pwc-archive/papers-with-abstracts huggingface dataset:
Performance of the Survey Propagation-guided decimation algorithm for the random NAE-K-SAT problem
Authors: David Gamarnik, Madhu Sudan
Date: 2014-02-01 00:00:00
Tasks: Clustering

We show that the Survey Propagation-guided decimation algorithm fails to find
satisfying assignments on random instances of the "Not-All-Equal-$K$-SAT"
problem if the number of message passing iterations is bounded by a constant
independent of the size of the instance and the clause-to-variable ratio is
above $(1+o_K(1)){2^{K-1}\over K}\log^2 K$ for sufficiently large $K$. Our
analysis in fact applies to a broad class of algorithms described as
"sequential local algorithms". Such algorithms iteratively set variables based
on some local information and then recurse on the reduced instance. Survey
Propagation-guided as well as Belief Propagation-guided decimation algorithms -
two widely studied message passing based algorithms, fall under this category
of algorithms provided the number of message passing iterations is bounded by a
constant. Another well-known algorithm falling into this category is the Unit
Clause algorithm. Our work constitutes the first rigorous analysis of the
performance of the SP-guided decimation algorithm.
  The approach underlying our paper is based on an intricate geometry of the
solution space of random NAE-$K$-SAT problem. We show that above the
$(1+o_K(1)){2^{K-1}\over K}\log^2 K$ threshold, the overlap structure of
$m$-tuples of satisfying assignments exhibit a certain clustering behavior
expressed in the form of constraints on distances between the $m$ assignments,
for appropriately chosen $m$. We further show that if a sequential local
algorithm succeeds in finding a satisfying assignment with probability bounded
away from zero, then one can construct an $m$-tuple of solutions violating
these constraints, thus leading to a contradiction. Along with (citation), this
result is the first work which directly links the clustering property of random
constraint satisfaction problems to the computational hardness of finding
satisfying assignments.
Computational Beauty: Aesthetic Judgment at the Intersection of Art and Science
Authors: Emily L. Spratt, Ahmed Elgammal
Date: 2014-09-30 00:00:00

In part one of the Critique of Judgment, Immanuel Kant wrote that "the
judgment of taste...is not a cognitive judgment, and so not logical, but is
aesthetic."\cite{Kant} While the condition of aesthetic discernment has long
been the subject of philosophical discourse, the role of the arbiters of that
judgment has more often been assumed than questioned. The art historian,
critic, connoisseur, and curator have long held the esteemed position of the
aesthetic judge, their training, instinct, and eye part of the inimitable
subjective processes that Kant described as occurring upon artistic evaluation.
Although the concept of intangible knowledge in regard to aesthetic theory has
been much explored, little discussion has arisen in response to the development
of new types of artificial intelligence as a challenge to the seemingly
ineffable abilities of the human observer. This paper examines the developments
in the field of computer vision analysis of paintings from canonical movements
with the history of Western art and the reaction of art historians to the
application of this technology in the field. Through an investigation of the
ethical consequences of this innovative technology, the unquestioned authority
of the art expert is challenged and the subjective nature of aesthetic judgment
is brought to philosophical scrutiny once again.
A Novel Scheme for Intelligent Recognition of Pornographic Images
Authors: Seyed Mostafa Kia, Hossein Rahmani, Reza Mortezaei, Mohsen Ebrahimi Moghaddam, Amer Namazi
Date: 2014-02-24 00:00:00

Harmful contents are rising in internet day by day and this motivates the
essence of more research in fast and reliable obscene and immoral material
filtering. Pornographic image recognition is an important component in each
filtering system. In this paper, a new approach for detecting pornographic
images is introduced. In this approach, two new features are suggested. These
two features in combination with other simple traditional features provide
decent difference between porn and non-porn images. In addition, we applied
fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron)
and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of
system was evaluated over 18354 download images from internet. The attained
precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on
test dataset. Achieved results verify the performance of proposed system versus
other related works.
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial
Authors: Yarin Gal, Mark van der Wilk
Date: 2014-02-06 00:00:00
Tasks: Gaussian Processes, regression, Variational Inference

