pwc-archive/papers-with-abstracts huggingface dataset:
Markov Logic Networks for Natural Language Question Answering
Authors: Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, Oren Etzioni
Date: 2015-07-10 00:00:00
Tasks: Entity Resolution, Question Answering

Our goal is to answer elementary-level science questions using knowledge
extracted automatically from science textbooks, expressed in a subset of
first-order logic. Given the incomplete and noisy nature of these automatically
extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but
the exact way of leveraging MLNs is by no means obvious. We investigate three
ways of applying MLNs to our task. In the first, we simply use the extracted
science rules directly as MLN clauses. Unlike typical MLN applications, our
domain has long and complex rules, leading to an unmanageable number of
groundings. We exploit the structure present in hard constraints to improve
tractability, but the formulation remains ineffective. In the second approach,
we instead interpret science rules as describing prototypical entities, thus
mapping rules directly to grounded MLN assertions, whose constants are then
clustered using existing entity resolution methods. This drastically simplifies
the network, but still suffers from brittleness. Finally, our third approach,
called Praline, uses MLNs to align the lexical elements as well as define and
control how inference should be performed in this task. Our experiments,
demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest
that the flexibility and different inference semantics of Praline are a better
fit for the natural language question answering task.
Linear Convergence of Variance-Reduced Stochastic Gradient without Strong Convexity
Authors: Pinghua Gong, Jieping Ye
Date: 2014-06-04 00:00:00

Stochastic gradient algorithms estimate the gradient based on only one or a
few samples and enjoy low computational cost per iteration. They have been
widely used in large-scale optimization problems. However, stochastic gradient
algorithms are usually slow to converge and achieve sub-linear convergence
rates, due to the inherent variance in the gradient computation. To accelerate
the convergence, some variance-reduced stochastic gradient algorithms, e.g.,
proximal stochastic variance-reduced gradient (Prox-SVRG) algorithm, have
recently been proposed to solve strongly convex problems. Under the strongly
convex condition, these variance-reduced stochastic gradient algorithms achieve
a linear convergence rate. However, many machine learning problems are convex
but not strongly convex. In this paper, we introduce Prox-SVRG and its
projected variant called Variance-Reduced Projected Stochastic Gradient (VRPSG)
to solve a class of non-strongly convex optimization problems widely used in
machine learning. As the main technical contribution of this paper, we show
that both VRPSG and Prox-SVRG achieve a linear convergence rate without strong
convexity. A key ingredient in our proof is a Semi-Strongly Convex (SSC)
inequality which is the first to be rigorously proved for a class of
non-strongly convex problems in both constrained and regularized settings.
Moreover, the SSC inequality is independent of algorithms and may be applied to
analyze other stochastic gradient algorithms besides VRPSG and Prox-SVRG, which
may be of independent interest. To the best of our knowledge, this is the first
work that establishes the linear convergence rate for the variance-reduced
stochastic gradient algorithms on solving both constrained and regularized
problems without strong convexity.
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach
Authors: Luís Marujo, Ricardo Ribeiro, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell
Date: 2015-07-10 00:00:00
Proceeding: SEMEVAL 2015 6
Tasks: Document Summarization, Multi-Document Summarization

The increasing amount of online content motivated the development of
multi-document summarization methods. In this work, we explore straightforward
approaches to extend single-document summarization methods to multi-document
summarization. The proposed methods are based on the hierarchical combination
of single-document summaries, and achieves state of the art results.
Deep Perceptual Mapping for Thermal to Visible Face Recognition
Authors: M. Saquib Sarfraz, Rainer Stiefelhagen
Date: 2015-07-10 00:00:00
Tasks: Face Recognition

Cross modal face matching between the thermal and visible spectrum is a much
de- sired capability for night-time surveillance and security applications. Due
to a very large modality gap, thermal-to-visible face recognition is one of the
most challenging face matching problem. In this paper, we present an approach
to bridge this modality gap by a significant margin. Our approach captures the
highly non-linear relationship be- tween the two modalities by using a deep
neural network. Our model attempts to learn a non-linear mapping from visible
to thermal spectrum while preserving the identity in- formation. We show
substantive performance improvement on a difficult thermal-visible face
dataset. The presented approach improves the state-of-the-art by more than 10%
in terms of Rank-1 identification and bridge the drop in performance due to the
modality gap by more than 40%.
Lazy Explanation-Based Approximation for Probabilistic Logic Programming
Authors: Joris Renkens, Angelika Kimmig, Luc De Raedt
Date: 2015-07-10 00:00:00

We introduce a lazy approach to the explanation-based approximation of
probabilistic logic programs. It uses only the most significant part of the
program when searching for explanations. The result is a fast and anytime
approximate inference algorithm which returns hard lower and upper bounds on
the exact probability. We experimentally show that this method outperforms
state-of-the-art approximate inference.
Analysis of Microarray Data using Artificial Intelligence Based Techniques
Authors: Khalid Raza
Date: 2015-07-10 00:00:00

