pwc-archive/papers-with-abstracts huggingface dataset:
Multi Terminal Probabilistic Compressed Sensing
Authors: Saeid Haghighatshoar
Date: 2014-01-11 00:00:00
Tasks: compressed sensing

In this paper, the `Approximate Message Passing' (AMP) algorithm, initially
developed for compressed sensing of signals under i.i.d. Gaussian measurement
matrices, has been extended to a multi-terminal setting (MAMP algorithm). It
has been shown that similar to its single terminal counterpart, the behavior of
MAMP algorithm is fully characterized by a `State Evolution' (SE) equation for
large block-lengths. This equation has been used to obtain the rate-distortion
curve of a multi-terminal memoryless source. It is observed that by spatially
coupling the measurement matrices, the rate-distortion curve of MAMP algorithm
undergoes a phase transition, where the measurement rate region corresponding
to a low distortion (approximately zero distortion) regime is fully
characterized by the joint and conditional Renyi information dimension (RID) of
the multi-terminal source. This measurement rate region is very similar to the
rate region of the Slepian-Wolf distributed source coding problem where the RID
plays a role similar to the discrete entropy.
  Simulations have been done to investigate the empirical behavior of MAMP
algorithm. It is observed that simulation results match very well with
predictions of SE equation for reasonably large block-lengths.
A Deep and Tractable Density Estimator
Authors: Benigno Uria, Iain Murray, Hugo Larochelle
Date: 2013-10-07 00:00:00
Tasks: Density Estimation, Image Generation

The Neural Autoregressive Distribution Estimator (NADE) and its real-valued
version RNADE are competitive density models of multidimensional data across a
variety of domains. These models use a fixed, arbitrary ordering of the data
dimensions. One can easily condition on variables at the beginning of the
ordering, and marginalize out variables at the end of the ordering, however
other inference tasks require approximate inference. In this work we introduce
an efficient procedure to simultaneously train a NADE model for each possible
ordering of the variables, by sharing parameters across all these models. We
can thus use the most convenient model for each inference task at hand, and
ensembles of such models with different orderings are immediately available.
Moreover, unlike the original NADE, our training procedure scales to deep
models. Empirically, ensembles of Deep NADE models obtain state of the art
density estimation performance.
The semantic similarity ensemble
Authors: Andrea Ballatore, Michela Bertolotto, David C. Wilson
Date: 2014-01-11 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

Computational measures of semantic similarity between geographic terms
provide valuable support across geographic information retrieval, data mining,
and information integration. To date, a wide variety of approaches to
geo-semantic similarity have been devised. A judgment of similarity is not
intrinsically right or wrong, but obtains a certain degree of cognitive
plausibility, depending on how closely it mimics human behavior. Thus selecting
the most appropriate measure for a specific task is a significant challenge. To
address this issue, we make an analogy between computational similarity
measures and soliciting domain expert opinions, which incorporate a subjective
set of beliefs, perceptions, hypotheses, and epistemic biases. Following this
analogy, we define the semantic similarity ensemble (SSE) as a composition of
different similarity measures, acting as a panel of experts having to reach a
decision on the semantic similarity of a set of geographic terms. The approach
is evaluated in comparison to human judgments, and results indicate that an SSE
performs better than the average of its parts. Although the best member tends
to outperform the ensemble, all ensembles outperform the average performance of
each ensemble's member. Hence, in contexts where the best measure is unknown,
the ensemble provides a more cognitively plausible approach.
Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression
Authors: Yukun Bao, Tao Xiong, Zhongyi Hu
Date: 2014-01-11 00:00:00
Tasks: Prediction, regression, Time Series, Time Series Analysis, Time Series Prediction

