pwc-archive/papers-with-abstracts huggingface dataset:
A Publicly Available Cross-Platform Lemmatizer for Bulgarian
Authors: Grigor Iliev, Nadezhda Borisova, Elena Karashtranova, Dafina Kostadinova
Date: 2015-06-13 00:00:00
Tasks: Lemmatization, MORPH

Our dictionary-based lemmatizer for the Bulgarian language presented here is
distributed as free software, publicly available to download and use under the
GPL v3 license. The presented software is written entirely in Java and is
distributed as a GATE plugin. To our best knowledge, at the time of writing
this article, there are not any other free lemmatization tools specifically
targeting the Bulgarian language. The presented lemmatizer is a work in
progress and currently yields an accuracy of about 95% in comparison to the
manually annotated corpus BulTreeBank-Morph, which contains 273933 tokens.
Deep Structured Models For Group Activity Recognition
Authors: Zhiwei Deng, Mengyao Zhai, Lei Chen, Yuhao Liu, Srikanth Muralidharan, Mehrsan Javan Roshtkhari, Greg Mori
Date: 2015-06-12 00:00:00
Tasks: Activity Recognition, Group Activity Recognition

This paper presents a deep neural-network-based hierarchical graphical model
for individual and group activity recognition in surveillance scenes. Deep
networks are used to recognize the actions of individual people in a scene.
Next, a neural-network-based hierarchical graphical model refines the predicted
labels for each class by considering dependencies between the classes. This
refinement step mimics a message-passing step similar to inference in a
probabilistic graphical model. We show that this approach can be effective in
group activity recognition, with the deep graphical model improving recognition
rates over baseline methods.
Search Strategies for Binary Feature Selection for a Naive Bayes Classifier
Authors: Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi
Date: 2015-06-12 00:00:00
Tasks: feature selection, General Classification

We compare in this paper several feature selection methods for the Naive
Bayes Classifier (NBC) when the data under study are described by a large
number of redundant binary indicators. Wrapper approaches guided by the NBC
estimation of the classification error probability out-perform filter
approaches while retaining a reasonable computational cost.
Using the Mean Absolute Percentage Error for Regression Models
Authors: Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi
Date: 2015-06-12 00:00:00
Tasks: regression

We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We show that universal consistency of
Empirical Risk Minimization remains possible using the MAPE instead of the MAE.
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
Authors: Marco Corneli, Pierre Latouche, Fabrice Rossi
Date: 2015-06-12 00:00:00

The latent block model (LBM) is a flexible probabilistic tool to describe
interactions between node sets in bipartite networks, but it does not account
for interactions of time varying intensity between nodes in unknown classes. In
this paper we propose a non stationary temporal extension of the LBM that
clusters simultaneously the two node sets of a bipartite network and constructs
classes of time intervals on which interactions are stationary. The number of
clusters as well as the membership to classes are obtained by maximizing the
exact complete-data integrated likelihood relying on a greedy search approach.
Experiments on simulated and real data are carried out in order to assess the
proposed methodology.
Reducing offline evaluation bias of collaborative filtering algorithms
Authors: Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi
Date: 2015-06-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommendation systems have been integrated into the majority of large online
systems to filter and rank information according to user profiles. It thus
influences the way users interact with the system and, as a consequence, bias
the evaluation of the performance of a recommendation algorithm computed using
historical data (via offline evaluation). This paper presents a new application
of a weighted offline evaluation to reduce this bias for collaborative
filtering algorithms.
Random Maxout Features
Authors: Youssef Mroueh, Steven Rennie, Vaibhava Goel
Date: 2015-06-11 00:00:00
Tasks: Data Visualization, Dimensionality Reduction, General Classification, Generalization Bounds

In this paper, we propose and study random maxout features, which are
constructed by first projecting the input data onto sets of randomly generated
vectors with Gaussian elements, and then outputing the maximum projection value
for each set. We show that the resulting random feature map, when used in
conjunction with linear models, allows for the locally linear estimation of the
function of interest in classification tasks, and for the locally linear
embedding of points when used for dimensionality reduction or data
visualization. We derive generalization bounds for learning that assess the
error in approximating locally linear functions by linear functions in the
maxout feature space, and empirically evaluate the efficacy of the approach on
the MNIST and TIMIT classification tasks.
Automatic Variational Inference in Stan
Authors: Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei
Date: 2015-06-10 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Bayesian Inference, Probabilistic Programming, Variational Inference