Microarray is one of the essential technologies used by the biologist to
measure genome-wide expression levels of genes in a particular organism under
some particular conditions or stimuli. As microarrays technologies have become
more prevalent, the challenges of analyzing these data for getting better
insight about biological processes have essentially increased. Due to
availability of artificial intelligence based sophisticated computational
techniques, such as artificial neural networks, fuzzy logic, genetic
algorithms, and many other nature-inspired algorithms, it is possible to
analyse microarray gene expression data in more better way. Here, we reviewed
artificial intelligence based techniques for the analysis of microarray gene
expression data. Further, challenges in the field and future work direction
have also been suggested.
A Trainable Neuromorphic Integrated Circuit that Exploits Device Mismatch
Authors: Chetan Singh Thakur, Runchun Wang, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
Date: 2015-07-10 00:00:00

Random device mismatch that arises as a result of scaling of the CMOS
(complementary metal-oxide semi-conductor) technology into the deep submicron
regime degrades the accuracy of analogue circuits. Methods to combat this
increase the complexity of design. We have developed a novel neuromorphic
system called a Trainable Analogue Block (TAB), which exploits device mismatch
as a means for random projections of the input to a higher dimensional space.
The TAB framework is inspired by the principles of neural population coding
operating in the biological nervous system. Three neuronal layers, namely
input, hidden, and output, constitute the TAB framework, with the number of
hidden layer neurons far exceeding the input layer neurons. Here, we present
measurement results of the first prototype TAB chip built using a 65nm process
technology and show its learning capability for various regression tasks. Our
TAB chip exploits inherent randomness and variability arising due to the
fabrication process to perform various learning tasks. Additionally, we
characterise each neuron and discuss the statistical variability of its tuning
curve that arises due to random device mismatch, a desirable property for the
learning capability of the TAB. We also discuss the effect of the number of
hidden neurons and the resolution of output weights on the accuracy of the
learning capability of the TAB.
Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes
Authors: Hai X. Pham, Chongyu Chen, Luc N. Dao, Vladimir Pavlovic, Jianfei Cai, Tat-Jen Cham
Date: 2015-07-10 00:00:00
Tasks: 3D Reconstruction, Face Model

We introduce a novel robust hybrid 3D face tracking framework from RGBD video
streams, which is capable of tracking head pose and facial actions without
pre-calibration or intervention from a user. In particular, we emphasize on
improving the tracking performance in instances where the tracked subject is at
a large distance from the cameras, and the quality of point cloud deteriorates
severely. This is accomplished by the combination of a flexible 3D shape
regressor and the joint 2D+3D optimization on shape parameters. Our approach
fits facial blendshapes to the point cloud of the human head, while being
driven by an efficient and rapid 3D shape regressor trained on generic RGB
datasets. As an on-line tracking system, the identity of the unknown user is
adapted on-the-fly resulting in improved 3D model reconstruction and
consequently better tracking performance. The result is a robust RGBD face
tracker, capable of handling a wide range of target scene depths, beyond those
that can be afforded by traditional depth or RGB face trackers. Lastly, since
the blendshape is not able to accurately recover the real facial shape, we use
the tracked 3D face model as a prior in a novel filtering process to further
refine the depth map for use in other tasks, such as 3D reconstruction.
Locally Non-linear Embeddings for Extreme Multi-label Learning
Authors: Kush Bhatia, Himanshu Jain, Purushottam Kar, Prateek Jain, Manik Varma
Date: 2015-07-09 00:00:00
Tasks: Extreme Multi-Label Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning, TAG

The objective in extreme multi-label learning is to train a classifier that
can automatically tag a novel data point with the most relevant subset of
labels from an extremely large label set. Embedding based approaches make
training and prediction tractable by assuming that the training label matrix is
low-rank and hence the effective number of labels can be reduced by projecting
the high dimensional label vectors onto a low dimensional linear subspace.
Still, leading embedding approaches have been unable to deliver high prediction
accuracies or scale to large problems as the low rank assumption is violated in
most real world applications.
  This paper develops the X-One classifier to address both limitations. The
main technical contribution in X-One is a formulation for learning a small
ensemble of local distance preserving embeddings which can accurately predict
infrequently occurring (tail) labels. This allows X-One to break free of the
traditional low-rank assumption and boost classification accuracy by learning
embeddings which preserve pairwise distances between only the nearest label
vectors.
  We conducted extensive experiments on several real-world as well as benchmark
data sets and compared our method against state-of-the-art methods for extreme
multi-label classification. Experiments reveal that X-One can make
significantly more accurate predictions then the state-of-the-art methods
including both embeddings (by as much as 35%) as well as trees (by as much as
6%). X-One can also scale efficiently to data sets with a million labels which
are beyond the pale of leading embedding methods.
Robot In a Room: Toward Perfect Object Recognition in Closed Environments
Authors: Shuran Song, Linguang Zhang, Jianxiong Xiao
Date: 2015-07-09 00:00:00
Tasks: Object, Object Recognition

