pwc-archive/papers-with-abstracts huggingface dataset:
Towards an Error Correction Memory to Enhance Technical Texts Authoring in LELIE
Authors: Juyeon Kang, Patrick Saint Dizier
Date: 2014-06-16 00:00:00

In this paper, we investigate and experiment the notion of error correction
memory applied to error correction in technical texts. The main purpose is to
induce relatively generic correction patterns associated with more contextual
correction recommendations, based on previously memorized and analyzed
corrections. The notion of error correction memory is developed within the
framework of the LELIE project and illustrated on the case of fuzzy lexical
items, which is a major problem in technical texts.
Handling non-compositionality in multilingual CNLs
Authors: Ramona Enache, Inari Listenmaa, Prasanth Kolachina
Date: 2014-06-16 00:00:00
Tasks: Machine Translation, Translation

In this paper, we describe methods for handling multilingual
non-compositional constructions in the framework of GF. We specifically look at
methods to detect and extract non-compositional phrases from parallel texts and
propose methods to handle such constructions in GF grammars. We expect that the
methods to handle non-compositional constructions will enrich CNLs by providing
more flexibility in the design of controlled languages. We look at two specific
use cases of non-compositional constructions: a general-purpose method to
detect and extract multilingual multiword expressions and a procedure to
identify nominal compounds in German. We evaluate our procedure for multiword
expressions by performing a qualitative analysis of the results. For the
experiments on nominal compounds, we incorporate the detected compounds in a
full SMT pipeline and evaluate the impact of our method in machine translation
process.
Translation Of Telugu-Marathi and Vice-Versa using Rule Based Machine Translation
Authors: Siddhartha Ghosh, Sujata Thamke, Kalyani U. R. S
Date: 2014-06-16 00:00:00
Tasks: Machine Translation, Translation

In todays digital world automated Machine Translation of one language to
another has covered a long way to achieve different kinds of success stories.
Whereas Babel Fish supports a good number of foreign languages and only Hindi
from Indian languages, the Google Translator takes care of about 10 Indian
languages. Though most of the Automated Machine Translation Systems are doing
well but handling Indian languages needs a major care while handling the local
proverbs/ idioms. Most of the Machine Translation system follows the direct
translation approach while translating one Indian language to other. Our
research at KMIT R&D Lab found that handling the local proverbs/idioms is not
given enough attention by the earlier research work. This paper focuses on two
of the majorly spoken Indian languages Marathi and Telugu, and translation
between them. Handling proverbs and idioms of both the languages have been
given a special care, and the research outcome shows a significant achievement
in this direction.
A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition
Authors: Jamil Ahmad, Zahoor Jan, Zia-ud-Din, Shoaib Muhammad Khan
Date: 2014-06-16 00:00:00

Retrieving similar images from a large dataset based on the image content has
been a very active research area and is a very challenging task. Studies have
shown that retrieving similar images based on their shape is a very effective
method. For this purpose a large number of methods exist in literature. The
combination of more than one feature has also been investigated for this
purpose and has shown promising results. In this paper a fusion based shapes
recognition method has been proposed. A set of local boundary based and region
based features are derived from the labeled grid based representation of the
shape and are combined with a few global shape features to produce a composite
shape descriptor. This composite shape descriptor is then used in a weighted
ranking algorithm to find similarities among shapes from a large dataset. The
experimental analysis has shown that the proposed method is powerful enough to
discriminate the geometrically similar shapes from the non-similar ones.
Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy Posterior Sampling Algorithm
Authors: Yasin Abbasi-Yadkori, Csaba Szepesvari
Date: 2014-06-16 00:00:00
Tasks: Computational Efficiency

