pwc-archive/papers-with-abstracts huggingface dataset:
Using Machine Learning to Decide When to Precondition Cylindrical Algebraic Decomposition With Groebner Bases
Authors: Zongyan Huang, Matthew England, James H. Davenport, Lawrence C. Paulson
Date: 2016-08-15 00:00:00
Tasks: BIG-bench Machine Learning

Cylindrical Algebraic Decomposition (CAD) is a key tool in computational
algebraic geometry, particularly for quantifier elimination over real-closed
fields. However, it can be expensive, with worst case complexity doubly
exponential in the size of the input. Hence it is important to formulate the
problem in the best manner for the CAD algorithm. One possibility is to
precondition the input polynomials using Groebner Basis (GB) theory. Previous
experiments have shown that while this can often be very beneficial to the CAD
algorithm, for some problems it can significantly worsen the CAD performance.
  In the present paper we investigate whether machine learning, specifically a
support vector machine (SVM), may be used to identify those CAD problems which
benefit from GB preconditioning. We run experiments with over 1000 problems
(many times larger than previous studies) and find that the machine learned
choice does better than the human-made heuristic.
An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants
Authors: Marco Fraccaro, Ulrich Paquet, Ole Winther
Date: 2016-04-07 00:00:00

The estimation of normalizing constants is a fundamental step in
probabilistic model comparison. Sequential Monte Carlo methods may be used for
this task and have the advantage of being inherently parallelizable. However,
the standard choice of using a fixed number of particles at each iteration is
suboptimal because some steps will contribute disproportionately to the
variance of the estimate. We introduce an adaptive version of the Resample-Move
algorithm, in which the particle set is adaptively expanded whenever a better
approximation of an intermediate distribution is needed. The algorithm builds
on the expression for the optimal number of particles and the corresponding
minimum variance found under ideal conditions. Benchmark results on challenging
Gaussian Process Classification and Restricted Boltzmann Machine applications
show that Adaptive Resample-Move (ARM) estimates the normalizing constant with
a smaller variance, using less computational resources, than either
Resample-Move with a fixed number of particles or Annealed Importance Sampling.
A further advantage over Annealed Importance Sampling is that ARM is easier to
tune.
Face Alignment In-the-Wild: A Survey
Authors: Xin Jin, Xiaoyang Tan
Date: 2016-08-15 00:00:00
Tasks: Face Alignment, Robust Face Alignment, Survey

Over the last two decades, face alignment or localizing fiducial facial
points has received increasing attention owing to its comprehensive
applications in automatic face analysis. However, such a task has proven
extremely challenging in unconstrained environments due to many confounding
factors, such as pose, occlusions, expression and illumination. While numerous
techniques have been developed to address these challenges, this problem is
still far away from being solved. In this survey, we present an up-to-date
critical review of the existing literatures on face alignment, focusing on
those methods addressing overall difficulties and challenges of this topic
under uncontrolled conditions. Specifically, we categorize existing face
alignment techniques, present detailed descriptions of the prominent algorithms
within each category, and discuss their advantages and disadvantages.
Furthermore, we organize special discussions on the practical aspects of face
alignment in-the-wild, towards the development of a robust face alignment
system. In addition, we show performance statistics of the state of the art,
and conclude this paper with several promising directions for future research.
Stable-Unstable Semantics: Beyond NP with Normal Logic Programs
Authors: Bart Bogaerts, Tomi Janhunen, Shahab Tasharrofi
Date: 2016-08-05 00:00:00