In this tutorial we explain the inference procedures developed for the sparse
Gaussian process (GP) regression and Gaussian process latent variable model
(GPLVM). Due to page limit the derivation given in Titsias (2009) and Titsias &
Lawrence (2010) is brief, hence getting a full picture of it requires
collecting results from several different sources and a substantial amount of
algebra to fill-in the gaps. Our main goal is thus to collect all the results
and full derivations into one place to help speed up understanding this work.
In doing so we present a re-parametrisation of the inference that allows it to
be carried out in parallel. A secondary goal for this document is, therefore,
to accompany our paper and open-source implementation of the parallel inference
scheme for the models. We hope that this document will bridge the gap between
the equations as implemented in code and those published in the original
papers, in order to make it easier to extend existing work. We assume prior
knowledge of Gaussian processes and variational inference, but we also include
references for further reading where appropriate.
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Authors: Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
Date: 2014-02-06 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Dimensionality Reduction, Gaussian Processes, regression, Variational Inference

Gaussian processes (GPs) are a powerful tool for probabilistic inference over
functions. They have been applied to both regression and non-linear
dimensionality reduction, and offer desirable properties such as uncertainty
estimates, robustness to over-fitting, and principled ways for tuning
hyper-parameters. However the scalability of these models to big datasets
remains an active topic of research. We introduce a novel re-parametrisation of
variational inference for sparse GP regression and latent variable models that
allows for an efficient distributed algorithm. This is done by exploiting the
decoupling of the data given the inducing points to re-formulate the evidence
lower bound in a Map-Reduce setting. We show that the inference scales well
with data and computational resources, while preserving a balanced distribution
of the load among the nodes. We further demonstrate the utility in scaling
Gaussian processes to big data. We show that GP performance improves with
increasing amounts of data in regression (on flight data with 2 million
records) and latent variable modelling (on MNIST). The results show that GPs
perform better than many common models often used for big data.
Bayesian and regularization approaches to multivariable linear system identification: the role of rank penalties
Authors: Giulia Prando, Alessandro Chiuso, Gianluigi Pillonetto
Date: 2014-09-29 00:00:00

Recent developments in linear system identification have proposed the use of
non-parameteric methods, relying on regularization strategies, to handle the
so-called bias/variance trade-off. This paper introduces an impulse response
estimator which relies on an $\ell_2$-type regularization including a
rank-penalty derived using the log-det heuristic as a smooth approximation to
the rank function. This allows to account for different properties of the
estimated impulse response (e.g. smoothness and stability) while also
penalizing high-complexity models. This also allows to account and enforce
coupling between different input-output channels in MIMO systems. According to
the Bayesian paradigm, the parameters defining the relative weight of the two
regularization terms as well as the structure of the rank penalty are estimated
optimizing the marginal likelihood. Once these hyperameters have been
estimated, the impulse response estimate is available in closed form.
Experiments show that the proposed method is superior to the estimator relying
on the "classic" $\ell_2$-regularization alone as well as those based in atomic
and nuclear norm.
Arabic Spelling Correction using Supervised Learning
Authors: Youssef Hassan, Mohamed Aly, Amir Atiya
Date: 2014-09-29 00:00:00
Proceeding: WS 2014 10
Tasks: Spelling Correction

In this work, we address the problem of spelling correction in the Arabic
language utilizing the new corpus provided by QALB (Qatar Arabic Language Bank)
project which is an annotated corpus of sentences with errors and their
corrections. The corpus contains edit, add before, split, merge, add after,
move and other error types. We are concerned with the first four error types as
they contribute more than 90% of the spelling errors in the corpus. The
proposed system has many models to address each error type on its own and then
integrating all the models to provide an efficient and robust system that
achieves an overall recall of 0.59, precision of 0.58 and F1 score of 0.58
including all the error types on the development set. Our system participated
in the QALB 2014 shared task "Automatic Arabic Error Correction" and achieved
an F1 score of 0.6, earning the sixth place out of nine participants.
Short-Term Predictability of Photovoltaic Production over Italy
Authors: Matteo De Felice, Marcello Petitta, Paolo M. Ruti
Date: 2014-09-29 00:00:00
Tasks: Management

