pwc-archive/papers-with-abstracts huggingface dataset:
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Authors: Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang
Date: 2016-09-15 00:00:00
Tasks: Classification, Clustering, General Classification

Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed
to classify schizophrenic and healthy patients based on their SNPs, DNA
Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods
for finding nonlinear correlations between high-dimensional datasets. Data was
gathered from 183 patients, 79 with schizophrenia and 104 healthy controls.
Kernel and Multiple Kernel CCA represent new avenues for studying
schizophrenia, because, to our knowledge, these methods have not been used on
these data before. Classification is performed via k-means clustering on the
kernel matrix outputs of the Kernel and Multiple Kernel CCA algorithm.
Accuracies of the Kernel and Multiple Kernel CCA classification are compared to
that of the regularized linear CCA algorithm classification, and are found to
be significantly more accurate. Both algorithms demonstrate maximal accuracies
when the combination of DNA methylation and fMRI data are used, and experience
lower accuracies when the SNP data are incorporated.
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
Authors: Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, Rudolf Mester
Date: 2016-09-15 00:00:00

Detecting small obstacles on the road ahead is a critical part of the driving
task which has to be mastered by fully autonomous cars. In this paper, we
present a method based on stereo vision to reliably detect such obstacles from
a moving vehicle. The proposed algorithm performs statistical hypothesis tests
in disparity space directly on stereo image data, assessing freespace and
obstacle hypotheses on independent local patches. This detection approach does
not depend on a global road model and handles both static and moving obstacles.
For evaluation, we employ a novel lost-cargo image sequence dataset comprising
more than two thousand frames with pixelwise annotations of obstacle and
free-space and provide a thorough comparison to several stereo-based baseline
methods. The dataset will be made available to the community to foster further
research on this important topic. The proposed approach outperforms all
considered baselines in our evaluations on both pixel and object level and runs
at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles
down to the height of 5 cm can successfully be detected at 20 m distance at low
false positive rates.
Context Aware Nonnegative Matrix Factorization Clustering
Authors: Rocco Tripodi, Sebastiano Vascon, Marcello Pelillo
Date: 2016-09-15 00:00:00
Tasks: Clustering

In this article we propose a method to refine the clustering results obtained
with the nonnegative matrix factorization (NMF) technique, imposing consistency
constraints on the final labeling of the data. The research community focused
its effort on the initialization and on the optimization part of this method,
without paying attention to the final cluster assignments. We propose a game
theoretic framework in which each object to be clustered is represented as a
player, which has to choose its cluster membership. The information obtained
with NMF is used to initialize the strategy space of the players and a weighted
graph is used to model the interactions among the players. These interactions
allow the players to choose a cluster which is coherent with the clusters
chosen by similar players, a property which is not guaranteed by NMF, since it
produces a soft clustering of the data. The results on common benchmarks show
that our model is able to improve the performances of many NMF formulations.
Factored Neural Machine Translation
Authors: Mercedes García-Martínez, Loïc Barrault, Fethi Bougares
Date: 2016-09-15 00:00:00
Tasks: Machine Translation, NMT, TAG, Translation

We present a new approach for neural machine translation (NMT) using the
morphological and grammatical decomposition of the words (factors) in the
output side of the neural network. This architecture addresses two main
problems occurring in MT, namely dealing with a large target language
vocabulary and the out of vocabulary (OOV) words. By the means of factors, we
are able to handle larger vocabulary and reduce the training time (for systems
with equivalent target language vocabulary size). In addition, we can produce
new words that are not in the vocabulary. We use a morphological analyser to
get a factored representation of each word (lemmas, Part of Speech tag, tense,
person, gender and number). We have extended the NMT approach with attention
mechanism in order to have two different outputs, one for the lemmas and the
other for the rest of the factors. The final translation is built using some
\textit{a priori} linguistic information. We compare our extension with a
word-based NMT system. The experiments, performed on the IWSLT'15 dataset
translating from English to French, show that while the performance do not
always increase, the system can manage a much larger vocabulary and
consistently reduce the OOV rate. We observe up to 2% BLEU point improvement in
a simulated out of domain translation setup.
PicHunt: Social Media Image Retrieval for Improved Law Enforcement
Authors: Sonal Goel, Niharika Sachdeva, Ponnurangam Kumaraguru, A. V. Subramanyam, Divam Gupta
Date: 2016-08-02 00:00:00
Tasks: Image Retrieval, Retrieval