We study Bayesian optimal control of a general class of smoothly
parameterized Markov decision problems. Since computing the optimal control is
computationally expensive, we design an algorithm that trades off performance
for computational efficiency. The algorithm is a lazy posterior sampling method
that maintains a distribution over the unknown parameter. The algorithm changes
its policy only when the variance of the distribution is reduced sufficiently.
Importantly, we analyze the algorithm and show the precise nature of the
performance vs. computation tradeoff. Finally, we show the effectiveness of the
method on a web server control application.
A Bengali HMM Based Speech Synthesis System
Authors: Sankar Mukherjee, Shyamal Kumar Das Mandal
Date: 2014-06-16 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

The paper presents the capability of an HMM-based TTS system to produce
Bengali speech. In this synthesis method, trajectories of speech parameters are
generated from the trained Hidden Markov Models. A final speech waveform is
synthesized from those speech parameters. In our experiments, spectral
properties were represented by Mel Cepstrum Coefficients. Both the training and
synthesis issues are investigated in this paper using annotated Bengali speech
database. Experimental evaluation depicts that the developed text-to-speech
system is capable of producing adequately natural speech in terms of
intelligibility and intonation for Bengali.
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
Authors: Roozbeh Mottaghi, Sanja Fidler, Alan Yuille, Raquel Urtasun, Devi Parikh
Date: 2014-06-16 00:00:00
Tasks: Object, object-detection, Object Detection, Scene Recognition, Scene Understanding, Semantic Segmentation

Recent trends in image understanding have pushed for holistic scene
understanding models that jointly reason about various tasks such as object
detection, scene recognition, shape analysis, contextual reasoning, and local
appearance based classifiers. In this work, we are interested in understanding
the roles of these different tasks in improved scene understanding, in
particular semantic segmentation, object detection and scene recognition.
Towards this goal, we "plug-in" human subjects for each of the various
components in a state-of-the-art conditional random field model. Comparisons
among various hybrid human-machine CRFs give us indications of how much "head
room" there is to improve scene understanding by focusing research efforts on
various individual tasks.
Freeze-Thaw Bayesian Optimization
Authors: Kevin Swersky, Jasper Snoek, Ryan Prescott Adams
Date: 2014-06-16 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

In this paper we develop a dynamic form of Bayesian optimization for machine
learning models with the goal of rapidly finding good hyperparameter settings.
Our method uses the partial information gained during the training of a machine
learning model in order to decide whether to pause training and start a new
model, or resume the training of a previously-considered model. We specifically
tailor our method to machine learning problems by developing a novel
positive-definite covariance kernel to capture a variety of training curves.
Furthermore, we develop a Gaussian process prior that scales gracefully with
additional temporal observations. Finally, we provide an information-theoretic
framework to automate the decision process. Experiments on several common
machine learning models show that our approach is extremely effective in
practice.
The Laplacian K-modes algorithm for clustering
Authors: Weiran Wang, Miguel Á. Carreira-Perpiñán
Date: 2014-06-16 00:00:00
Tasks: Clustering, valid

In addition to finding meaningful clusters, centroid-based clustering
algorithms such as K-means or mean-shift should ideally find centroids that are
valid patterns in the input space, representative of data in their cluster.
This is challenging with data having a nonconvex or manifold structure, as with
images or text. We introduce a new algorithm, Laplacian K-modes, which
naturally combines three powerful ideas in clustering: the explicit use of
assignment variables (as in K-means); the estimation of cluster centroids which
are modes of each cluster's density estimate (as in mean-shift); and the
regularizing effect of the graph Laplacian, which encourages similar
assignments for nearby points (as in spectral clustering). The optimization
algorithm alternates an assignment step, which is a convex quadratic program,
and a mean-shift step, which separates for each cluster centroid. The algorithm
finds meaningful density estimates for each cluster, even with challenging
problems where the clusters have manifold structure, are highly nonconvex or in
high dimension. It also provides centroids that are valid patterns, truly
representative of their cluster (unlike K-means), and an out-of-sample mapping
that predicts soft assignments for a new point.
Tenacious tagging of images via Mellin monomials
Authors: Kieran G. Larkin, Peter A. Fletcher, Stephen J. Hardy
Date: 2012-08-29 00:00:00
Tasks: SSIM