Accurate time series prediction over long future horizons is challenging and
of great interest to both practitioners and academics. As a well-known
intelligent algorithm, the standard formulation of Support Vector Regression
(SVR) could be taken for multi-step-ahead time series prediction, only relying
either on iterated strategy or direct strategy. This study proposes a novel
multiple-step-ahead time series prediction approach which employs
multiple-output support vector regression (M-SVR) with multiple-input
multiple-output (MIMO) prediction strategy. In addition, the rank of three
leading prediction strategies with SVR is comparatively examined, providing
practical implications on the selection of the prediction strategy for
multi-step-ahead forecasting while taking SVR as modeling technique. The
proposed approach is validated with the simulated and real datasets. The
quantitative and comprehensive assessments are performed on the basis of the
prediction accuracy and computational cost. The results indicate that: 1) the
M-SVR using MIMO strategy achieves the best accurate forecasts with accredited
computational load, 2) the standard SVR using direct strategy achieves the
second best accurate forecasts, but with the most expensive computational cost,
and 3) the standard SVR using iterated strategy is the worst in terms of
prediction accuracy, but with the least computational cost.
Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences
Authors: Tao Xiong, Yukun Bao, Zhongyi Hu
Date: 2014-01-11 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Following the "decomposition-and-ensemble" principle, the empirical mode
decomposition (EMD)-based modeling framework has been widely used as a
promising alternative for nonlinear and nonstationary time series modeling and
prediction. The end effect, which occurs during the sifting process of EMD and
is apt to distort the decomposed sub-series and hurt the modeling process
followed, however, has been ignored in previous studies. Addressing the end
effect issue, this study proposes to incorporate end condition methods into
EMD-based decomposition and ensemble modeling framework for one- and multi-step
ahead time series prediction. Four well-established end condition methods,
Mirror method, Coughlin's method, Slope-based method, and Rato's method, are
selected, and support vector regression (SVR) is employed as the modeling
technique. For the purpose of justification and comparison, well-known NN3
competition data sets are used and four well-established prediction models are
selected as benchmarks. The experimental results demonstrated that significant
improvement can be achieved by the proposed EMD-based SVR models with end
condition methods. The EMD-SBM-SVR model and EMD-Rato-SVR model, in particular,
achieved the best prediction performances in terms of goodness of forecast
measures and equality of accuracy of competing forecasts test.
Multiscale Shrinkage and Lévy Processes
Authors: Xin Yuan, Vinayak Rao, Shaobo Han, Lawrence Carin
Date: 2014-01-11 00:00:00
Tasks: Bayesian Inference, Compressive Sensing, Denoising

A new shrinkage-based construction is developed for a compressible vector
$\boldsymbol{x}\in\mathbb{R}^n$, for cases in which the components of $\xv$ are
naturally associated with a tree structure. Important examples are when $\xv$
corresponds to the coefficients of a wavelet or block-DCT representation of
data. The method we consider in detail, and for which numerical results are
presented, is based on increments of a gamma process. However, we demonstrate
that the general framework is appropriate for many other types of shrinkage
priors, all within the L\'{e}vy process family, with the gamma process a
special case. Bayesian inference is carried out by approximating the posterior
with samples from an MCMC algorithm, as well as by constructing a heuristic
variational approximation to the posterior. We also consider
expectation-maximization (EM) for a MAP (point) solution. State-of-the-art
results are manifested for compressive sensing and denoising applications, the
latter with spiky (non-Gaussian) noise.
An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models
Authors: Sinan Yildirim, A. Taylan Cemgil, Sumeetpal S. Singh
Date: 2014-01-11 00:00:00

In this paper we formulate the nonnegative matrix factorisation (NMF) problem
as a maximum likelihood estimation problem for hidden Markov models and propose
online expectation-maximisation (EM) algorithms to estimate the NMF and the
other unknown static parameters. We also propose a sequential Monte Carlo
approximation of our online EM algorithm. We show the performance of the
proposed method with two numerical examples.
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape
Authors: José I. Ronda, Antonio Valdés, Guillermo Gallego
Date: 2012-03-05 00:00:00
Tasks: 3D Reconstruction

In 3D reconstruction, the recovery of the calibration parameters of the
cameras is paramount since it provides metric information about the observed
scene, e.g., measures of angles and ratios of distances. Autocalibration
enables the estimation of the camera parameters without using a calibration
device, but by enforcing simple constraints on the camera parameters. In the
absence of information about the internal camera parameters such as the focal
length and the principal point, the knowledge of the camera pixel shape is
usually the only available constraint. Given a projective reconstruction of a
rigid scene, we address the problem of the autocalibration of a minimal set of
cameras with known pixel shape and otherwise arbitrarily varying intrinsic and
extrinsic parameters. We propose an algorithm that only requires 5 cameras (the
theoretical minimum), thus halving the number of cameras required by previous
algorithms based on the same constraint. To this purpose, we introduce as our
basic geometric tool the six-line conic variety (SLCV), consisting in the set
of planes intersecting six given lines of 3D space in points of a conic. We
show that the set of solutions of the Euclidean upgrading problem for three
cameras with known pixel shape can be parameterized in a computationally
efficient way. This parameterization is then used to solve autocalibration from
five or more cameras, reducing the three-dimensional search space to a
two-dimensional one. We provide experiments with real images showing the good
performance of the technique.
Dempster-Shafer Theory for Move Prediction in Start Kicking of The Bicycle Kick of Sepak Takraw Game
Authors: Andino Maseleno, Md. Mahmud Hasan
Date: 2014-01-10 00:00:00