While general object recognition is still far from being solved, this paper
proposes a way for a robot to recognize every object at an almost human-level
accuracy. Our key observation is that many robots will stay in a relatively
closed environment (e.g. a house or an office). By constraining a robot to stay
in a limited territory, we can ensure that the robot has seen most objects
before and the speed of introducing a new object is slow. Furthermore, we can
build a 3D map of the environment to reliably subtract the background to make
recognition easier. We propose extremely robust algorithms to obtain a 3D map
and enable humans to collectively annotate objects. During testing time, our
algorithm can recognize all objects very reliably, and query humans from crowd
sourcing platform if confidence is low or new objects are identified. This
paper explains design decisions in building such a system, and constructs a
benchmark for extensive evaluation. Experiments suggest that making robot
vision appear to be working from an end user's perspective is a reachable goal
today, as long as the robot stays in a closed environment. By formulating this
task, we hope to lay the foundation of a new direction in vision for robotics.
Code and data will be available upon acceptance.
Quantum Inspired Training for Boltzmann Machines
Authors: Nathan Wiebe, Ashish Kapoor, Christopher Granade, Krysta M. Svore
Date: 2015-07-09 00:00:00

We present an efficient classical algorithm for training deep Boltzmann
machines (DBMs) that uses rejection sampling in concert with variational
approximations to estimate the gradients of the training objective function.
Our algorithm is inspired by a recent quantum algorithm for training DBMs. We
obtain rigorous bounds on the errors in the approximate gradients; in turn, we
find that choosing the instrumental distribution to minimize the alpha=2
divergence with the Gibbs state minimizes the asymptotic algorithmic
complexity. Our rejection sampling approach can yield more accurate gradients
than low-order contrastive divergence training and the costs incurred in
finding increasingly accurate gradients can be easily parallelized. Finally our
algorithm can train full Boltzmann machines and scales more favorably with the
number of layers in a DBM than greedy contrastive divergence training.
Deep convolutional filter banks for texture recognition and segmentation
Authors: Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi
Date: 2014-11-25 00:00:00
Tasks: Material Recognition, Scene Recognition

Research in texture recognition often concentrates on the problem of material
recognition in uncluttered conditions, an assumption rarely met by
applications. In this work we conduct a first study of material and describable
texture at- tributes recognition in clutter, using a new dataset derived from
the OpenSurface texture repository. Motivated by the challenge posed by this
problem, we propose a new texture descriptor, D-CNN, obtained by Fisher Vector
pooling of a Convolutional Neural Network (CNN) filter bank. D-CNN
substantially improves the state-of-the-art in texture, mate- rial and scene
recognition. Our approach achieves 82.3% accuracy on Flickr material dataset
and 81.1% accuracy on MIT indoor scenes, providing absolute gains of more than
10% over existing approaches. D-CNN easily trans- fers across domains without
requiring feature adaptation as for methods that build on the fully-connected
layers of CNNs. Furthermore, D-CNN can seamlessly incorporate multi-scale
information and describe regions of arbitrary shapes and sizes. Our approach is
particularly suited at lo- calizing stuff categories and obtains
state-of-the-art re- sults on MSRC segmentation dataset, as well as promising
results on recognizing materials and surface attributes in clutter on the
OpenSurfaces dataset.
FAQ-based Question Answering via Word Alignment
Authors: Zhiguo Wang, Abraham Ittycheriah
Date: 2015-07-09 00:00:00
Tasks: Learning-To-Rank, Question Answering, Question Similarity, Sentence, Word Alignment

In this paper, we propose a novel word-alignment-based method to solve the
FAQ-based question answering task. First, we employ a neural network model to
calculate question similarity, where the word alignment between two questions
is used for extracting features. Second, we design a bootstrap-based feature
extraction method to extract a small set of effective lexical features. Third,
we propose a learning-to-rank algorithm to train parameters more suitable for
the ranking tasks. Experimental results, conducted on three languages (English,
Spanish and Japanese), demonstrate that the question similarity model is more
effective than baseline systems, the sparse features bring 5% improvements on
top-1 accuracy, and the learning-to-rank algorithm works significantly better
than the traditional method. We further evaluate our method on the answer
sentence selection task. Our method outperforms all the previous systems on the
standard TREC data set.
Sampling from a log-concave distribution with Projected Langevin Monte Carlo
Authors: Sébastien Bubeck, Ronen Eldan, Joseph Lehec
Date: 2015-07-09 00:00:00