Variational inference is a scalable technique for approximate Bayesian
inference. Deriving variational inference algorithms requires tedious
model-specific calculations; this makes it difficult to automate. We propose an
automatic variational inference algorithm, automatic differentiation
variational inference (ADVI). The user only provides a Bayesian model and a
dataset; nothing else. We make no conjugacy assumptions and support a broad
class of models. The algorithm automatically determines an appropriate
variational family and optimizes the variational objective. We implement ADVI
in Stan (code available now), a probabilistic programming framework. We compare
ADVI to MCMC sampling across hierarchical generalized linear models,
nonconjugate matrix factorization, and a mixture model. We train the mixture
model on a quarter million images. With ADVI we can use variational inference
on any model we write in Stan.
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems
Authors: Zhanxing Zhu, Amos J. Storkey
Date: 2015-06-12 00:00:00

We consider a generic convex-concave saddle point problem with separable
structure, a form that covers a wide-ranged machine learning applications.
Under this problem structure, we follow the framework of primal-dual updates
for saddle point problems, and incorporate stochastic block coordinate descent
with adaptive stepsize into this framework. We theoretically show that our
proposal of adaptive stepsize potentially achieves a sharper linear convergence
rate compared with the existing methods. Additionally, since we can select
"mini-batch" of block coordinates to update, our method is also amenable to
parallel processing for large-scale data. We apply the proposed method to
regularized empirical risk minimization and show that it performs comparably
or, more often, better than state-of-the-art methods on both synthetic and
real-world data sets.
Towards Benchmarking Scene Background Initialization
Authors: Lucia Maddalena, Alfredo Petrosino
Date: 2015-06-12 00:00:00
Tasks: Benchmarking

Given a set of images of a scene taken at different times, the availability
of an initial background model that describes the scene without foreground
objects is the prerequisite for a wide range of applications, ranging from
video surveillance to computational photography. Even though several methods
have been proposed for scene background initialization, the lack of a common
groundtruthed dataset and of a common set of metrics makes it difficult to
compare their performance. To move first steps towards an easy and fair
comparison of these methods, we assembled a dataset of sequences frequently
adopted for background initialization, selected or created ground truths for
quantitative evaluation through a selected suite of metrics, and compared
results obtained by some existing methods, making all the material publicly
available.
Open Ended Intelligence: The individuation of Intelligent Agents
Authors: David Weinbaum, Viktoras Veitas
Date: 2015-05-23 00:00:00
Tasks: Reading Comprehension, speech-recognition, Speech Recognition

Artificial General Intelligence is a field of research aiming to distill the
principles of intelligence that operate independently of a specific problem
domain or a predefined context and utilize these principles in order to
synthesize systems capable of performing any intellectual task a human being is
capable of and eventually go beyond that. While "narrow" artificial
intelligence which focuses on solving specific problems such as speech
recognition, text comprehension, visual pattern recognition, robotic motion,
etc. has shown quite a few impressive breakthroughs lately, understanding
general intelligence remains elusive. In the paper we offer a novel theoretical
approach to understanding general intelligence. We start with a brief
introduction of the current conceptual approach. Our critique exposes a number
of serious limitations that are traced back to the ontological roots of the
concept of intelligence. We then propose a paradigm shift from intelligence
perceived as a competence of individual agents defined in relation to an a
priori given problem domain or a goal, to intelligence perceived as a formative
process of self-organization by which intelligent agents are individuated. We
call this process open-ended intelligence. Open-ended intelligence is developed
as an abstraction of the process of cognitive development so its application
can be extended to general agents and systems. We introduce and discuss three
facets of the idea: the philosophical concept of individuation, sense-making
and the individuation of general cognitive agents. We further show how
open-ended intelligence can be framed in terms of a distributed,
self-organizing network of interacting elements and how such process is
scalable. The framework highlights an important relation between coordination
and intelligence and a new understanding of values. We conclude with a number
of questions for future research.
Multi-Atlas Segmentation of Biomedical Images: A Survey
Authors: Juan Eugenio Iglesias, Mert Rory Sabuncu
Date: 2014-12-10 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Survey

Multi-atlas segmentation (MAS), first introduced and popularized by the
pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh,
Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert
and Hammers (2006), is becoming one of the most widely-used and successful
image segmentation techniques in biomedical applications. By manipulating and
utilizing the entire dataset of "atlases" (training images that have been
previously labeled, e.g., manually by an expert), rather than some model-based
average representation, MAS has the flexibility to better capture anatomical
variation, thus offering superior segmentation accuracy. This benefit, however,
typically comes at a high computational cost. Recent advancements in computer
hardware and image processing software have been instrumental in addressing
this challenge and facilitated the wide adoption of MAS. Today, MAS has come a
long way and the approach includes a wide array of sophisticated algorithms
that employ ideas from machine learning, probabilistic modeling, optimization,
and computer vision, among other fields. This paper presents a survey of
published MAS algorithms and studies that have applied these methods to various
biomedical problems. In writing this survey, we have three distinct aims. Our
primary goal is to document how MAS was originally conceived, later evolved,
and now relates to alternative methods. Second, this paper is intended to be a
detailed reference of past research activity in MAS, which now spans over a
decade (2003 - 2014) and entails novel methodological developments and
application-specific solutions. Finally, our goal is to also present a
perspective on the future of MAS, which, we believe, will be one of the
dominant approaches in biomedical image segmentation.
On Using Monolingual Corpora in Neural Machine Translation
Authors: Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, Yoshua Bengio
Date: 2015-03-11 00:00:00
Tasks: de-en, Machine Translation, Translation