First responders are increasingly using social media to identify and reduce
crime for well-being and safety of the society. Images shared on social media
hurting religious, political, communal and other sentiments of people, often
instigate violence and create law & order situations in society. This results
in the need for first responders to inspect the spread of such images and users
propagating them on social media. In this paper, we present a comparison
between different hand-crafted features and a Convolutional Neural Network
(CNN) model to retrieve similar images, which outperforms state-of-art
hand-crafted features. We propose an Open-Source-Intelligent (OSINT) real-time
image search system, robust to retrieve modified images that allows first
responders to analyze the current spread of images, sentiments floating and
details of users propagating such content. The system also aids officials to
save time of manually analyzing the content by reducing the search space on an
average by 67%.
A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents
Authors: Haris Aziz, Simon Mackenzie
Date: 2016-04-13 00:00:00

We consider the well-studied cake cutting problem in which the goal is to
find an envy-free allocation based on queries from $n$ agents. The problem has
received attention in computer science, mathematics, and economics. It has been
a major open problem whether there exists a discrete and bounded envy-free
protocol. We resolve the problem by proposing a discrete and bounded envy-free
protocol for any number of agents. The maximum number of queries required by
the protocol is $n^{n^{n^{n^{n^n}}}}$. We additionally show that even if we do
not run our protocol to completion, it can find in at most $n^3{(n^2)}^n$
queries a partial allocation of the cake that achieves proportionality (each
agent gets at least $1/n$ of the value of the whole cake) and envy-freeness.
Finally we show that an envy-free partial allocation can be computed in at most
$n^3{(n^2)}^n$ queries such that each agent gets a connected piece that gives
the agent at least $1/(3n)$ of the value of the whole cake.
Multilevel Monte Carlo for Scalable Bayesian Computations
Authors: Mike Giles, Tigran Nagapetyan, Lukasz Szpruch, Sebastian Vollmer, Konstantinos Zygalakis
Date: 2016-09-15 00:00:00

Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian
computations. However, they need to access the full data set in order to
evaluate the posterior density at every step of the algorithm. This results in
a great computational burden in big data applications. In contrast to MCMC
methods, Stochastic Gradient MCMC (SGMCMC) algorithms such as the Stochastic
Gradient Langevin Dynamics (SGLD) only require access to a batch of the data
set at every step. This drastically improves the computational performance and
scales well to large data sets. However, the difficulty with SGMCMC algorithms
comes from the sensitivity to its parameters which are notoriously difficult to
tune. Moreover, the Root Mean Square Error (RMSE) scales as
$\mathcal{O}(c^{-\frac{1}{3}})$ as opposed to standard MCMC
$\mathcal{O}(c^{-\frac{1}{2}})$ where $c$ is the computational cost.
  We introduce a new class of Multilevel Stochastic Gradient Markov chain Monte
Carlo algorithms that are able to mitigate the problem of tuning the step size
and more importantly of recovering the $\mathcal{O}(c^{-\frac{1}{2}})$
convergence of standard Markov Chain Monte Carlo methods without the need to
introduce Metropolis-Hasting steps. A further advantage of this new class of
algorithms is that it can easily be parallelised over a heterogeneous computer
architecture. We illustrate our methodology using Bayesian logistic regression
and provide numerical evidence that for a prescribed relative RMSE the
computational cost is sublinear in the number of data items.
Matrix Product State for Higher-Order Tensor Compression and Classification
Authors: Johann A. Bengua, Ho N. Phien, Hoang D. Tuan, Minh N. Do
Date: 2016-09-15 00:00:00
Tasks: General Classification

