pwc-archive/papers-with-abstracts huggingface dataset:
Kannada named entity recognition and classification (nerc) based on multinomial naïve bayes (mnb) classifier
Authors: S. Amarappa, S. V. Sathyanarayana
Date: 2015-09-12 00:00:00
Tasks: General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Named Entity Recognition and Classification (NERC) is a process of
identification of proper nouns in the text and classification of those nouns
into certain predefined categories like person name, location, organization,
date, and time etc. NERC in Kannada is an essential and challenging task. The
aim of this work is to develop a novel model for NERC, based on Multinomial
Na\"ive Bayes (MNB) Classifier. The Methodology adopted in this paper is based
on feature extraction of training corpus, by using term frequency, inverse
document frequency and fitting them to a tf-idf-vectorizer. The paper discusses
the various issues in developing the proposed model. The details of
implementation and performance evaluation are discussed. The experiments are
conducted on a training corpus of size 95,170 tokens and test corpus of 5,000
tokens. It is observed that the model works with Precision, Recall and
F1-measure of 83%, 79% and 81% respectively.
Poisson Matrix Recovery and Completion
Authors: Yang Cao, Yao Xie
Date: 2015-04-20 00:00:00
Tasks: compressed sensing, Matrix Completion

We extend the theory of low-rank matrix recovery and completion to the case
when Poisson observations for a linear combination or a subset of the entries
of a matrix are available, which arises in various applications with count
data. We consider the usual matrix recovery formulation through maximum
likelihood with proper constraints on the matrix $M$ of size $d_1$-by-$d_2$,
and establish theoretical upper and lower bounds on the recovery error. Our
bounds for matrix completion are nearly optimal up to a factor on the order of
$\mathcal{O}(\log(d_1 d_2))$. These bounds are obtained by combing techniques
for compressed sensing for sparse vectors with Poisson noise and for analyzing
low-rank matrices, as well as adapting the arguments used for one-bit matrix
completion \cite{davenport20121} (although these two problems are different in
nature) and the adaptation requires new techniques exploiting properties of the
Poisson likelihood function and tackling the difficulties posed by the locally
sub-Gaussian characteristic of the Poisson distribution. Our results highlight
a few important distinctions of the Poisson case compared to the prior work
including having to impose a minimum signal-to-noise requirement on each
observed entry and a gap in the upper and lower bounds. We also develop a set
of efficient iterative algorithms and demonstrate their good performance on
synthetic examples and real data.
Language Understanding for Text-based Games Using Deep Reinforcement Learning
Authors: Karthik Narasimhan, tejas kulkarni, Regina Barzilay
Date: 2015-06-30 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games

In this paper, we consider the task of learning control policies for
text-based games. In these games, all interactions in the virtual world are
through text and the underlying state is not observed. The resulting language
barrier makes such environments challenging for automatic game players. We
employ a deep reinforcement learning framework to jointly learn state
representations and action policies using game rewards as feedback. This
framework enables us to map text descriptions into vector representations that
capture the semantics of the game states. We evaluate our approach on two game
worlds, comparing against baselines using bag-of-words and bag-of-bigrams for
state representations. Our algorithm outperforms the baselines on both worlds
demonstrating the importance of learning expressive representations.
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
Authors: Theodoros Tsiligkaridis, Keith W. Forsythe
Date: 2014-09-29 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Clustering, Online Clustering, parameter estimation

We develop a sequential low-complexity inference procedure for Dirichlet
process mixtures of Gaussians for online clustering and parameter estimation
when the number of clusters are unknown a-priori. We present an easily
computable, closed form parametric expression for the conditional likelihood,
in which hyperparameters are recursively updated as a function of the streaming
data assuming conjugate priors. Motivated by large-sample asymptotics, we
propose a novel adaptive low-complexity design for the Dirichlet process
concentration parameter and show that the number of classes grow at most at a
logarithmic rate. We further prove that in the large-sample limit, the
conditional likelihood and data predictive distribution become asymptotically
Gaussian. We demonstrate through experiments on synthetic and real data sets
that our approach is superior to other online state-of-the-art methods.
Learning Co-Sparse Analysis Operators with Separable Structures
Authors: Matthias Seibert, Julian Wörmann, Rémi Gribonval, Martin Kleinsteuber
Date: 2015-03-09 00:00:00

