under Generalized Ranking
Authors: Wesley Cowan, Michael N. Katehakis
Date: 2015-10-07 00:00:00

We consider the \mnk{classical} problem of a controller activating (or
sampling) sequentially from a finite number of $N \geq 2$ populations,
specified by unknown distributions. Over some time horizon, at each time $n =
1, 2, \ldots$, the controller wishes to select a population to sample, with the
goal of sampling from a population that optimizes some "score" function of its
distribution, e.g., maximizing the expected sum of outcomes or minimizing
variability. We define a class of \textit{Uniformly Fast (UF)} sampling
policies and show, under mild regularity conditions, that there is an
asymptotic lower bound for the expected total number of sub-optimal population
activations. Then, we provide sufficient conditions under which a UCB policy is
UF and asymptotically optimal, since it attains this lower bound. Explicit
solutions are provided for a number of examples of interest, including general
score functionals on unconstrained Pareto distributions (of potentially
infinite mean), and uniform distributions of unknown support. Additional
results on bandits of Normal distributions are also provided.
Asymptotically Optimal Multi-Armed Bandit Policies under a Cost Constraint
Authors: Apostolos N. Burnetas, Odysseas Kanavetas, Michael N. Katehakis
Date: 2015-09-09 00:00:00

We develop asymptotically optimal policies for the multi armed bandit (MAB),
problem, under a cost constraint. This model is applicable in situations where
each sample (or activation) from a population (bandit) incurs a known bandit
dependent cost. Successive samples from each population are iid random
variables with unknown distribution. The objective is to design a feasible
policy for deciding from which population to sample from, so as to maximize the
expected sum of outcomes of $n$ total samples or equivalently to minimize the
regret due to lack on information on sample distributions, For this problem we
consider the class of feasible uniformly fast (f-UF) convergent policies, that
satisfy the cost constraint sample-path wise. We first establish a necessary
asymptotic lower bound for the rate of increase of the regret function of f-UF
policies. Then we construct a class of f-UF policies and provide conditions
under which they are asymptotically optimal within the class of f-UF policies,
achieving this asymptotic lower bound. At the end we provide the explicit form
of such policies for the case in which the unknown distributions are Normal
with unknown means and known variances.
Deep-Spying: Spying using Smartwatch and Deep Learning
Authors: Tony Beltramelli, Sebastian Risi
Date: 2015-12-17 00:00:00
Tasks: Deep Learning

Wearable technologies are today on the rise, becoming more common and broadly
available to mainstream users. In fact, wristband and armband devices such as
smartwatches and fitness trackers already took an important place in the
consumer electronics market and are becoming ubiquitous. By their very nature
of being wearable, these devices, however, provide a new pervasive attack
surface threatening users privacy, among others.
  In the meantime, advances in machine learning are providing unprecedented
possibilities to process complex data efficiently. Allowing patterns to emerge
from high dimensional unavoidably noisy data.
  The goal of this work is to raise awareness about the potential risks related
to motion sensors built-in wearable devices and to demonstrate abuse
opportunities leveraged by advanced neural network architectures.
  The LSTM-based implementation presented in this research can perform
touchlogging and keylogging on 12-keys keypads with above-average accuracy even
when confronted with raw unprocessed data. Thus demonstrating that deep neural
networks are capable of making keystroke inference attacks based on motion
sensors easier to achieve by removing the need for non-trivial pre-processing
pipelines and carefully engineered feature extraction strategies. Our results
suggest that the complete technological ecosystem of a user can be compromised
when a wearable wristband device is worn.
Reconstruction of Enhanced Ultrasound Images From Compressed Measurements Using Simultaneous Direction Method of Multipliers
Authors: Zhouye Chen, Adrian Basarab, Denis Kouamé
Date: 2015-12-17 00:00:00
Tasks: compressed sensing, Image Deconvolution, Image Reconstruction

High resolution ultrasound image reconstruction from a reduced number of
measurements is of great interest in ultrasound imaging, since it could enhance
both the frame rate and image resolution. Compressive deconvolution, combining
compressed sensing and image deconvolution, represents an interesting
possibility to consider this challenging task. The model of compressive
deconvolution includes, in addition to the compressive sampling matrix, a 2D
convolution operator carrying the information on the system point spread
function. Through this model, the resolution of reconstructed ultrasound images
from compressed measurements mainly depends on three aspects: the acquisition
setup, i.e. the incoherence of the sampling matrix, the image regularization,
i.e. the sparsity prior, and the optimization technique. In this paper, we
mainly focused on the last two aspects. We proposed a novel simultaneous
direction method of multipliers-based optimization scheme to invert the linear
model, including two regularization terms expressing the sparsity of the RF
images in a given basis and the generalized Gaussian statistical assumption on
tissue reflectivity functions. The performance of the method is evaluated on
both simulated and in vivo data.
A thermodynamical approach towards multi-criteria decision making (MCDM)
Authors: Mohit Verma, J. Rajasankar
Date: 2015-12-17 00:00:00
Tasks: Decision Making

