pwc-archive/papers-with-abstracts huggingface dataset:
Near-Ideal Behavior of Compressed Sensing Algorithms
Authors: Mehmet Eren Ahsen, Mathukumalli Vidyasagar
Date: 2014-01-26 00:00:00
Tasks: compressed sensing

In a recent paper, it is shown that the LASSO algorithm exhibits "near-ideal
behavior," in the following sense: Suppose $y = Az + \eta$ where $A$ satisfies
the restricted isometry property (RIP) with a sufficiently small constant, and
$\Vert \eta \Vert_2 \leq \epsilon$. Then minimizing $\Vert z \Vert_1$ subject
to $\Vert y - Az \Vert_2 \leq \epsilon$ leads to an estimate $\hat{x}$ whose
error $\Vert \hat{x} - x \Vert_2$ is bounded by a universal constant times the
error achieved by an "oracle" that knows the location of the nonzero components
of $x$. In the world of optimization, the LASSO algorithm has been generalized
in several directions such as the group LASSO, the sparse group LASSO, either
without or with tree-structured overlapping groups, and most recently, the
sorted LASSO. In this paper, it is shown that {\it any algorithm\/} exhibits
near-ideal behavior in the above sense, provided only that (i) the norm used to
define the sparsity index is "decomposable," (ii) the penalty norm that is
minimized in an effort to enforce sparsity is "$\gamma$-decomposable," and
(iii) a "compressibility condition" in terms of a group restricted isometry
property is satisfied. Specifically, the group LASSO, and the sparse group
LASSO (with some permissible overlap in the groups), as well as the sorted
$\ell_1$-norm minimization all exhibit near-ideal behavior. Explicit bounds on
the residual error are derived that contain previously known results as special
cases.
TurKPF: TurKontrol as a Particle Filter
Authors: Ethan Petuchowski, Matthew Lease
Date: 2014-04-20 00:00:00

TurKontrol, and algorithm presented in (Dai et al. 2010), uses a POMDP to
model and control an iterative workflow for crowdsourced work. Here, TurKontrol
is re-implemented as "TurKPF," which uses a Particle Filter to reduce
computation time & memory usage. Most importantly, in our experimental
environment with default parameter settings, the action is chosen nearly
instantaneously. Through a series of experiments we see that TurKPF and
TurKontrol perform similarly.
Multi-Target Regression via Random Linear Target Combinations
Authors: Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Aikaterini Vrekou, Ioannis Vlahavas
Date: 2014-04-20 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-target regression, regression

Multi-target regression is concerned with the simultaneous prediction of
multiple continuous target variables based on the same set of input variables.
It arises in several interesting industrial and environmental application
domains, such as ecological modelling and energy forecasting. This paper
presents an ensemble method for multi-target regression that constructs new
target variables via random linear combinations of existing targets. We discuss
the connection of our approach with multi-label classification algorithms, in
particular RA$k$EL, which originally inspired this work, and a family of recent
multi-label classification algorithms that involve output coding. Experimental
results on 12 multi-target datasets show that it performs significantly better
than a strong baseline that learns a single model for each target using
gradient boosting and compares favourably to multi-objective random forest
approach, which is a state-of-the-art approach. The experiments further show
that our approach improves more when stronger unconditional dependencies exist
among the targets.
Inter-rater Agreement on Sentence Formality
Authors: Shibamouli Lahiri, Xiaofei Lu
Date: 2011-09-01 00:00:00
Tasks: Sentence

Formality is one of the most important dimensions of writing style variation.
In this study we conducted an inter-rater reliability experiment for assessing
sentence formality on a five-point Likert scale, and obtained good agreement
results as well as different rating distributions for different sentence
categories. We also performed a difficulty analysis to identify the bottlenecks
of our rating procedure. Our main objective is to design an automatic scoring
mechanism for sentence-level formality, and this study is important for that
purpose.
Robust Subspace Recovery via Bi-Sparsity Pursuit
Authors: Xiao Bian, Hamid Krim
Date: 2014-03-31 00:00:00
Tasks: BIG-bench Machine Learning