This paper presents Dempster-Shafer theory for move prediction in start
kicking of the bicycle kick of sepak takraw game. Sepak takraw is a highly
complex net-barrier kicking sport that involves dazzling displays of quick
reflexes, acrobatic twists, turns and swerves of the agile human body movement.
A Bicycle kick or Scissor kick is a physical move made by throwing the body up
into the air, making a shearing movement with the legs to get one leg in front
of the other without holding on to the ground. Specifically, this paper
considers bicycle kick of sepak takraw game in start kicking of the ball with
uncertainty where player has different awareness regarding the contingencies.
We have chosen Dempster-Shafer theory because the advantages of the
Dempster-Shafer theory which include the ability to model information in a
flexible way without requiring a probability to be assigned to each element in
a set, providing a convenient and simple mechanism for combining two or more
pieces of evidence under certain conditions, it can model ignorance explicitly,
rejection of the law of additivity for belief in disjoint propositions.
STIMONT: A core ontology for multimedia stimuli description
Authors: Marko Horvat, Nikola Bogunović, Krešimir Ćosić
Date: 2014-01-10 00:00:00
Tasks: Retrieval

Affective multimedia documents such as images, sounds or videos elicit
emotional responses in exposed human subjects. These stimuli are stored in
affective multimedia databases and successfully used for a wide variety of
research in psychology and neuroscience in areas related to attention and
emotion processing. Although important all affective multimedia databases have
numerous deficiencies which impair their applicability. These problems, which
are brought forward in the paper, result in low recall and precision of
multimedia stimuli retrieval which makes creating emotion elicitation
procedures difficult and labor-intensive. To address these issues a new core
ontology STIMONT is introduced. The STIMONT is written in OWL-DL formalism and
extends W3C EmotionML format with an expressive and formal representation of
affective concepts, high-level semantics, stimuli document metadata and the
elicited physiology. The advantages of ontology in description of affective
multimedia stimuli are demonstrated in a document retrieval experiment and
compared against contemporary keyword-based querying methods. Also, a software
tool Intelligent Stimulus Generator for retrieval of affective multimedia and
construction of stimuli sequences is presented.
Learning Paired-associate Images with An Unsupervised Deep Learning Architecture
Authors: Ti Wang, Daniel L. Silver
Date: 2013-12-20 00:00:00
Tasks: Deep Learning

This paper presents an unsupervised multi-modal learning system that learns
associative representation from two input modalities, or channels, such that
input on one channel will correctly generate the associated response at the
other and vice versa. In this way, the system develops a kind of supervised
classification model meant to simulate aspects of human associative memory. The
system uses a deep learning architecture (DLA) composed of two input/output
channels formed from stacked Restricted Boltzmann Machines (RBM) and an
associative memory network that combines the two channels. The DLA is trained
on pairs of MNIST handwritten digit images to develop hierarchical features and
associative representations that are able to reconstruct one image given its
paired-associate. Experiments show that the multi-modal learning system
generates models that are as accurate as back-propagation networks but with the
advantage of a bi-directional network and unsupervised learning from either
paired or non-paired training examples.
Transformation-based Feature Computation for Algorithm Portfolios
Authors: Barry Hurley, Serdar Kadioglu, Yuri Malitsky, Barry O'Sullivan
Date: 2014-01-10 00:00:00

Instance-specific algorithm configuration and algorithm portfolios have been
shown to offer significant improvements over single algorithm approaches in a
variety of application domains. In the SAT and CSP domains algorithm portfolios
have consistently dominated the main competitions in these fields for the past
five years. For a portfolio approach to be effective there are two crucial
conditions that must be met. First, there needs to be a collection of
complementary solvers with which to make a portfolio. Second, there must be a
collection of problem features that can accurately identify structural
differences between instances. This paper focuses on the latter issue: feature
representation, because, unlike SAT, not every problem has well-studied
features. We employ the well-known SATzilla feature set, but compute
alternative sets on different SAT encodings of CSPs. We show that regardless of
what encoding is used to convert the instances, adequate structural information
is maintained to differentiate between problem instances, and that this can be
exploited to make an effective portfolio-based CSP solver.
N2Sky - Neural Networks as Services in the Clouds
Authors: Erich Schikuta, Erwin Mann
Date: 2014-01-10 00:00:00
Tasks: Neural Network simulation

We present the N2Sky system, which provides a framework for the exchange of
neural network specific knowledge, as neural network paradigms and objects, by
a virtual organization environment. It follows the sky computing paradigm
delivering ample resources by the usage of federated Clouds. N2Sky is a novel
Cloud-based neural network simulation environment, which follows a pure service
oriented approach. The system implements a transparent environment aiming to
enable both novice and experienced users to do neural network research easily
and comfortably. N2Sky is built using the RAVO reference architecture of
virtual organizations which allows itself naturally integrating into the Cloud
service stack (SaaS, PaaS, and IaaS) of service oriented architectures.
Online Matrix Completion Through Nuclear Norm Regularisation
Authors: Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon
Date: 2014-01-10 00:00:00
Tasks: Matrix Completion, Recommendation Systems