Standard answer set programming (ASP) targets at solving search problems from
the first level of the polynomial time hierarchy (PH). Tackling search problems
beyond NP using ASP is less straightforward. The class of disjunctive logic
programs offers the most prominent way of reaching the second level of the PH,
but encoding respective hard problems as disjunctive programs typically
requires sophisticated techniques such as saturation or meta-interpretation.
The application of such techniques easily leads to encodings that are
inaccessible to non-experts. Furthermore, while disjunctive ASP solvers often
rely on calls to a (co-)NP oracle, it may be difficult to detect from the input
program where the oracle is being accessed. In other formalisms, such as
Quantified Boolean Formulas (QBFs), the interface to the underlying oracle is
more transparent as it is explicitly recorded in the quantifier prefix of a
formula. On the other hand, ASP has advantages over QBFs from the modeling
perspective. The rich high-level languages such as ASP-Core-2 offer a wide
variety of primitives that enable concise and natural encodings of search
problems. In this paper, we present a novel logic programming--based modeling
paradigm that combines the best features of ASP and QBFs. We develop so-called
combined logic programs in which oracles are directly cast as (normal) logic
programs themselves. Recursive incarnations of this construction enable logic
programming on arbitrarily high levels of the PH. We develop a proof-of-concept
implementation for our new paradigm.
  This paper is under consideration for acceptance in TPLP.
Attribute Extraction from Product Titles in eCommerce
Authors: Ajinkya More
Date: 2016-08-15 00:00:00
Tasks: Attribute, Attribute Extraction, Entity Extraction using GAN

This paper presents a named entity extraction system for detecting attributes
in product titles of eCommerce retailers like Walmart. The absence of syntactic
structure in such short pieces of text makes extracting attribute values a
challenging problem. We find that combining sequence labeling algorithms such
as Conditional Random Fields and Structured Perceptron with a curated
normalization scheme produces an effective system for the task of extracting
product attribute values from titles. To keep the discussion concrete, we will
illustrate the mechanics of the system from the point of view of a particular
attribute - brand. We also discuss the importance of an attribute extraction
system in the context of retail websites with large product catalogs, compare
our approach to other potential approaches to this problem and end the paper
with a discussion of the performance of our system for extracting attributes.
Novel Views of Objects from a Single Image
Authors: Konstantinos Rematas, Chuong Nguyen, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars
Date: 2016-01-31 00:00:00
Tasks: Novel View Synthesis, Object

Taking an image of an object is at its core a lossy process. The rich
information about the three-dimensional structure of the world is flattened to
an image plane and decisions such as viewpoint and camera parameters are final
and not easily revertible. As a consequence, possibilities of changing
viewpoint are limited. Given a single image depicting an object, novel-view
synthesis is the task of generating new images that render the object from a
different viewpoint than the one given. The main difficulty is to synthesize
the parts that are disoccluded; disocclusion occurs when parts of an object are
hidden by the object itself under a specific viewpoint. In this work, we show
how to improve novel-view synthesis by making use of the correlations observed
in 3D models and applying them to new image instances. We propose a technique
to use the structural information extracted from a 3D model that matches the
image object in terms of viewpoint and shape. For the latter part, we propose
an efficient 2D-to-3D alignment method that associates precisely the image
appearance with the 3D model geometry with minimal user interaction. Our
technique is able to simulate plausible viewpoint changes for a variety of
object classes within seconds. Additionally, we show that our synthesized
images can be used as additional training data that improves the performance of
standard object detectors.
Numerically Grounded Language Models for Semantic Error Correction
Authors: Georgios P. Spithourakis, Isabelle Augenstein, Sebastian Riedel
Date: 2016-08-14 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Fact Checking, Grammatical Error Correction, Language Modeling, Language Modelling, Speech-to-Text

Semantic error detection and correction is an important task for applications
such as fact checking, speech-to-text or grammatical error correction. Current
approaches generally focus on relatively shallow semantics and do not account
for numeric quantities. Our approach uses language models grounded in numbers
within the text. Such groundings are easily achieved for recurrent neural
language model architectures, which can be further conditioned on incomplete
background knowledge bases. Our evaluation on clinical reports shows that
numerical grounding improves perplexity by 33% and F1 for semantic error
correction by 5 points when compared to ungrounded approaches. Conditioning on
a knowledge base yields further improvements.
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Authors: Tim Althoff, Kevin Clark, Jure Leskovec
Date: 2016-05-14 00:00:00
Proceeding: TACL 2016 1
Tasks: Clustering, Language Modeling, Language Modelling