In the co-sparse analysis model a set of filters is applied to a signal out
of the signal class of interest yielding sparse filter responses. As such, it
may serve as a prior in inverse problems, or for structural analysis of signals
that are known to belong to the signal class. The more the model is adapted to
the class, the more reliable it is for these purposes. The task of learning
such operators for a given class is therefore a crucial problem. In many
applications, it is also required that the filter responses are obtained in a
timely manner, which can be achieved by filters with a separable structure. Not
only can operators of this sort be efficiently used for computing the filter
responses, but they also have the advantage that less training samples are
required to obtain a reliable estimate of the operator. The first contribution
of this work is to give theoretical evidence for this claim by providing an
upper bound for the sample complexity of the learning process. The second is a
stochastic gradient descent (SGD) method designed to learn an analysis operator
with separable structures, which includes a novel and efficient step size
selection rule. Numerical experiments are provided that link the sample
complexity to the convergence speed of the SGD algorithm.
DeepSat - A Learning framework for Satellite Imagery
Authors: Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert DiBiano, Manohar Karki, Ramakrishna Nemani
Date: 2015-09-11 00:00:00
Tasks: Classification, Denoising, General Classification, image-classification, Image Classification, Object Recognition, Satellite Image Classification

Satellite image classification is a challenging problem that lies at the
crossroads of remote sensing, computer vision, and machine learning. Due to the
high variability inherent in satellite data, most of the current object
classification approaches are not suitable for handling satellite datasets. The
progress of satellite image analytics has also been inhibited by the lack of a
single labeled high-resolution dataset with multiple class labels. The
contributions of this paper are twofold - (1) first, we present two new
satellite datasets called SAT-4 and SAT-6, and (2) then, we propose a
classification framework that extracts features from an input image, normalizes
them and feeds the normalized feature vectors to a Deep Belief Network for
classification. On the SAT-4 dataset, our best network produces a
classification accuracy of 97.95% and outperforms three state-of-the-art object
recognition algorithms, namely - Deep Belief Networks, Convolutional Neural
Networks and Stacked Denoising Autoencoders by ~11%. On SAT-6, it produces a
classification accuracy of 93.9% and outperforms the other algorithms by ~15%.
Comparative studies with a Random Forest classifier show the advantage of an
unsupervised learning approach over traditional supervised learning techniques.
A statistical analysis based on Distribution Separability Criterion and
Intrinsic Dimensionality Estimation substantiates the effectiveness of our
approach in learning better representations for satellite imagery.
Some Supplementaries to The Counting Semantics for Abstract Argumentation
Authors: Fuan Pu, Jian Luo, Guiming Luo
Date: 2015-09-11 00:00:00
Tasks: Abstract Argumentation

Dung's abstract argumentation framework consists of a set of interacting
arguments and a series of semantics for evaluating them. Those semantics
partition the powerset of the set of arguments into two classes: extensions and
non-extensions. In order to reason with a specific semantics, one needs to take
a credulous or skeptical approach, i.e. an argument is eventually accepted, if
it is accepted in one or all extensions, respectively. In our previous work
\cite{ref-pu2015counting}, we have proposed a novel semantics, called
\emph{counting semantics}, which allows for a more fine-grained assessment to
arguments by counting the number of their respective attackers and defenders
based on argument graph and argument game. In this paper, we continue our
previous work by presenting some supplementaries about how to choose the
damaging factor for the counting semantics, and what relationships with some
existing approaches, such as Dung's classical semantics, generic gradual
valuations. Lastly, an axiomatic perspective on the ranking semantics induced
by our counting semantics are presented.
Lazy Factored Inference for Functional Probabilistic Programming
Authors: Avi Pfeffer, Brian Ruttenberg, Amy Sliva, Michael Howard, Glenn Takata
Date: 2015-09-11 00:00:00
Tasks: Probabilistic Programming

Probabilistic programming provides the means to represent and reason about
complex probabilistic models using programming language constructs. Even simple
probabilistic programs can produce models with infinitely many variables.
Factored inference algorithms are widely used for probabilistic graphical
models, but cannot be applied to these programs because all the variables and
factors have to be enumerated. In this paper, we present a new inference
framework, lazy factored inference (LFI), that enables factored algorithms to
be used for models with infinitely many variables. LFI expands the model to a
bounded depth and uses the structure of the program to precisely quantify the
effect of the unexpanded part of the model, producing lower and upper bounds to
the probability of the query.
Better Document-level Sentiment Analysis from RST Discourse Parsing
Authors: Parminder Bhatia, Yangfeng Ji, Jacob Eisenstein
Date: 2015-09-04 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Discourse Parsing, General Classification, Position, Sentiment Analysis

Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.
Premise Selection and External Provers for HOL4
Authors: Thibault Gauthier, Cezary Kaliszyk
Date: 2015-09-11 00:00:00