We describe a method for attaching persistent metadata to an image. The
method can be interpreted as a template-based blind watermarking scheme, robust
to common editing operations, namely: cropping, rotation, scaling, stretching,
shearing, compression, printing, scanning, noise, and color removal. Robustness
is achieved through the reciprocity of the embedding and detection invariants.
The embedded patterns are real onedimensional Mellin monomial patterns
distributed over two-dimensions. The embedded patterns are scale invariant and
can be directly embedded in an image by simple pixel addition. Detection
achieves rotation and general affine invariance by signal projection using
implicit Radon transformation. Embedded signals contract to one-dimension in
the two-dimensional Fourier polar domain. The real signals are detected by
correlation with complex Mellin monomial templates. Using a unique template of
4 chirp patterns we detect the affine signature with exquisite sensitivity and
moderate security. The practical implementation achieves efficiencies through
fast Fourier transform (FFT) correspondences such as the projection-slice
theorem, the FFT correlation relation, and fast resampling via the chirp-z
transform. The overall method utilizes orthodox spread spectrum patterns for
the payload and performs well in terms of the classic
robustness-capacity-visibility performance triangle. Tags are entirely
imperceptible with a mean SSIM greater than 0.988 in all cases tested.
Watermarked images survive almost all Stirmark attacks. The method is ideal for
attaching metadata robustly to both digital and analogue images.
Learning An Invariant Speech Representation
Authors: Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang, Lorenzo Rosasco, Tomaso Poggio
Date: 2014-06-16 00:00:00
Tasks: General Classification, Sound Classification, Vowel Classification

Recognition of speech, and in particular the ability to generalize and learn
from small sets of labelled examples like humans do, depends on an appropriate
representation of the acoustic input. We formulate the problem of finding
robust speech features for supervised learning with small sample complexity as
a problem of learning representations of the signal that are maximally
invariant to intraclass transformations and deformations. We propose an
extension of a theory for unsupervised learning of invariant visual
representations to the auditory domain and empirically evaluate its validity
for voiced speech sound classification. Our version of the theory requires the
memory-based, unsupervised storage of acoustic templates -- such as specific
phones or words -- together with all the transformations of each that normally
occur. A quasi-invariant representation for a speech segment can be obtained by
projecting it to each template orbit, i.e., the set of transformed signals, and
computing the associated one-dimensional empirical probability distributions.
The computations can be performed by modules of filtering and pooling, and
extended to hierarchical architectures. In this paper, we apply a single-layer,
multicomponent representation for phonemes and demonstrate improved accuracy
and decreased sample complexity for vowel classification compared to standard
spectral, cepstral and perceptual features.
Human language reveals a universal positivity bias
Authors: Peter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, Christopher M. Danforth
Date: 2014-06-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Translation

Using human evaluation of 100,000 words spread across 24 corpora in 10
languages diverse in origin and culture, we present evidence of a deep imprint
of human sociality in language, observing that (1) the words of natural human
language possess a universal positivity bias; (2) the estimated emotional
content of words is consistent between languages under translation; and (3)
this positivity bias is strongly independent of frequency of word usage.
Alongside these general regularities, we describe inter-language variations in
the emotional spectrum of languages which allow us to rank corpora. We also
show how our word evaluations can be used to construct physical-like
instruments for both real-time and offline measurement of the emotional content
of large-scale texts.
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Authors: Yichuan Zhang, Charles Sutton
Date: 2014-06-15 00:00:00
Proceeding: NeurIPS 2014 12