Photovoltaic (PV) power production increased drastically in Europe throughout
the last years. About the 6% of electricity in Italy comes from PV and for an
efficient management of the power grid an accurate and reliable forecasting of
production would be needed. Starting from a dataset of electricity production
of 65 Italian solar plants for the years 2011-2012 we investigate the
possibility to forecast daily production from one to ten days of lead time
without using on site measurements. Our study is divided in two parts: an
assessment of the predictability of meteorological variables using weather
forecasts and an analysis on the application of data-driven modelling in
predicting solar power production. We calibrate a SVM model using available
observations and then we force the same model with the predicted variables from
weather forecasts with a lead time from one to ten days. As expected, solar
power production is strongly influenced by cloudiness and clear sky, in fact we
observe that while during summer we obtain a general error under the 10%
(slightly lower in south Italy), during winter the error is abundantly above
the 20%.
Freshness-Aware Thompson Sampling
Authors: Djallel Bouneffouf
Date: 2014-09-29 00:00:00
Tasks: Recommendation Systems, Thompson Sampling

To follow the dynamicity of the user's content, researchers have recently
started to model interactions between users and the Context-Aware Recommender
Systems (CARS) as a bandit problem where the system needs to deal with
exploration and exploitation dilemma. In this sense, we propose to study the
freshness of the user's content in CARS through the bandit problem. We
introduce in this paper an algorithm named Freshness-Aware Thompson Sampling
(FA-TS) that manages the recommendation of fresh document according to the
user's risk of the situation. The intensive evaluation and the detailed
analysis of the experimental results reveals several important discoveries in
the exploration/exploitation (exr/exp) behaviour.
A Neural Networks Committee for the Contextual Bandit Problem
Authors: Robin Allesiardo, Raphael Feraud, Djallel Bouneffouf
Date: 2014-09-29 00:00:00

This paper presents a new contextual bandit algorithm, NeuralBandit, which
does not need hypothesis on stationarity of contexts and rewards. Several
neural networks are trained to modelize the value of rewards knowing the
context. Two variants, based on multi-experts approach, are proposed to choose
online the parameters of multi-layer perceptrons. The proposed algorithms are
successfully tested on a large dataset with and without stationarity of
rewards.
Communication-Efficient Distributed Dual Coordinate Ascent
Authors: Martin Jaggi, Virginia Smith, Martin Takáč, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael. I. Jordan
Date: 2014-09-04 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: BIG-bench Machine Learning, Distributed Optimization

Communication remains the most significant bottleneck in the performance of
distributed optimization algorithms for large-scale machine learning. In this
paper, we propose a communication-efficient framework, CoCoA, that uses local
computation in a primal-dual setting to dramatically reduce the amount of
necessary communication. We provide a strong convergence rate analysis for this
class of algorithms, as well as experiments on real-world distributed datasets
with implementations in Spark. In our experiments, we find that as compared to
state-of-the-art mini-batch versions of SGD and SDCA algorithms, CoCoA
converges to the same .001-accurate solution quality on average 25x as quickly.
Random forests with random projections of the output space for high dimensional multi-label classification
Authors: Arnaud Joly, Pierre Geurts, Louis Wehenkel
Date: 2014-04-14 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