Learning-assisted automated reasoning has recently gained popularity among
the users of Isabelle/HOL, HOL Light, and Mizar. In this paper, we present an
add-on to the HOL4 proof assistant and an adaptation of the HOLyHammer system
that provides machine learning-based premise selection and automated reasoning
also for HOL4. We efficiently record the HOL4 dependencies and extract features
from the theorem statements, which form a basis for premise selection.
HOLyHammer transforms the HOL4 statements in the various TPTP-ATP proof
formats, which are then processed by the ATPs. We discuss the different
evaluation settings: ATPs, accessible lemmas, and premise numbers. We measure
the performance of HOLyHammer on the HOL4 standard library. The results are
combined accordingly and compared with the HOL Light experiments, showing a
comparably high quality of predictions. The system directly benefits HOL4 users
by automatically finding proofs dependencies that can be reconstructed by
Metis.
Sharing HOL4 and HOL Light proof knowledge
Authors: Thibault Gauthier, Cezary Kaliszyk
Date: 2015-09-11 00:00:00

New proof assistant developments often involve concepts similar to already
formalized ones. When proving their properties, a human can often take
inspiration from the existing formalized proofs available in other provers or
libraries. In this paper we propose and evaluate a number of methods, which
strengthen proof automation by learning from proof libraries of different
provers. Certain conjectures can be proved directly from the dependencies
induced by similar proofs in the other library. Even if exact correspondences
are not found, learning-reasoning systems can make use of the association
between proved theorems and their characteristics to predict the relevant
premises. Such external help can be further combined with internal advice. We
evaluate the proposed knowledge-sharing methods by reproving the HOL Light and
HOL4 standard libraries. The learning-reasoning system HOL(y)Hammer, whose
single best strategy could automatically find proofs for 30% of the HOL Light
problems, can prove 40% with the knowledge from HOL4.
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Authors: Margret Keuper, Evgeny Levinkov, Nicolas Bonneel, Guillaume Lavoué, Thomas Brox, Bjoern Andres
Date: 2015-05-26 00:00:00
Proceeding: ICCV 2015 12

Formulations of the Image Decomposition Problem as a Multicut Problem (MP)
w.r.t. a superpixel graph have received considerable attention. In contrast,
instances of the MP w.r.t. a pixel grid graph have received little attention,
firstly, because the MP is NP-hard and instances w.r.t. a pixel grid graph are
hard to solve in practice, and, secondly, due to the lack of long-range terms
in the objective function of the MP. We propose a generalization of the MP with
long-range terms (LMP). We design and implement two efficient algorithms
(primal feasible heuristics) for the MP and LMP which allow us to study
instances of both problems w.r.t. the pixel grid graphs of the images in the
BSDS-500 benchmark. The decompositions we obtain do not differ significantly
from the state of the art, suggesting that the LMP is a competitive formulation
of the Image Decomposition Problem. To demonstrate the generality of the LMP,
we apply it also to the Mesh Decomposition Problem posed by the Princeton
benchmark, obtaining state-of-the-art decompositions.
CURL: Co-trained Unsupervised Representation Learning for Image Classification
Authors: Simone Bianco, Gianluigi Ciocca, Claudio Cusano
Date: 2015-05-29 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object Recognition, Representation Learning, Semi-Supervised Image Classification

In this paper we propose a strategy for semi-supervised image classification
that leverages unsupervised representation learning and co-training. The
strategy, that is called CURL from Co-trained Unsupervised Representation
Learning, iteratively builds two classifiers on two different views of the
data. The two views correspond to different representations learned from both
labeled and unlabeled data and differ in the fusion scheme used to combine the
image features. To assess the performance of our proposal, we conducted several
experiments on widely used data sets for scene and object recognition. We
considered three scenarios (inductive, transductive and self-taught learning)
that differ in the strategy followed to exploit the unlabeled data. As image
features we considered a combination of GIST, PHOG, and LBP as well as features
extracted from a Convolutional Neural Network. Moreover, two embodiments of
CURL are investigated: one using Ensemble Projection as unsupervised
representation learning coupled with Logistic Regression, and one based on
LapSVM. The results show that CURL clearly outperforms other supervised and
semi-supervised learning methods in the state of the art.
Newton-based maximum likelihood estimation in nonlinear state space models
Authors: Manon Kok, Johan Dahlin, Thomas B. Schön, Adrian Wills
Date: 2015-02-12 00:00:00
Tasks: parameter estimation, State Space Models, valid