In multi-criteria decision making (MCDM) problems, ratings are assigned to
the alternatives on different criteria by the expert group. In this paper, we
propose a thermodynamically consistent model for MCDM using the analogies for
thermodynamical indicators - energy, exergy and entropy. The most commonly used
method for analysing MCDM problem is Technique for Order of Preference by
Similarity to Ideal Solution (TOPSIS). The conventional TOPSIS method uses a
measure similar to that of energy for the ranking of alternatives. We
demonstrate that the ranking of the alternatives is more meaningful if we use
exergy in place of energy. The use of exergy is superior due to the inclusion
of a factor accounting for the quality of the ratings by the expert group. The
unevenness in the ratings by the experts is measured by entropy. The procedure
for the calculation of the thermodynamical indicators is explained in both
crisp and fuzzy environment. Finally, two case studies are carried out to
demonstrate effectiveness of the proposed model.
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments
Authors: Denis Steckelmacher, Peter Vrancx
Date: 2015-12-17 00:00:00
Tasks: CPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper explores the performance of fitted neural Q iteration for
reinforcement learning in several partially observable environments, using
three recurrent neural network architectures: Long Short-Term Memory, Gated
Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of
several thousands candidate architectures. A variant of fitted Q iteration,
based on Advantage values instead of Q values, is also explored. The results
show that GRU performs significantly better than LSTM and MUT1 for most of the
problems considered, requiring less training episodes and less CPU time before
learning a very good policy. Advantage learning also tends to produce better
results.
Deep Active Object Recognition by Joint Label and Action Prediction
Authors: Mohsen Malmir, Karan Sikka, Deborah Forster, Ian Fasel, Javier R. Movellan, Garrison W. Cottrell
Date: 2015-12-17 00:00:00
Tasks: Object, Object Recognition, Prediction, Reinforcement Learning

An active object recognition system has the advantage of being able to act in
the environment to capture images that are more suited for training and that
lead to better performance at test time. In this paper, we propose a deep
convolutional neural network for active object recognition that simultaneously
predicts the object label, and selects the next action to perform on the object
with the aim of improving recognition performance. We treat active object
recognition as a reinforcement learning problem and derive the cost function to
train the network for joint prediction of the object label and the action. A
generative model of object similarities based on the Dirichlet distribution is
proposed and embedded in the network for encoding the state of the system. The
training is carried out by simultaneously minimizing the label and action
prediction errors using gradient descent. We empirically show that the proposed
network is able to predict both the object label and the actions on GERMS, a
dataset for active object recognition. We compare the test label prediction
accuracy of the proposed model with Dirichlet and Naive Bayes state encoding.
The results of experiments suggest that the proposed model equipped with
Dirichlet state encoding is superior in performance, and selects images that
lead to better training and higher accuracy of label prediction at test time.
Clustering and Inference From Pairwise Comparisons
Authors: Rui Wu, Jiaming Xu, R. Srikant, Laurent Massoulié, Marc Lelarge, Bruce Hajek
Date: 2015-02-16 00:00:00
Tasks: Clustering

Given a set of pairwise comparisons, the classical ranking problem computes a
single ranking that best represents the preferences of all users. In this
paper, we study the problem of inferring individual preferences, arising in the
context of making personalized recommendations. In particular, we assume that
there are $n$ users of $r$ types; users of the same type provide similar
pairwise comparisons for $m$ items according to the Bradley-Terry model. We
propose an efficient algorithm that accurately estimates the individual
preferences for almost all users, if there are $r \max \{m, n\}\log m \log^2 n$
pairwise comparisons per type, which is near optimal in sample complexity when
$r$ only grows logarithmically with $m$ or $n$. Our algorithm has three steps:
first, for each user, compute the \emph{net-win} vector which is a projection
of its $\binom{m}{2}$-dimensional vector of pairwise comparisons onto an
$m$-dimensional linear subspace; second, cluster the users based on the net-win
vectors; third, estimate a single preference for each cluster separately. The
net-win vectors are much less noisy than the high dimensional vectors of
pairwise comparisons and clustering is more accurate after the projection as
confirmed by numerical experiments. Moreover, we show that, when a cluster is
only approximately correct, the maximum likelihood estimation for the
Bradley-Terry model is still close to the true preference.
Unsupervised Feature Construction for Improving Data Representation and Semantics
Authors: Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich
Date: 2015-12-17 00:00:00
Tasks: Two-sample testing