This paper introduces matrix product state (MPS) decomposition as a new and
systematic method to compress multidimensional data represented by higher-order
tensors. It solves two major bottlenecks in tensor compression: computation and
compression quality. Regardless of tensor order, MPS compresses tensors to
matrices of moderate dimension which can be used for classification. Mainly
based on a successive sequence of singular value decompositions (SVD), MPS is
quite simple to implement and arrives at the global optimal matrix, bypassing
local alternating optimization, which is not only computationally expensive but
cannot yield the global solution. Benchmark results show that MPS can achieve
better classification performance with favorable computation cost compared to
other tensor compression methods.
Speech vocoding for laboratory phonology
Authors: Milos Cernak, Stefan Benus, Alexandros Lazaridis
Date: 2016-01-22 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Using phonological speech vocoding, we propose a platform for exploring
relations between phonology and speech processing, and in broader terms, for
exploring relations between the abstract and physical structures of a speech
signal. Our goal is to make a step towards bridging phonology and speech
processing and to contribute to the program of Laboratory Phonology. We show
three application examples for laboratory phonology: compositional phonological
speech modelling, a comparison of phonological systems and an experimental
phonological parametric text-to-speech (TTS) system. The featural
representations of the following three phonological systems are considered in
this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English
(SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded
speech, we conclude that the latter achieves slightly better results than the
former. However, GP - the most compact phonological speech representation -
performs comparably to the systems with a higher number of phonological
features. The parametric TTS based on phonological speech representation, and
trained from an unlabelled audiobook in an unsupervised manner, achieves
intelligibility of 85% of the state-of-the-art parametric speech synthesis. We
envision that the presented approach paves the way for researchers in both
fields to form meaningful hypotheses that are explicitly testable using the
concepts developed and exemplified in this paper. On the one hand, laboratory
phonologists might test the applied concepts of their theoretical models, and
on the other hand, the speech processing community may utilize the concepts
developed for the theoretical phonological models for improvements of the
current state-of-the-art applications.
Low-Rank Matrix Recovery using Gabidulin Codes in Characteristic Zero
Authors: Sven Müelich, Sven Puchinger, Martin Bossert
Date: 2016-04-15 00:00:00

We present a new approach on low-rank matrix recovery (LRMR) based on
Gabidulin Codes. Since most applications of LRMR deal with matrices over
infinite fields, we use the recently introduced generalization of Gabidulin
codes to fields of characterstic zero. We show that LRMR can be reduced to
decoding of Gabidulin codes and discuss which field extensions can be used in
the code construction.
Tensor Graphical Model: Non-convex Optimization and Statistical Inference
Authors: Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng
Date: 2016-09-15 00:00:00

We consider the estimation and inference of graphical models that
characterize the dependency structure of high-dimensional tensor-valued data.
To facilitate the estimation of the precision matrix corresponding to each way
of the tensor, we assume the data follow a tensor normal distribution whose
covariance has a Kronecker product structure. A critical challenge in the
estimation and inference of this model is the fact that its penalized maximum
likelihood estimation involves minimizing a non-convex objective function. To
address it, this paper makes two contributions: (i) In spite of the
non-convexity of this estimation problem, we prove that an alternating
minimization algorithm, which iteratively estimates each sparse precision
matrix while fixing the others, attains an estimator with an optimal
statistical rate of convergence. (ii) We propose a de-biased statistical
inference procedure for testing hypotheses on the true support of the sparse
precision matrices, and employ it for testing a growing number of hypothesis
with false discovery rate (FDR) control. The asymptotic normality of our test
statistic and the consistency of FDR control procedure are established. Our
theoretical results are backed up by thorough numerical studies and our real
applications on neuroimaging studies of Autism spectrum disorder and users'
advertising click analysis bring new scientific findings and business insights.
The proposed methods are encoded into a publicly available R package Tlasso.
False Discoveries Occur Early on the Lasso Path
Authors: Weijie Su, Malgorzata Bogdan, Emmanuel Candes
Date: 2015-11-05 00:00:00