Successful applications of sparse models in computer vision and machine
learning imply that in many real-world applications, high dimensional data is
distributed in a union of low dimensional subspaces. Nevertheless, the
underlying structure may be affected by sparse errors and/or outliers. In this
paper, we propose a bi-sparse model as a framework to analyze this problem and
provide a novel algorithm to recover the union of subspaces in presence of
sparse corruptions. We further show the effectiveness of our method by
experiments on both synthetic data and real-world vision data.
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density
Authors: Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama
Date: 2014-04-20 00:00:00
Tasks: Clustering, Density Estimation

Mean shift clustering finds the modes of the data probability density by
identifying the zero points of the density gradient. Since it does not require
to fix the number of clusters in advance, the mean shift has been a popular
clustering algorithm in various application fields. A typical implementation of
the mean shift is to first estimate the density by kernel density estimation
and then compute its gradient. However, since good density estimation does not
necessarily imply accurate estimation of the density gradient, such an indirect
two-step approach is not reliable. In this paper, we propose a method to
directly estimate the gradient of the log-density without going through density
estimation. The proposed method gives the global solution analytically and thus
is computationally efficient. We then develop a mean-shift-like fixed-point
algorithm to find the modes of the density for clustering. As in the mean
shift, one does not need to set the number of clusters in advance. We
empirically show that the proposed clustering method works much better than the
mean shift especially for high-dimensional data. Experimental results further
indicate that the proposed method outperforms existing clustering methods.
Complete Complementary Results Report of the MARF's NLP Approach to the DEFT 2010 Competition
Authors: Serguei A. Mokhov
Date: 2010-06-18 00:00:00
Tasks: Articles

This companion paper complements the main DEFT'10 article describing the MARF
approach (arXiv:0905.1235) to the DEFT'10 NLP challenge (described at
http://www.groupes.polymtl.ca/taln2010/deft.php in French). This paper is aimed
to present the complete result sets of all the conducted experiments and their
settings in the resulting tables highlighting the approach and the best
results, but also showing the worse and the worst and their subsequent
analysis. This particular work focuses on application of the MARF's classical
and NLP pipelines to identification tasks within various francophone corpora to
identify decades when certain articles were published for the first track
(Piste 1) and place of origin of a publication (Piste 2), such as the journal
and location (France vs. Quebec). This is the sixth iteration of the release of
the results.
Shiva++: An Enhanced Graph based Ontology Matcher
Authors: Iti Mathur, Nisheeth Joshi, Hemant Darbari, Ajai Kumar
Date: 2014-04-19 00:00:00
Tasks: Graph Matching, Management

With the web getting bigger and assimilating knowledge about different
concepts and domains, it is becoming very difficult for simple database driven
applications to capture the data for a domain. Thus developers have come out
with ontology based systems which can store large amount of information and can
apply reasoning and produce timely information. Thus facilitating effective
knowledge management. Though this approach has made our lives easier, but at
the same time has given rise to another problem. Two different ontologies
assimilating same knowledge tend to use different terms for the same concepts.
This creates confusion among knowledge engineers and workers, as they do not
know which is a better term then the other. Thus we need to merge ontologies
working on same domain so that the engineers can develop a better application
over it. This paper shows the development of one such matcher which merges the
concepts available in two ontologies at two levels; 1) at string level and 2)
at semantic level; thus producing better merged ontologies. We have used a
graph matching technique which works at the core of the system. We have also
evaluated the system and have tested its performance with its predecessor which
works only on string matching. Thus current approach produces better results.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Authors: Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Date: 2013-12-20 00:00:00
Tasks: General Classification, Image Attribution, image-classification, Image Classification, Interpretability Techniques for Deep Learning, Semantic Segmentation, Weakly-Supervised Object Segmentation