Recent work on end-to-end neural network-based architectures for machine
translation has shown promising results for En-Fr and En-De translation.
Arguably, one of the major factors behind this success has been the
availability of high quality parallel corpora. In this work, we investigate how
to leverage abundant monolingual corpora for neural machine translation.
Compared to a phrase-based and hierarchical baseline, we obtain up to $1.96$
BLEU improvement on the low-resource language pair Turkish-English, and $1.59$
BLEU on the focused domain task of Chinese-English chat messages. While our
method was initially targeted toward such tasks with less parallel data, we
show that it also extends to high resource languages such as Cs-En and De-En
where we obtain an improvement of $0.39$ and $0.47$ BLEU scores over the neural
machine translation baselines, respectively.
Knowledge Representation in Learning Classifier Systems: A Review
Authors: Farzaneh Shoeleh, Mahshid Majd, Ali Hamzeh, Sattar Hashemi
Date: 2015-06-12 00:00:00

Knowledge representation is a key component to the success of all rule based
systems including learning classifier systems (LCSs). This component brings
insight into how to partition the problem space what in turn seeks prominent
role in generalization capacity of the system as a whole. Recently, knowledge
representation component has received great deal of attention within data
mining communities due to its impacts on rule based systems in terms of
efficiency and efficacy. The current work is an attempt to find a comprehensive
and yet elaborate view into the existing knowledge representation techniques in
LCS domain in general and XCS in specific. To achieve the objectives, knowledge
representation techniques are grouped into different categories based on the
classification approach in which they are incorporated. In each category, the
underlying rule representation schema and the format of classifier condition to
support the corresponding representation are presented. Furthermore, a precise
explanation on the way that each technique partitions the problem space along
with the extensive experimental results is provided. To have an elaborated view
on the functionality of each technique, a comparative analysis of existing
techniques on some conventional problems is provided. We expect this survey to
be of interest to the LCS researchers and practitioners since it provides a
guideline for choosing a proper knowledge representation technique for a given
problem and also opens up new streams of research on this topic.
MCMC for Variationally Sparse Gaussian Processes
Authors: James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani
Date: 2015-06-12 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Form, Gaussian Processes

Gaussian process (GP) models form a core part of probabilistic machine
learning. Considerable research effort has been made into attacking three
issues with GP models: how to compute efficiently when the number of data is
large; how to approximate the posterior when the likelihood is not Gaussian and
how to estimate covariance function parameter posteriors. This paper
simultaneously addresses these, using a variational approximation to the
posterior which is sparse in support of the function but otherwise free-form.
The result is a Hybrid Monte-Carlo sampling scheme which allows for a
non-Gaussian approximation over the function values and covariance parameters
simultaneously, with efficient computations based on inducing-point sparse GPs.
Code to replicate each experiment in this paper will be available shortly.
Sparse Multi-layer Image Approximation: Facial Image Compression
Authors: Sohrab Ferdowsi, Svyatoslav Voloshynovskiy, Dimche Kostadinov
Date: 2015-06-12 00:00:00
Tasks: Data Compression, Dictionary Learning, Image Compression

We propose a scheme for multi-layer representation of images. The problem is
first treated from an information-theoretic viewpoint where we analyze the
behavior of different sources of information under a multi-layer data
compression framework and compare it with a single-stage (shallow) structure.
We then consider the image data as the source of information and link the
proposed representation scheme to the problem of multi-layer dictionary
learning for visual data. For the current work we focus on the problem of image
compression for a special class of images where we report a considerable
performance boost in terms of PSNR at high compression ratios in comparison
with the JPEG2000 codec.
Technical Report: Image Captioning with Semantically Similar Images
Authors: Martin Kolář, Michal Hradiš, Pavel Zemčík
Date: 2015-06-12 00:00:00
Tasks: Image Captioning

This report presents our submission to the MS COCO Captioning Challenge 2015.
The method uses Convolutional Neural Network activations as an embedding to
find semantically similar images. From these images, the most typical caption
is selected based on unigram frequencies. Although the method received low
scores with automated evaluation metrics and in human assessed average
correctness, it is competitive in the ratio of captions which pass the Turing
test and which are assessed as better or equal to human captions.
Phase Retrieval using Alternating Minimization
Authors: Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
Date: 2013-06-02 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Retrieval