In regression settings where explanatory variables have very low correlations
and there are relatively few effects, each of large magnitude, we expect the
Lasso to find the important variables with few errors, if any. This paper shows
that in a regime of linear sparsity---meaning that the fraction of variables
with a non-vanishing effect tends to a constant, however small---this cannot
really be the case, even when the design variables are stochastically
independent. We demonstrate that true features and null features are always
interspersed on the Lasso path, and that this phenomenon occurs no matter how
strong the effect sizes are. We derive a sharp asymptotic trade-off between
false and true positive rates or, equivalently, between measures of type I and
type II errors along the Lasso path. This trade-off states that if we ever want
to achieve a type II error (false negative rate) under a critical value, then
anywhere on the Lasso path the type I error (false positive rate) will need to
exceed a given threshold so that we can never have both errors at a low level
at the same time. Our analysis uses tools from approximate message passing
(AMP) theory as well as novel elements to deal with a possibly adaptive
selection of the Lasso regularizing parameter.
Tsallis Regularized Optimal Transport and Ecological Inference
Authors: Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
Date: 2016-09-15 00:00:00

Optimal transport is a powerful framework for computing distances between
probability distributions. We unify the two main approaches to optimal
transport, namely Monge-Kantorovitch and Sinkhorn-Cuturi, into what we define
as Tsallis regularized optimal transport (\trot). \trot~interpolates a rich
family of distortions from Wasserstein to Kullback-Leibler, encompassing as
well Pearson, Neyman and Hellinger divergences, to name a few. We show that
metric properties known for Sinkhorn-Cuturi generalize to \trot, and provide
efficient algorithms for finding the optimal transportation plan with formal
convergence proofs. We also present the first application of optimal transport
to the problem of ecological inference, that is, the reconstruction of joint
distributions from their marginals, a problem of large interest in the social
sciences. \trot~provides a convenient framework for ecological inference by
allowing to compute the joint distribution --- that is, the optimal
transportation plan itself --- when side information is available, which is
\textit{e.g.} typically what census represents in political science.
Experiments on data from the 2012 US presidential elections display the
potential of \trot~in delivering a faithful reconstruction of the joint
distribution of ethnic groups and voter preferences.
Detection in the stochastic block model with multiple clusters: proof of the achievability conjectures, acyclic BP, and the information-computation gap
Authors: Emmanuel Abbe, Colin Sandon
Date: 2015-12-30 00:00:00
Tasks: Clustering, Stochastic Block Model

In a paper that initiated the modern study of the stochastic block model,
Decelle et al., backed by Mossel et al., made the following conjecture: Denote
by $k$ the number of balanced communities, $a/n$ the probability of connecting
inside communities and $b/n$ across, and set
$\mathrm{SNR}=(a-b)^2/(k(a+(k-1)b)$; for any $k \geq 2$, it is possible to
detect communities efficiently whenever $\mathrm{SNR}>1$ (the KS threshold),
whereas for $k\geq 4$, it is possible to detect communities
information-theoretically for some $\mathrm{SNR}<1$. Massouli\'e, Mossel et
al.\ and Bordenave et al.\ succeeded in proving that the KS threshold is
efficiently achievable for $k=2$, while Mossel et al.\ proved that it cannot be
crossed information-theoretically for $k=2$. The above conjecture remained open
for $k \geq 3$.
  This paper proves this conjecture, further extending the efficient detection
to non-symmetrical SBMs with a generalized notion of detection and KS
threshold. For the efficient part, a linearized acyclic belief propagation
(ABP) algorithm is developed and proved to detect communities for any $k$ down
to the KS threshold in time $O(n \log n)$. Achieving this requires showing
optimality of ABP in the presence of cycles, a challenge for message passing
algorithms. The paper further connects ABP to a power iteration method with a
nonbacktracking operator of generalized order, formalizing the interplay
between message passing and spectral methods. For the information-theoretic
(IT) part, a non-efficient algorithm sampling a typical clustering is shown to
break down the KS threshold at $k=4$. The emerging gap is shown to be large in
some cases; if $a=0$, the KS threshold reads $b \gtrsim k^2$ whereas the IT
bound reads $b \gtrsim k \ln(k)$, making the SBM a good study-case for
information-computation gaps.
The multi-vehicle covering tour problem: building routes for urban patrolling
Authors: Washington Alves de Oliveira, Antonio Carlos Moretti, Ednei Felix Reis
Date: 2013-09-21 00:00:00