Feature-based format is the main data representation format used by machine
learning algorithms. When the features do not properly describe the initial
data, performance starts to degrade. Some algorithms address this problem by
internally changing the representation space, but the newly-constructed
features are rarely comprehensible. We seek to construct, in an unsupervised
way, new features that are more appropriate for describing a given dataset and,
at the same time, comprehensible for a human user. We propose two algorithms
that construct the new features as conjunctions of the initial primitive
features or their negations. The generated feature sets have reduced
correlations between features and succeed in catching some of the hidden
relations between individuals in a dataset. For example, a feature like $sky
\wedge \neg building \wedge panorama$ would be true for non-urban images and is
more informative than simple features expressing the presence or the absence of
an object. The notion of Pareto optimality is used to evaluate feature sets and
to obtain a balance between total correlation and the complexity of the
resulted feature set. Statistical hypothesis testing is used in order to
automatically determine the values of the parameters used for constructing a
data-dependent feature set. We experimentally show that our approaches achieve
the construction of informative feature sets for multiple datasets.
Dense Optical Flow Prediction from a Static Image
Authors: Jacob Walker, Abhinav Gupta, Martial Hebert
Date: 2015-05-02 00:00:00
Proceeding: ICCV 2015 12
Tasks: motion prediction, Optical Flow Estimation, Prediction

Given a scene, what is going to move, and in what direction will it move?
Such a question could be considered a non-semantic form of action prediction.
In this work, we present a convolutional neural network (CNN) based approach
for motion prediction. Given a static image, this CNN predicts the future
motion of each and every pixel in the image in terms of optical flow. Our CNN
model leverages the data in tens of thousands of realistic videos to train our
model. Our method relies on absolutely no human labeling and is able to predict
motion based on the context of the scene. Because our CNN model makes no
assumptions about the underlying scene, it can predict future optical flow on a
diverse set of scenarios. We outperform all previous approaches by large
margins.
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices
Authors: Anoop Cherian, Suvrit Sra
Date: 2015-07-10 00:00:00
Tasks: BIG-bench Machine Learning, Dictionary Learning, Retrieval, Riemannian optimization

Data encoded as symmetric positive definite (SPD) matrices frequently arise
in many areas of computer vision and machine learning. While these matrices
form an open subset of the Euclidean space of symmetric matrices, viewing them
through the lens of non-Euclidean Riemannian geometry often turns out to be
better suited in capturing several desirable data properties. However,
formulating classical machine learning algorithms within such a geometry is
often non-trivial and computationally expensive. Inspired by the great success
of dictionary learning and sparse coding for vector-valued data, our goal in
this paper is to represent data in the form of SPD matrices as sparse conic
combinations of SPD atoms from a learned dictionary via a Riemannian geometric
approach. To that end, we formulate a novel Riemannian optimization objective
for dictionary learning and sparse coding in which the representation loss is
characterized via the affine invariant Riemannian metric. We also present a
computationally simple algorithm for optimizing our model. Experiments on
several computer vision datasets demonstrate superior classification and
retrieval performance using our approach when compared to sparse coding via
alternative non-Riemannian formulations.
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
Authors: David Eigen, Rob Fergus
Date: 2014-11-18 00:00:00
Proceeding: ICCV 2015 12
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Superpixels, Surface Normal Estimation

In this paper we address three different computer vision tasks using a single
basic architecture: depth prediction, surface normal estimation, and semantic
labeling. We use a multiscale convolutional network that is able to adapt
easily to each task using only small modifications, regressing from the input
image to the output map directly. Our method progressively refines predictions
using a sequence of scales, and captures many image details without any
superpixels or low-level segmentation. We achieve state-of-the-art performance
on benchmarks for all three tasks.
A convergence and asymptotic analysis of the generalized symmetric FastICA algorithm
Authors: Tianwen Wei
Date: 2014-08-01 00:00:00

This contribution deals with the generalized symmetric FastICA algorithm in
the domain of Independent Component Analysis (ICA). The generalized symmetric
version of FastICA has been shown to have the potential to achieve the
Cram\'er-Rao Bound (CRB) by allowing the usage of different nonlinearity
functions in its parallel implementations of one-unit FastICA. In spite of this
appealing property, a rigorous study of the asymptotic error of the generalized
symmetric FastICA algorithm is still missing in the community. In fact, all the
existing results exhibit certain limitations, such as ignoring the impact of
data standardization on the asymptotic statistics or being based on a heuristic
approach.
  In this work, we aim at filling this blank.
  The first result of this contribution is the characterization of the limits
of the generalized symmetric FastICA. It is shown that the algorithm optimizes
a function that is a sum of the contrast functions used by traditional one-unit
FastICA with a correction of the sign. Based on this characterization, we
derive a closed-form analytic expression of the asymptotic covariance matrix of
the generalized symmetric FastICA estimator using the method of estimating
equation and M-estimator.