Phase retrieval problems involve solving linear equations, but with missing
sign (or phase, for complex numbers) information. More than four decades after
it was first proposed, the seminal error reduction algorithm of (Gerchberg and
Saxton 1972) and (Fienup 1982) is still the popular choice for solving many
variants of this problem. The algorithm is based on alternating minimization;
i.e. it alternates between estimating the missing phase information, and the
candidate solution. Despite its wide usage in practice, no global convergence
guarantees for this algorithm are known. In this paper, we show that a
(resampling) variant of this approach converges geometrically to the solution
of one such problem -- finding a vector $\mathbf{x}$ from
$\mathbf{y},\mathbf{A}$, where $\mathbf{y} =
\left|\mathbf{A}^{\top}\mathbf{x}\right|$ and $|\mathbf{z}|$ denotes a vector
of element-wise magnitudes of $\mathbf{z}$ -- under the assumption that
$\mathbf{A}$ is Gaussian.
  Empirically, we demonstrate that alternating minimization performs similar to
recently proposed convex techniques for this problem (which are based on
"lifting" to a convex matrix problem) in sample complexity and robustness to
noise. However, it is much more efficient and can scale to large problems.
Analytically, for a resampling version of alternating minimization, we show
geometric convergence to the solution, and sample complexity that is off by log
factors from obvious lower bounds. We also establish close to optimal scaling
for the case when the unknown vector is sparse. Our work represents the first
theoretical guarantee for alternating minimization (albeit with resampling) for
any variant of phase retrieval problems in the non-convex setting.
Robust Structured Low-Rank Approximation on the Grassmannian
Authors: Clemens Hage, Martin Kleinsteuber
Date: 2015-06-12 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Over the past years Robust PCA has been established as a standard tool for
reliable low-rank approximation of matrices in the presence of outliers.
Recently, the Robust PCA approach via nuclear norm minimization has been
extended to matrices with linear structures which appear in applications such
as system identification and data series analysis. At the same time it has been
shown how to control the rank of a structured approximation via matrix
factorization approaches. The drawbacks of these methods either lie in the lack
of robustness against outliers or in their static nature of repeated
batch-processing. We present a Robust Structured Low-Rank Approximation method
on the Grassmannian that on the one hand allows for fast re-initialization in
an online setting due to subspace identification with manifolds, and that is
robust against outliers due to a smooth approximation of the $\ell_p$-norm cost
function on the other hand. The method is evaluated in online time series
forecasting tasks on simulated and real-world data.
New Results for Domineering from Combinatorial Game Theory Endgame Databases
Authors: Jos Uiterwijk, Michael Barton
Date: 2015-06-12 00:00:00

We have constructed endgame databases for all single-component positions up
to 15 squares for Domineering, filled with exact Combinatorial Game Theory
(CGT) values in canonical form. The most important findings are as follows.
  First, as an extension of Conway's [8] famous Bridge Splitting Theorem for
Domineering, we state and prove another theorem, dubbed the Bridge Destroying
Theorem for Domineering. Together these two theorems prove very powerful in
determining the CGT values of large positions as the sum of the values of
smaller fragments, but also to compose larger positions with specified values
from smaller fragments. Using the theorems, we then prove that for any dyadic
rational number there exist Domineering positions with that value.
  Second, we investigate Domineering positions with infinitesimal CGT values,
in particular ups and downs, tinies and minies, and nimbers. In the databases
we find many positions with single or double up and down values, but no ups and
downs with higher multitudes. However, we prove that such single-component ups
and downs easily can be constructed. Further, we find Domineering positions
with 11 different tinies and minies values. For each we give an example. Next,
for nimbers we find many Domineering positions with values up to *3. This is
surprising, since Drummond-Cole [10] suspected that no *2 and *3 positions in
standard Domineering would exist. We show and characterize many *2 and *3
positions. Finally, we give some Domineering positions with values interesting
for other reasons.
  Third, we have investigated the temperature of all positions in our
databases. There appears to be exactly one position with temperature 2 (as
already found before) and no positions with temperature larger than 2. This
supports Berlekamp's conjecture that 2 is the highest possible temperature in
Domineering.
Optimal $γ$ and $C$ for $ε$-Support Vector Regression with RBF Kernels
Authors: Longfei Lu
Date: 2015-06-12 00:00:00
Tasks: regression

The objective of this study is to investigate the efficient determination of
$C$ and $\gamma$ for Support Vector Regression with RBF or mahalanobis kernel
based on numerical and statistician considerations, which indicates the
connection between $C$ and kernels and demonstrates that the deviation of
geometric distance of neighbour observation in mapped space effects the predict
accuracy of $\epsilon$-SVR. We determinate the arrange of $\gamma$ & $C$ and
propose our method to choose their best values.