In this paper we study a particular aspect of the urban community policing:
routine patrol route planning. We seek routes that guarantee visibility, as
this has a sizable impact on the community perceived safety, allowing quick
emergency responses and providing surveillance of selected sites (e.g.,
hospitals, schools). The planning is restricted to the availability of vehicles
and strives to achieve balanced routes. We study an adaptation of the model for
the multi-vehicle covering tour problem, in which a set of locations must be
visited, whereas another subset must be close enough to the planned routes. It
constitutes an NP-complete integer programming problem. Suboptimal solutions
are obtained with several heuristics, some adapted from the literature and
others developed by us. We solve some adapted instances from TSPLIB and an
instance with real data, the former being compared with results from
literature, and latter being compared with empirical data.
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
Authors: T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye
Date: 2016-09-14 00:00:00
Tasks: Density Estimation, General Classification

We have created a large diverse set of cars from overhead images, which are
useful for training a deep learner to binary classify, detect and count them.
The dataset and all related material will be made publically available. The set
contains contextual matter to aid in identification of difficult targets. We
demonstrate classification and detection on this dataset using a neural network
we call ResCeption. This network combines residual learning with
Inception-style layers and is used to count cars in one look. This is a new way
to count objects rather than by localization or density estimation. It is
fairly accurate, fast and easy to implement. Additionally, the counting method
is not car or scene specific. It would be easy to train this method to count
other kinds of objects and counting over new scenes requires no extra set up or
assumptions about object locations.
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
Authors: C-A. Deledalle, N. Papadakis, J. Salmon, S. Vaiter
Date: 2016-06-16 00:00:00
Tasks: Image Restoration

In this paper, we propose a new framework to remove parts of the systematic
errors affecting popular restoration algorithms, with a special focus for image
processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization,
we develop an approach re-fitting the results of standard methods towards the
input data. Total variation regularizations and non-local means are special
cases of interest. We identify important covariant information that should be
preserved by the re-fitting method, and emphasize the importance of preserving
the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we
provide an approach that has a "twicing" flavor and allows re-fitting the
restored signal by adding back a local affine transformation of the residual
term. We illustrate the benefits of our method on numerical simulations for
image restoration tasks.
Bayesian Reinforcement Learning: A Survey
Authors: Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar
Date: 2016-09-14 00:00:00
Tasks: Bayesian Inference, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Bayesian methods for machine learning have been widely investigated, yielding
principled methods for incorporating prior information into inference
algorithms. In this survey, we provide an in-depth review of the role of
Bayesian methods for the reinforcement learning (RL) paradigm. The major
incentives for incorporating Bayesian reasoning in RL are: 1) it provides an
elegant approach to action-selection (exploration/exploitation) as a function
of the uncertainty in learning; and 2) it provides a machinery to incorporate
prior knowledge into the algorithms. We first discuss models and methods for
Bayesian inference in the simple single-step Bandit model. We then review the
extensive recent literature on Bayesian methods for model-based RL, where prior
information can be expressed on the parameters of the Markov model. We also
present Bayesian methods for model-free RL, where priors are expressed over the
value function or policy class. The objective of the paper is to provide a
comprehensive survey on Bayesian RL algorithms and their theoretical and
empirical properties.