It is the main goal of this paper to propose a novel method to perform matrix
completion on-line. Motivated by a wide variety of applications, ranging from
the design of recommender systems to sensor network localization through
seismic data reconstruction, we consider the matrix completion problem when
entries of the matrix of interest are observed gradually. Precisely, we place
ourselves in the situation where the predictive rule should be refined
incrementally, rather than recomputed from scratch each time the sample of
observed entries increases. The extension of existing matrix completion methods
to the sequential prediction context is indeed a major issue in the Big Data
era, and yet little addressed in the literature. The algorithm promoted in this
article builds upon the Soft Impute approach introduced in Mazumder et al.
(2010). The major novelty essentially arises from the use of a randomised
technique for both computing and updating the Singular Value Decomposition
(SVD) involved in the algorithm. Though of disarming simplicity, the method
proposed turns out to be very efficient, while requiring reduced computations.
Several numerical experiments based on real datasets illustrating its
performance are displayed, together with preliminary results giving it a
theoretical basis.
Satellite image classification and segmentation using non-additive entropy
Authors: Lucas Assirati, Alexandre Souto Martinez, Odemir Martinez Bruno
Date: 2014-01-10 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Satellite Image Classification, Segmentation

Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis
entropy on the pattern recognition and segmentation of coloured images obtained
by satellites, via "Google Earth". By segmentation we mean split an image to
locate regions of interest. Here, we discriminate and define an image partition
classes according to a training basis. This training basis consists of three
pattern classes: aquatic, urban and vegetation regions. Our numerical
experiments demonstrate that the Tsallis entropy, used as a feature vector
composed of distinct entropic indexes $q$ outperforms the standard entropy.
There are several applications of our proposed methodology, once satellite
images can be used to monitor migration form rural to urban regions,
agricultural activities, oil spreading on the ocean etc.
Exploiting generalisation symmetries in accuracy-based learning classifier systems: An initial study
Authors: Larry Bull
Date: 2014-01-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Modern learning classifier systems typically exploit a niched genetic
algorithm to facilitate rule discovery. When used for reinforcement learning,
such rules represent generalisations over the state-action-reward space. Whilst
encouraging maximal generality, the niching can potentially hinder the
formation of generalisations in the state space which are symmetrical, or very
similar, over different actions. This paper introduces the use of rules which
contain multiple actions, maintaining accuracy and reward metrics for each
action. It is shown that problem symmetries can be exploited, improving
performance, whilst not degrading performance when symmetries are reduced.
Lasso and equivalent quadratic penalized models
Authors: Stefan Hummelsheim
Date: 2014-01-10 00:00:00
Tasks: regression

The least absolute shrinkage and selection operator (lasso) and ridge
regression produce usually different estimates although input, loss function
and parameterization of the penalty are identical. In this paper we look for
ridge and lasso models with identical solution set.
  It turns out, that the lasso model with shrink vector $\lambda$ and a
quadratic penalized model with shrink matrix as outer product of $\lambda$ with
itself are equivalent, in the sense that they have equal solutions. To achieve
this, we have to restrict the estimates to be positive. This doesn't limit the
area of application since we can easily decompose every estimate in a positive
and negative part. The resulting problem can be solved with a non negative
least square algorithm.
  Beside this quadratic penalized model, an augmented regression model with
positive bounded estimates is developed which is also equivalent to the lasso
model, but is probably faster to solve.
Assessing Wikipedia-Based Cross-Language Retrieval Models
Authors: Benjamin Roth
Date: 2014-01-10 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Retrieval, Translation

This work compares concept models for cross-language retrieval: First, we
adapt probabilistic Latent Semantic Analysis (pLSA) for multilingual documents.
Experiments with different weighting schemes show that a weighting method
favoring documents of similar length in both language sides gives best results.
Considering that both monolingual and multilingual Latent Dirichlet Allocation
(LDA) behave alike when applied for such documents, we use a training corpus
built on Wikipedia where all documents are length-normalized and obtain
improvements over previously reported scores for LDA. Another focus of our work
is on model combination. For this end we include Explicit Semantic Analysis
(ESA) in the experiments. We observe that ESA is not competitive with LDA in a
query based retrieval task on CLEF 2000 data. The combination of machine
translation with concept models increased performance by 21.1% map in
comparison to machine translation alone. Machine translation relies on parallel
corpora, which may not be available for many language pairs. We further explore
how much cross-lingual information can be carried over by a specific
information source in Wikipedia, namely linked text. The best results are
obtained using a language modeling approach, entirely without information from
parallel corpora. The need for smoothing raises interesting questions on
soundness and efficiency. Link models capture only a certain kind of
information and suggest weighting schemes to emphasize particular words. For a
combined model, another interesting question is therefore how to integrate
different weighting schemes. Using a very simple combination scheme, we obtain
results that compare favorably to previously reported results on the CLEF 2000
dataset.