We adapt the idea of random projections applied to the output space, so as to
enhance tree-based ensemble methods in the context of multi-label
classification. We show how learning time complexity can be reduced without
affecting computational complexity and accuracy of predictions. We also show
that random output space projections may be used in order to reach different
bias-variance tradeoffs, over a broad panel of benchmark problems, and that
this may lead to improved accuracy while reducing significantly the
computational burden of the learning stage.
Controversy and Sentiment in Online News
Authors: Yelena Mejova, Amy X. Zhang, Nicholas Diakopoulos, Carlos Castillo
Date: 2014-09-29 00:00:00
Tasks: Articles

How do news sources tackle controversial issues? In this work, we take a
data-driven approach to understand how controversy interplays with emotional
expression and biased language in the news. We begin by introducing a new
dataset of controversial and non-controversial terms collected using
crowdsourcing. Then, focusing on 15 major U.S. news outlets, we compare
millions of articles discussing controversial and non-controversial issues over
a span of 7 months. We find that in general, when it comes to controversial
issues, the use of negative affect and biased language is prevalent, while the
use of strong emotion is tempered. We also observe many differences across news
sources. Using these findings, we show that we can indicate to what extent an
issue is controversial, by comparing it with other issues in terms of how they
are portrayed across different media.
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction
Authors: Beyza Ermis, A. Taylan Cemgil
Date: 2014-09-29 00:00:00
Tasks: Bayesian Inference, Link Prediction, Prediction, Variational Inference

Probabilistic approaches for tensor factorization aim to extract meaningful
structure from incomplete data by postulating low rank constraints. Recently,
variational Bayesian (VB) inference techniques have successfully been applied
to large scale models. This paper presents full Bayesian inference via VB on
both single and coupled tensor factorization models. Our method can be run even
for very large models and is easily implemented. It exhibits better prediction
performance than existing approaches based on maximum likelihood on several
real-world datasets for missing link prediction problem.
Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search
Authors: J. Gerard Wolff
Date: 2014-09-29 00:00:00
Tasks: Diagnostic, Medical Diagnosis

This paper describes a novel approach to medical diagnosis based on the SP
theory of computing and cognition. The main attractions of this approach are: a
format for representing diseases that is simple and intuitive; an ability to
cope with errors and uncertainties in diagnostic information; the simplicity of
storing statistical information as frequencies of occurrence of diseases; a
method for evaluating alternative diagnostic hypotheses that yields true
probabilities; and a framework that should facilitate unsupervised learning of
medical knowledge and the integration of medical diagnosis with other AI
applications.
Robustness and Generalization for Metric Learning
Authors: Aurélien Bellet, Amaury Habrard
Date: 2012-09-05 00:00:00
Tasks: Generalization Bounds, Metric Learning

Metric learning has attracted a lot of interest over the last decade, but the
generalization ability of such methods has not been thoroughly studied. In this
paper, we introduce an adaptation of the notion of algorithmic robustness
(previously introduced by Xu and Mannor) that can be used to derive
generalization bounds for metric learning. We further show that a weak notion
of robustness is in fact a necessary and sufficient condition for a metric
learning algorithm to generalize. To illustrate the applicability of the
proposed framework, we derive generalization results for a large family of
existing metric learning algorithms, including some sparse formulations that
are not covered by previous results.
CRF-based Named Entity Recognition @ICON 2013
Authors: Arjun Das, Utpal Garain
Date: 2014-09-29 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper describes performance of CRF based systems for Named Entity
Recognition (NER) in Indian language as a part of ICON 2013 shared task. In
this task we have considered a set of language independent features for all the
languages. Only for English a language specific feature, i.e. capitalization,
has been added. Next the use of gazetteer is explored for Bengali, Hindi and
English. The gazetteers are built from Wikipedia and other sources. Test
results show that the system achieves the highest F measure of 88% for English
and the lowest F measure of 69% for both Tamil and Telugu. Note that for the
least performing two languages no gazetteer was used. NER in Bengali and Hindi
finds accuracy (F measure) of 87% and 79%, respectively.
Improving the Performance of English-Tamil Statistical Machine Translation System using Source-Side Pre-Processing
Authors: M. Anand Kumar, V. Dhanalakshmi, K. P. Soman, V. Sharmiladevi
Date: 2014-09-29 00:00:00
Tasks: Machine Translation, Sentence, Translation