Mental illness is one of the most pressing public health issues of our time.
While counseling and psychotherapy can be effective treatments, our knowledge
about how to conduct successful counseling conversations has been limited due
to lack of large-scale data with labeled outcomes of the conversations. In this
paper, we present a large-scale, quantitative study on the discourse of
text-message-based counseling conversations. We develop a set of novel
computational discourse analysis methods to measure how various linguistic
aspects of conversations are correlated with conversation outcomes. Applying
techniques such as sequence-based conversation models, language model
comparisons, message clustering, and psycholinguistics-inspired word frequency
analyses, we discover actionable conversation strategies that are associated
with better conversation outcomes.
Agnostic Estimation of Mean and Covariance
Authors: Kevin A. Lai, Anup B. Rao, Santosh Vempala
Date: 2016-04-24 00:00:00

We consider the problem of estimating the mean and covariance of a
distribution from iid samples in $\mathbb{R}^n$, in the presence of an $\eta$
fraction of malicious noise; this is in contrast to much recent work where the
noise itself is assumed to be from a distribution of known type. The agnostic
problem includes many interesting special cases, e.g., learning the parameters
of a single Gaussian (or finding the best-fit Gaussian) when $\eta$ fraction of
data is adversarially corrupted, agnostically learning a mixture of Gaussians,
agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean
and covariance with error guarantees in terms of information-theoretic lower
bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value
Decomposition.
Word2Vec is a special case of Kernel Correspondence Analysis and Kernels for Natural Language Processing
Authors: Hirotaka Niitsuma, Minho Lee
Date: 2016-05-17 00:00:00

We show that correspondence analysis (CA) is equivalent to defining a Gini
index with appropriately scaled one-hot encoding. Using this relation, we
introduce a nonlinear kernel extension to CA. This extended CA gives a known
analysis for natural language via specialized kernels that use an appropriate
contingency table. We propose a semi-supervised CA, which is a special case of
the kernel extension to CA. Because CA requires excessive memory if applied to
numerous categories, CA has not been used for natural language processing. We
address this problem by introducing delayed evaluation to randomized singular
value decomposition. The memory-efficient CA is then applied to a word-vector
representation task. We propose a tail-cut kernel, which is an extension to the
skip-gram within the kernel extension to CA. Our tail-cut kernel outperforms
existing word-vector representation methods.
The Spectral Condition Number Plot for Regularization Parameter Determination
Authors: Carel F. W. Peeters, Mark A. van de Wiel, Wessel N. van Wieringen
Date: 2016-08-14 00:00:00
Tasks: Vocal Bursts Type Prediction

Many modern statistical applications ask for the estimation of a covariance
(or precision) matrix in settings where the number of variables is larger than
the number of observations. There exists a broad class of ridge-type estimators
that employs regularization to cope with the subsequent singularity of the
sample covariance matrix. These estimators depend on a penalty parameter and
choosing its value can be hard, in terms of being computationally unfeasible or
tenable only for a restricted set of ridge-type estimators. Here we introduce a
simple graphical tool, the spectral condition number plot, for informed
heuristic penalty parameter selection. The proposed tool is computationally
friendly and can be employed for the full class of ridge-type covariance
(precision) estimators.
Proceedings of the LexSem+Logics Workshop 2016
Authors: Steven Neale, Valeria de Paiva, Arantxa Otegi, Alexandre Rademaker
Date: 2016-08-14 00:00:00
Tasks: Entity Disambiguation, Open Information Extraction, Part-Of-Speech Tagging, Semantic Similarity, Semantic Textual Similarity