We extend the Langevin Monte Carlo (LMC) algorithm to compactly supported
measures via a projection step, akin to projected Stochastic Gradient Descent
(SGD). We show that (projected) LMC allows to sample in polynomial time from a
log-concave distribution with smooth potential. This gives a new Markov chain
to sample from a log-concave distribution. Our main result shows in particular
that when the target distribution is uniform, LMC mixes in $\tilde{O}(n^7)$
steps (where $n$ is the dimension). We also provide preliminary experimental
evidence that LMC performs at least as well as hit-and-run, for which a better
mixing time of $\tilde{O}(n^4)$ was proved by Lov{\'a}sz and Vempala.
Managing Autonomous Mobility on Demand Systems for Better Passenger Experience
Authors: Wen Shen, Cristina Lopes
Date: 2015-07-09 00:00:00
Tasks: Autonomous Vehicles, Scheduling

Autonomous mobility on demand systems, though still in their infancy, have
very promising prospects in providing urban population with sustainable and
safe personal mobility in the near future. While much research has been
conducted on both autonomous vehicles and mobility on demand systems, to the
best of our knowledge, this is the first work that shows how to manage
autonomous mobility on demand systems for better passenger experience. We
introduce the Expand and Target algorithm which can be easily integrated with
three different scheduling strategies for dispatching autonomous vehicles. We
implement an agent-based simulation platform and empirically evaluate the
proposed approaches with the New York City taxi data. Experimental results
demonstrate that the algorithm significantly improve passengers' experience by
reducing the average passenger waiting time by up to 29.82% and increasing the
trip success rate by up to 7.65%.
Adaptive Chemical Reaction Optimization for Global Numerical Optimization
Authors: James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li
Date: 2015-07-09 00:00:00

A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction
Optimization (CRO), has been applied to many optimization problems in both
discrete and continuous domains. To alleviate the effort in tuning parameters,
this paper reduces the number of optimization parameters in canonical CRO and
develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO)
adapts better to different optimization problems. We perform simulations with
ACRO on a widely-used benchmark of continuous problems. The simulation results
show that ACRO has superior performance over canonical CRO.
Parameter Sensitivity Analysis of Social Spider Algorithm
Authors: James J. Q. Yu, Victor O. K. Li
Date: 2015-07-09 00:00:00
Tasks: Sensitivity

Social Spider Algorithm (SSA) is a recently proposed general-purpose
real-parameter metaheuristic designed to solve global numerical optimization
problems. This work systematically benchmarks SSA on a suite of 11 functions
with different control parameters. We conduct parameter sensitivity analysis of
SSA using advanced non-parametric statistical tests to generate statistically
significant conclusion on the best performing parameter settings. The
conclusion can be adopted in future work to reduce the effort in parameter
tuning. In addition, we perform a success rate test to reveal the impact of the
control parameters on the convergence speed of the algorithm.
A New Approach to Probabilistic Programming Inference
Authors: Frank Wood, Jan Willem van de Meent, Vikash Mansinghka
Date: 2015-07-03 00:00:00
Tasks: Probabilistic Programming

We introduce and demonstrate a new approach to inference in expressive
probabilistic programming languages based on particle Markov chain Monte Carlo.
Our approach is simple to implement and easy to parallelize. It applies to
Turing-complete probabilistic programming languages and supports accurate
inference in models that make use of complex control flow, including stochastic
recursion. It also includes primitives from Bayesian nonparametric statistics.
Our experiments show that this approach can be more efficient than previously
introduced single-site Metropolis-Hastings methods.
Data Mining of Causal Relations from Text: Analysing Maritime Accident Investigation Reports
Authors: Santosh Tirunagari
Date: 2015-07-09 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval

Text mining is a process of extracting information of interest from text.
Such a method includes techniques from various areas such as Information
Retrieval (IR), Natural Language Processing (NLP), and Information Extraction
(IE). In this study, text mining methods are applied to extract causal
relations from maritime accident investigation reports collected from the
Marine Accident Investigation Branch (MAIB). These causal relations provide
information on various mechanisms behind accidents, including human and
organizational factors relating to the accident. The objective of this study is
to facilitate the analysis of the maritime accident investigation reports, by
means of extracting contributory causes with more feasibility. A careful
investigation of contributory causes from the reports provide opportunity to
improve safety in future.
  Two methods have been employed in this study to extract the causal relations.
They are 1) Pattern classification method and 2) Connectives method. The
earlier one uses naive Bayes and Support Vector Machines (SVM) as classifiers.
The latter simply searches for the words connecting cause and effect in
sentences.
  The causal patterns extracted using these two methods are compared to the
manual (human expert) extraction. The pattern classification method showed a
fair and sensible performance with F-measure(average) = 65% when compared to
connectives method with F-measure(average) = 58%. This study is an evidence,
that text mining methods could be employed in extracting causal relations from
marine accident investigation reports.