This paper addresses the visualisation of image classification models, learnt
using deep Convolutional Networks (ConvNets). We consider two visualisation
techniques, based on computing the gradient of the class score with respect to
the input image. The first one generates an image, which maximises the class
score [Erhan et al., 2009], thus visualising the notion of the class, captured
by a ConvNet. The second technique computes a class saliency map, specific to a
given image and class. We show that such maps can be employed for weakly
supervised object segmentation using classification ConvNets. Finally, we
establish the connection between the gradient-based ConvNet visualisation
methods and deconvolutional networks [Zeiler et al., 2013].
Geometric Abstraction from Noisy Image-Based 3D Reconstructions
Authors: Thomas Holzmann, Christof Hoppe, Stefan Kluckner, Horst Bischof
Date: 2014-04-19 00:00:00

Creating geometric abstracted models from image-based scene reconstructions
is difficult due to noise and irregularities in the reconstructed model. In
this paper, we present a geometric modeling method for noisy reconstructions
dominated by planar horizontal and orthogonal vertical structures. We partition
the scene into horizontal slices and create an inside/outside labeling
represented by a floor plan for each slice by solving an energy minimization
problem. Consecutively, we create an irregular discretization of the volume
according to the individual floor plans and again label each cell as
inside/outside by minimizing an energy function. By adjusting the smoothness
parameter, we introduce different levels of detail. In our experiments, we show
results with varying regularization levels using synthetically generated and
real-world data.
Opinion Mining In Hindi Language: A Survey
Authors: Richa Sharma, Shweta Nigam, Rekha Jain
Date: 2014-04-19 00:00:00
Tasks: Opinion Mining, Sentiment Analysis, Survey

Opinions are very important in the life of human beings. These Opinions
helped the humans to carry out the decisions. As the impact of the Web is
increasing day by day, Web documents can be seen as a new source of opinion for
human beings. Web contains a huge amount of information generated by the users
through blogs, forum entries, and social networking websites and so on To
analyze this large amount of information it is required to develop a method
that automatically classifies the information available on the Web. This domain
is called Sentiment Analysis and Opinion Mining. Opinion Mining or Sentiment
Analysis is a natural language processing task that mine information from
various text forms such as reviews, news, and blogs and classify them on the
basis of their polarity as positive, negative or neutral. But, from the last
few years, enormous increase has been seen in Hindi language on the Web.
Research in opinion mining mostly carried out in English language but it is
very important to perform the opinion mining in Hindi language also as large
amount of information in Hindi is also available on the Web. This paper gives
an overview of the work that has been done Hindi language.
CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
Authors: Daniele Codecasa, Fabio Stella
Date: 2014-04-18 00:00:00
Tasks: Clustering, General Classification

Continuous time Bayesian network classifiers are designed for temporal
classification of multivariate streaming data when time duration of events
matters and the class does not change over time. This paper introduces the
CTBNCToolkit: an open source Java toolkit which provides a stand-alone
application for temporal classification and a library for continuous time
Bayesian network classifiers. CTBNCToolkit implements the inference algorithm,
the parameter learning algorithm, and the structural learning algorithm for
continuous time Bayesian network classifiers. The structural learning algorithm
is based on scoring functions: the marginal log-likelihood score and the
conditional log-likelihood score are provided. CTBNCToolkit provides also an
implementation of the expectation maximization algorithm for clustering
purpose. The paper introduces continuous time Bayesian network classifiers. How
to use the CTBNToolkit from the command line is described in a specific
section. Tutorial examples are included to facilitate users to understand how
the toolkit must be used. A section dedicate to the Java library is proposed to
help further code extensions.
Rapid and deterministic estimation of probability densities using scale-free field theories
Authors: Justin B. Kinney
Date: 2013-12-23 00:00:00