Sampling from hierarchical Bayesian models is often difficult for MCMC
methods, because of the strong correlations between the model parameters and
the hyperparameters. Recent Riemannian manifold Hamiltonian Monte Carlo (RMHMC)
methods have significant potential advantages in this setting, but are
computationally expensive. We introduce a new RMHMC method, which we call
semi-separable Hamiltonian Monte Carlo, which uses a specially designed mass
matrix that allows the joint Hamiltonian over model parameters and
hyperparameters to decompose into two simpler Hamiltonians. This structure is
exploited by a new integrator which we call the alternating blockwise leapfrog
algorithm. The resulting method can mix faster than simpler Gibbs sampling
while being simpler and more efficient than previous instances of RMHMC.
Optimal Resource Allocation with Semi-Bandit Feedback
Authors: Tor Lattimore, Koby Crammer, Csaba Szepesvári
Date: 2014-06-15 00:00:00

We study a sequential resource allocation problem involving a fixed number of
recurring jobs. At each time-step the manager should distribute available
resources among the jobs in order to maximise the expected number of completed
jobs. Allocating more resources to a given job increases the probability that
it completes, but with a cut-off. Specifically, we assume a linear model where
the probability increases linearly until it equals one, after which allocating
additional resources is wasteful. We assume the difficulty of each job is
unknown and present the first algorithm for this problem and prove upper and
lower bounds on its regret. Despite its apparent simplicity, the problem has a
rich structure: we show that an appropriate optimistic algorithm can improve
its learning speed dramatically beyond the results one normally expects for
similar problems as the problem becomes resource-laden.
An Incremental Reseeding Strategy for Clustering
Authors: Xavier Bresson, Huiyi Hu, Thomas Laurent, Arthur Szlam, James Von Brecht
Date: 2014-06-15 00:00:00
Tasks: Clustering, graph partitioning

In this work we propose a simple and easily parallelizable algorithm for
multiway graph partitioning. The algorithm alternates between three basic
components: diffusing seed vertices over the graph, thresholding the diffused
seeds, and then randomly reseeding the thresholded clusters. We demonstrate
experimentally that the proper combination of these ingredients leads to an
algorithm that achieves state-of-the-art performance in terms of cluster purity
on standard benchmarks datasets. Moreover, the algorithm runs an order of
magnitude faster than the other algorithms that achieve comparable results in
terms of accuracy. We also describe a coarsen, cluster and refine approach
similar to GRACLUS and METIS that removes an additional order of magnitude from
the runtime of our algorithm while still maintaining competitive accuracy.
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
Authors: Misha Denil, Alban Demiraj, Nal Kalchbrenner, Phil Blunsom, Nando de Freitas
Date: 2014-06-15 00:00:00
Tasks: Feature Engineering, Information Retrieval, Retrieval, Sentence

Capturing the compositional process which maps the meaning of words to that
of documents is a central challenge for researchers in Natural Language
Processing and Information Retrieval. We introduce a model that is able to
represent the meaning of documents by embedding them in a low dimensional
vector space, while preserving distinctions of word and sentence order crucial
for capturing nuanced semantics. Our model is based on an extended Dynamic
Convolution Neural Network, which learns convolution filters at both the
sentence and document level, hierarchically learning to capture and compose low
level lexical features into high level semantic concepts. We demonstrate the
effectiveness of this model on a range of document modelling tasks, achieving
strong results with no feature engineering and with a more compact model.
Inspired by recent advances in visualising deep convolution networks for
computer vision, we present a novel visualisation technique for our document
networks which not only provides insight into their learning process, but also
can be interpreted to produce a compelling automatic summarisation system for
texts.
A Heuristic Method to Generate Better Initial Population for Evolutionary Methods
Authors: Erfan Khaji, Amin Satlikh Mohammadi
Date: 2014-06-15 00:00:00
Tasks: Evolutionary Algorithms

Initial population plays an important role in heuristic algorithms such as GA
as it help to decrease the time those algorithms need to achieve an acceptable
result. Furthermore, it may influence the quality of the final answer given by
evolutionary algorithms. In this paper, we shall introduce a heuristic method
to generate a target based initial population which possess two mentioned
characteristics. The efficiency of the proposed method has been shown by
presenting the results of our tests on the benchmarks.
Soccer League Optimization: A heuristic Algorithm Inspired by the Football System in European Countries
Authors: Erfan Khaji
Date: 2014-06-15 00:00:00