Maximum likelihood (ML) estimation using Newton's method in nonlinear state
space models (SSMs) is a challenging problem due to the analytical
intractability of the log-likelihood and its gradient and Hessian. We estimate
the gradient and Hessian using Fisher's identity in combination with a
smoothing algorithm. We explore two approximations of the log-likelihood and of
the solution of the smoothing problem. The first is a linearization
approximation which is computationally cheap, but the accuracy typically varies
between models. The second is a sampling approximation which is asymptotically
valid for any SSM but is more computationally costly. We demonstrate our
approach for ML parameter estimation on simulated data from two different SSMs
with encouraging results.
Nested Sequential Monte Carlo Methods
Authors: Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
Date: 2015-02-09 00:00:00

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from
sequences of probability distributions, even where the random variables are
high-dimensional. NSMC generalises the SMC framework by requiring only
approximate, properly weighted, samples from the SMC proposal distribution,
while still resulting in a correct SMC algorithm. Furthermore, NSMC can in
itself be used to produce such properly weighted samples. Consequently, one
NSMC sampler can be used to construct an efficient high-dimensional proposal
distribution for another NSMC sampler, and this nesting of the algorithm can be
done to an arbitrary degree. This allows us to consider complex and
high-dimensional models using SMC. We show results that motivate the efficacy
of our approach on several filtering problems with dimensions in the order of
100 to 1 000.
OCR accuracy improvement on document images through a novel pre-processing approach
Authors: Abdeslam El Harraj, Naoufal Raissouni
Date: 2015-09-11 00:00:00
Tasks: Binarization, Optical Character Recognition, Optical Character Recognition (OCR), Text Detection

Digital camera and mobile document image acquisition are new trends arising
in the world of Optical Character Recognition and text detection. In some
cases, such process integrates many distortions and produces poorly scanned
text or text-photo images and natural images, leading to an unreliable OCR
digitization. In this paper, we present a novel nonparametric and unsupervised
method to compensate for undesirable document image distortions aiming to
optimally improve OCR accuracy. Our approach relies on a very efficient stack
of document image enhancing techniques to recover deformation of the entire
document image. First, we propose a local brightness and contrast adjustment
method to effectively handle lighting variations and the irregular distribution
of image illumination. Second, we use an optimized greyscale conversion
algorithm to transform our document image to greyscale level. Third, we sharpen
the useful information in the resulting greyscale image using Un-sharp Masking
method. Finally, an optimal global binarization approach is used to prepare the
final document image to OCR recognition. The proposed approach can
significantly improve text detection rate and optical character recognition
accuracy. To demonstrate the efficiency of our approach, an exhaustive
experimentation on a standard dataset is presented.
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets
Authors: Saikat Basu, Manohar Karki, Sangram Ganguly, Robert DiBiano, Supratik Mukhopadhyay, Ramakrishna Nemani
Date: 2015-09-11 00:00:00
Tasks: General Classification

Learning sparse feature representations is a useful instrument for solving an
unsupervised learning problem. In this paper, we present three labeled
handwritten digit datasets, collectively called n-MNIST. Then, we propose a
novel framework for the classification of handwritten digits that learns sparse
representations using probabilistic quadtrees and Deep Belief Nets. On the
MNIST and n-MNIST datasets, our framework shows promising results and
significantly outperforms traditional Deep Belief Networks.
Measuring an Artificial Intelligence System's Performance on a Verbal IQ Test For Young Children
Authors: Stellan Ohlsson, Robert H. Sloan, György Turán, Aaron Urasky
Date: 2015-09-11 00:00:00
Tasks: Common Sense Reasoning, Question Answering

We administered the Verbal IQ (VIQ) part of the Wechsler Preschool and
Primary Scale of Intelligence (WPPSI-III) to the ConceptNet 4 AI system. The
test questions (e.g., "Why do we shake hands?") were translated into ConceptNet
4 inputs using a combination of the simple natural language processing tools
that come with ConceptNet together with short Python programs that we wrote.
The question answering used a version of ConceptNet based on spectral methods.
The ConceptNet system scored a WPPSI-III VIQ that is average for a
four-year-old child, but below average for 5 to 7 year-olds. Large variations
among subtests indicate potential areas of improvement. In particular, results
were strongest for the Vocabulary and Similarities subtests, intermediate for
the Information subtest, and lowest for the Comprehension and Word Reasoning
subtests. Comprehension is the subtest most strongly associated with common
sense. The large variations among subtests and ordinary common sense strongly
suggest that the WPPSI-III VIQ results do not show that "ConceptNet has the
verbal abilities a four-year-old." Rather, children's IQ tests offer one
objective metric for the evaluation and comparison of AI systems. Also, this
work continues previous research on Psychometric AI.
Multi-Attribute Proportional Representation
Authors: Jerome Lang, Piotr Skowron
Date: 2015-09-11 00:00:00
Tasks: Attribute

We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem collapses to the apportionment problem for party-list proportional representation systems (in such case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computation complexity.