The question of how best to estimate a continuous probability density from
finite data is an intriguing open problem at the interface of statistics and
physics. Previous work has argued that this problem can be addressed in a
natural way using methods from statistical field theory. Here I describe new
results that allow this field-theoretic approach to be rapidly and
deterministically computed in low dimensions, making it practical for use in
day-to-day data analysis. Importantly, this approach does not impose a
privileged length scale for smoothness of the inferred probability density, but
rather learns a natural length scale from the data due to the tradeoff between
goodness-of-fit and an Occam factor. Open source software implementing this
method in one and two dimensions is provided.
Coactive Learning for Locally Optimal Problem Solving
Authors: Robby Goetschalckx, Alan Fern, Prasad Tadepalli
Date: 2014-04-18 00:00:00

Coactive learning is an online problem solving setting where the solutions
provided by a solver are interactively improved by a domain expert, which in
turn drives learning. In this paper we extend the study of coactive learning to
problems where obtaining a globally optimal or near-optimal solution may be
intractable or where an expert can only be expected to make small, local
improvements to a candidate solution. The goal of learning in this new setting
is to minimize the cost as measured by the expert effort over time. We first
establish theoretical bounds on the average cost of the existing coactive
Perceptron algorithm. In addition, we consider new online algorithms that use
cost-sensitive and Passive-Aggressive (PA) updates, showing similar or improved
theoretical bounds. We provide an empirical evaluation of the learners in
various domains, which show that the Perceptron based algorithms are quite
effective and that unlike the case for online classification, the PA algorithms
do not yield significant performance gains.
Causal Interfaces
Authors: David A. Eubanks
Date: 2014-04-18 00:00:00

The interaction of two binary variables, assumed to be empirical
observations, has three degrees of freedom when expressed as a matrix of
frequencies. Usually, the size of causal influence of one variable on the other
is calculated as a single value, as increase in recovery rate for a medical
treatment, for example. We examine what is lost in this simplification, and
propose using two interface constants to represent positive and negative
implications separately. Given certain assumptions about non-causal outcomes,
the set of resulting epistemologies is a continuum. We derive a variety of
particular measures and contrast them with the one-dimensional index.
Bias Correction and Modified Profile Likelihood under the Wishart Complex Distribution
Authors: Abraão D. C. Nascimento, Alejandro C. Frery, Renato J. Cintra
Date: 2014-04-18 00:00:00

This paper proposes improved methods for the maximum likelihood (ML)
estimation of the equivalent number of looks $L$. This parameter has a
meaningful interpretation in the context of polarimetric synthetic aperture
radar (PolSAR) images. Due to the presence of coherent illumination in their
processing, PolSAR systems generate images which present a granular noise
called speckle. As a potential solution for reducing such interference, the
parameter $L$ controls the signal-noise ratio. Thus, the proposal of efficient
estimation methodologies for $L$ has been sought. To that end, we consider
firstly that a PolSAR image is well described by the scaled complex Wishart
distribution. In recent years, Anfinsen et al. derived and analyzed estimation
methods based on the ML and on trace statistical moments for obtaining the
parameter $L$ of the unscaled version of such probability law. This paper
generalizes that approach. We present the second-order bias expression proposed
by Cox and Snell for the ML estimator of this parameter. Moreover, the formula
of the profile likelihood modified by Barndorff-Nielsen in terms of $L$ is
discussed. Such derivations yield two new ML estimators for the parameter $L$,
which are compared to the estimators proposed by Anfinsen et al. The
performance of these estimators is assessed by means of Monte Carlo
experiments, adopting three statistical measures as comparison criterion: the
mean square error, the bias, and the coefficient of variation. Equivalently to
the simulation study, an application to actual PolSAR data concludes that the
proposed estimators outperform all the others in homogeneous scenarios.
Proposta di nuovi strumenti per comprendere come funziona la cognizione (Novel tools to understand how cognition works)
Authors: Devis Pantano
Date: 2014-01-07 00:00:00