In this paper a new heuristic optimization algorithm has been introduced
based on the performance of the major football leagues within each season in EU
countries. The algorithm starts with an initial population including three
different groups of teams: the wealthiest (strongest), the regular, the poorest
(weakest). Each individual of population constitute a football team while each
player is an indication of a player in a post. The optimization can hopefully
occurs when the competition among the teams in all the leagues is imitated as
the strongest teams usually purchase the best players of the regular teams and
in turn, regular teams purchase the best players of the weakest who should
always discover young players instead of buying professionals. It has been
shown that the algorithm can hopefully converge to an acceptable solution
solving various benchmarks. Key words: Heuristic Algorithms
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
Authors: Francesco Orabona
Date: 2014-06-15 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Learning Theory, Model Selection

Stochastic gradient descent algorithms for training linear and kernel
predictors are gaining more and more importance, thanks to their scalability.
While various methods have been proposed to speed up their convergence, the
model selection phase is often ignored. In fact, in theoretical works most of
the time assumptions are made, for example, on the prior knowledge of the norm
of the optimal solution, while in the practical world validation methods remain
the only viable approach. In this paper, we propose a new kernel-based
stochastic gradient descent algorithm that performs model selection while
training, with no parameters to tune, nor any form of cross-validation. The
algorithm builds on recent advancement in online learning theory for
unconstrained settings, to estimate over time the right regularization in a
data-dependent way. Optimal rates of convergence are proved under standard
smoothness assumptions on the target function, using the range space of the
fractional integral operator associated with the kernel.
Investigation of commuting Hamiltonian in quantum Markov network
Authors: Farzad Ghafari Jouneghani, Mohammad Babazadeh, Rogayeh Bayramzadeh, Hossein Movla
Date: 2013-09-25 00:00:00

Graphical Models have various applications in science and engineering which
include physics, bioinformatics, telecommunication and etc. Usage of graphical
models needs complex computations in order to evaluation of marginal
functions,so there are some powerful methods including mean field
approximation, belief propagation algorithm and etc. Quantum graphical models
have been recently developed in context of quantum information and computation,
and quantum statistical physics, which is possible by generalization of
classical probability theory to quantum theory. The main goal of this paper is
preparing a primary generalization of Markov network, as a type of graphical
models, to quantum case and applying in quantum statistical physics.We have
investigated the Markov network and the role of commuting Hamiltonian terms in
conditional independence with simple examples of quantum statistical physics.
Neural tuning size is a key factor underlying holistic face processing
Authors: Cheston Tan, Tomaso Poggio
Date: 2014-06-15 00:00:00
Tasks: Face Recognition

Faces are a class of visual stimuli with unique significance, for a variety
of reasons. They are ubiquitous throughout the course of a person's life, and
face recognition is crucial for daily social interaction. Faces are also unlike
any other stimulus class in terms of certain physical stimulus characteristics.
Furthermore, faces have been empirically found to elicit certain characteristic
behavioral phenomena, which are widely held to be evidence of "holistic"
processing of faces. However, little is known about the neural mechanisms
underlying such holistic face processing. In other words, for the processing of
faces by the primate visual system, the input and output characteristics are
relatively well known, but the internal neural computations are not. The main
aim of this work is to further the fundamental understanding of what causes the
visual processing of faces to be different from that of objects. In this
computational modeling work, we show that a single factor - "neural tuning
size" - is able to account for three key phenomena that are characteristic of
face processing, namely the Composite Face Effect (CFE), Face Inversion Effect
(FIE) and Whole-Part Effect (WPE). Our computational proof-of-principle
provides specific neural tuning properties that correspond to the
poorly-understood notion of holistic face processing, and connects these neural
properties to psychophysical behavior. Overall, our work provides a unified and
parsimonious theoretical account for the disparate empirical data on
face-specific processing, deepening the fundamental understanding of face
processing.