Machine Translation is one of the major oldest and the most active research
area in Natural Language Processing. Currently, Statistical Machine Translation
(SMT) dominates the Machine Translation research. Statistical Machine
Translation is an approach to Machine Translation which uses models to learn
translation patterns directly from data, and generalize them to translate a new
unseen text. The SMT approach is largely language independent, i.e. the models
can be applied to any language pair. Statistical Machine Translation (SMT)
attempts to generate translations using statistical methods based on bilingual
text corpora. Where such corpora are available, excellent results can be
attained translating similar texts, but such corpora are still not available
for many language pairs. Statistical Machine Translation systems, in general,
have difficulty in handling the morphology on the source or the target side
especially for morphologically rich languages. Errors in morphology or syntax
in the target language can have severe consequences on meaning of the sentence.
They change the grammatical function of words or the understanding of the
sentence through the incorrect tense information in verb. Baseline SMT also
known as Phrase Based Statistical Machine Translation (PBSMT) system does not
use any linguistic information and it only operates on surface word form.
Recent researches shown that adding linguistic information helps to improve the
accuracy of the translation with less amount of bilingual corpora. Adding
linguistic information can be done using the Factored Statistical Machine
Translation system through pre-processing steps. This paper investigates about
how English side pre-processing is used to improve the accuracy of
English-Tamil SMT system.
Sequential Complexity as a Descriptor for Musical Similarity
Authors: Peter Foster, Matthias Mauch, Simon Dixon
Date: 2014-02-27 00:00:00
Tasks: Prediction

We propose string compressibility as a descriptor of temporal structure in
audio, for the purpose of determining musical similarity. Our descriptors are
based on computing track-wise compression rates of quantised audio features,
using multiple temporal resolutions and quantisation granularities. To verify
that our descriptors capture musically relevant information, we incorporate our
descriptors into similarity rating prediction and song year prediction tasks.
We base our evaluation on a dataset of 15500 track excerpts of Western popular
music, for which we obtain 7800 web-sourced pairwise similarity ratings. To
assess the agreement among similarity ratings, we perform an evaluation under
controlled conditions, obtaining a rank correlation of 0.33 between intersected
sets of ratings. Combined with bag-of-features descriptors, we obtain
performance gains of 31.1% and 10.9% for similarity rating prediction and song
year prediction. For both tasks, analysis of selected descriptors reveals that
representing features at multiple time scales benefits prediction accuracy.
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
Authors: Arjun Jain, Jonathan Tompson, Yann Lecun, Christoph Bregler
Date: 2014-09-28 00:00:00
Tasks: 2D Human Pose Estimation, Pose Estimation

In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We apply our architecture to this dataset and report significantly
better performance than current state-of-the-art pose detection systems.
Combining human and machine learning for morphological analysis of galaxy images
Authors: Evan Kuminski, Joe George, John Wallin, Lior Shamir
Date: 2014-09-28 00:00:00
Tasks: BIG-bench Machine Learning, Morphological Analysis

The increasing importance of digital sky surveys collecting many millions of
galaxy images has reinforced the need for robust methods that can perform
morphological analysis of large galaxy image databases. Citizen science
initiatives such as Galaxy Zoo showed that large datasets of galaxy images can
be analyzed effectively by non-scientist volunteers, but since databases
generated by robotic telescopes grow much faster than the processing power of
any group of citizen scientists, it is clear that computer analysis is
required. Here we propose to use citizen science data for training machine
learning systems, and show experimental results demonstrating that machine
learning systems can be trained with citizen science data. Our findings show
that the performance of machine learning depends on the quality of the data,
which can be improved by using samples that have a high degree of agreement
between the citizen scientists. The source code of the method is publicly
available.