I think that the main reason why we do not understand the general principles
of how knowledge works (and probably also the reason why we have not yet
designed and built efficient machines capable of artificial intelligence), is
not the excessive complexity of cognitive phenomena, but the lack of the
conceptual and methodological tools to properly address the problem. It is like
trying to build up Physics without the concept of number, or to understand the
origin of species without including the mechanism of natural selection. In this
paper I propose some new conceptual and methodological tools, which seem to
offer a real opportunity to understand the logic of cognitive processes. I
propose a new method to properly treat the concepts of structure and schema,
and to perform on them operations of structural analysis. These operations
allow to move straightforwardly from concrete to more abstract representations.
With these tools I will suggest a definition for the concept of rule, of
regularity and of emergent phenomena. From the analysis of some important
aspects of the rules, I suggest to distinguish them in operational and
associative rules. I propose that associative rules assume a dominant role in
cognition. I also propose a definition for the concept of problem. At the end I
will briefly illustrate a possible general model for cognitive systems.
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
Authors: Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock
Date: 2014-04-18 00:00:00
Tasks: Denoising, Image Compression, Image Denoising

In this paper we study an algorithm for solving a minimization problem
composed of a differentiable (possibly non-convex) and a convex (possibly
non-differentiable) function. The algorithm iPiano combines forward-backward
splitting with an inertial force. It can be seen as a non-smooth split version
of the Heavy-ball method from Polyak. A rigorous analysis of the algorithm for
the proposed class of problems yields global convergence of the function values
and the arguments. This makes the algorithm robust for usage on non-convex
problems. The convergence result is obtained based on the \KL inequality. This
is a very weak restriction, which was used to prove convergence for several
other gradient methods. First, an abstract convergence theorem for a generic
algorithm is proved, and, then iPiano is shown to satisfy the requirements of
this theorem. Furthermore, a convergence rate is established for the general
problem class. We demonstrate iPiano on computer vision problems: image
denoising with learned priors and diffusion based image compression.
OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
Authors: Raj Rao Nadakuditi
Date: 2013-06-25 00:00:00
Tasks: Denoising

The truncated singular value decomposition (SVD) of the measurement matrix is
the optimal solution to the_representation_ problem of how to best approximate
a noisy measurement matrix using a low-rank matrix. Here, we consider the
(unobservable)_denoising_ problem of how to best approximate a low-rank signal
matrix buried in noise by optimal (re)weighting of the singular vectors of the
measurement matrix. We exploit recent results from random matrix theory to
exactly characterize the large matrix limit of the optimal weighting
coefficients and show that they can be computed directly from data for a large
class of noise models that includes the i.i.d. Gaussian noise case.
  Our analysis brings into sharp focus the shrinkage-and-thresholding form of
the optimal weights, the non-convex nature of the associated shrinkage function
(on the singular values) and explains why matrix regularization via singular
value thresholding with convex penalty functions (such as the nuclear norm)
will always be suboptimal. We validate our theoretical predictions with
numerical simulations, develop an implementable algorithm (OptShrink) that
realizes the predicted performance gains and show how our methods can be used
to improve estimation in the setting where the measured matrix has missing
entries.
A new combination approach based on improved evidence distance
Authors: Hongming Mo, Yong Deng
Date: 2014-04-18 00:00:00

Dempster-Shafer evidence theory is a powerful tool in information fusion.
When the evidence are highly conflicting, the counter-intuitive results will be
presented. To adress this open issue, a new method based on evidence distance
of Jousselme and Hausdorff distance is proposed. Weight of each evidence can be
computed, preprocess the original evidence to generate a new evidence. The
Dempster's combination rule is used to combine the new evidence. Comparing with
the existing methods, the new proposed method is efficient.
Ontology as a Source for Rule Generation
Authors: Olegs Verhodubs
Date: 2014-04-18 00:00:00

This paper discloses the potential of OWL (Web Ontology Language) ontologies
for generation of rules. The main purpose of this paper is to identify new
types of rules, which may be generated from OWL ontologies. Rules, generated
from OWL ontologies, are necessary for the functioning of the Semantic Web
Expert System. It is expected that the Semantic Web Expert System (SWES) will
be able to process ontologies from the Web with the purpose to supplement or
even to develop its knowledge base.