Lexical semantics continues to play an important role in driving research
directions in NLP, with the recognition and understanding of context becoming
increasingly important in delivering successful outcomes in NLP tasks. Besides
traditional processing areas such as word sense and named entity
disambiguation, the creation and maintenance of dictionaries, annotated corpora
and resources have become cornerstones of lexical semantics research and
produced a wealth of contextual information that NLP processes can exploit. New
efforts both to link and construct from scratch such information - as Linked
Open Data or by way of formal tools coming from logic, ontologies and automated
reasoning - have increased the interoperability and accessibility of resources
for lexical and computational semantics, even in those languages for which they
have previously been limited.
  LexSem+Logics 2016 combines the 1st Workshop on Lexical Semantics for
Lesser-Resources Languages and the 3rd Workshop on Logics and Ontologies. The
accepted papers in our program covered topics across these two areas,
including: the encoding of plurals in Wordnets, the creation of a thesaurus
from multiple sources based on semantic similarity metrics, and the use of
cross-lingual treebanks and annotations for universal part-of-speech tagging.
We also welcomed talks from two distinguished speakers: on Portuguese lexical
knowledge bases (different approaches, results and their application in NLP
tasks) and on new strategies for open information extraction (the capture of
verb-based propositions from massive text corpora).
Depth and depth-based classification with R-package ddalpha
Authors: Oleksii Pokotylo, Pavlo Mozharovskyi, Rainer Dyckerhoff
Date: 2016-08-14 00:00:00
Tasks: Classification, General Classification

Following the seminal idea of Tukey, data depth is a function that measures
how close an arbitrary point of the space is located to an implicitly defined
center of a data cloud. Having undergone theoretical and computational
developments, it is now employed in numerous applications with classification
being the most popular one. The R-package ddalpha is a software directed to
fuse experience of the applicant with recent achievements in the area of data
depth and depth-based classification.
  ddalpha provides an implementation for exact and approximate computation of
most reasonable and widely applied notions of data depth. These can be further
used in the depth-based multivariate and functional classifiers implemented in
the package, where the $DD\alpha$-procedure is in the main focus. The package
is expandable with user-defined custom depth methods and separators. The
implemented functions for depth visualization and the built-in benchmark
procedures may also serve to provide insights into the geometry of the data and
the quality of pattern recognition.
Coordinate Friendly Structures, Algorithms and Applications
Authors: Zhimin Peng, Tianyu Wu, Yangyang Xu, Ming Yan, Wotao Yin
Date: 2016-01-05 00:00:00

This paper focuses on coordinate update methods, which are useful for solving
problems involving large or high-dimensional datasets. They decompose a problem
into simple subproblems, where each updates one, or a small block of, variables
while fixing others. These methods can deal with linear and nonlinear mappings,
smooth and nonsmooth functions, as well as convex and nonconvex problems. In
addition, they are easy to parallelize.
  The great performance of coordinate update methods depends on solving simple
sub-problems. To derive simple subproblems for several new classes of
applications, this paper systematically studies coordinate-friendly operators
that perform low-cost coordinate updates.
  Based on the discovered coordinate friendly operators, as well as operator
splitting techniques, we obtain new coordinate update algorithms for a variety
of problems in machine learning, image processing, as well as sub-areas of
optimization. Several problems are treated with coordinate update for the first
time in history. The obtained algorithms are scalable to large instances
through parallel and even asynchronous computing. We present numerical examples
to illustrate how effective these algorithms are.
Machine Learning with Memristors via Thermodynamic RAM
Authors: Timothy W. Molter, M. Alexander Nugent
Date: 2016-08-14 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, CPU, GPU

Thermodynamic RAM (kT-RAM) is a neuromemristive co-processor design based on
the theory of AHaH Computing and implemented via CMOS and memristors. The
co-processor is a 2-D array of differential memristor pairs (synapses) that can
be selectively coupled together (neurons) via the digital bit addressing of the
underlying CMOS RAM circuitry. The chip is designed to plug into existing
digital computers and be interacted with via a simple instruction set.
Anti-Hebbian and Hebbian (AHaH) computing forms the theoretical framework from
which a nature-inspired type of computing architecture is built where, unlike
von Neumann architectures, memory and processor are physically combined for
synaptic operations. Through exploitation of AHaH attractor states,
memristor-based circuits converge to attractor basins that represents machine
learning solutions such as unsupervised feature learning, supervised
classification and anomaly detection. Because kT-RAM eliminates the need to
shuttle bits back and forth between memory and processor and can operate at
very low voltage levels, it can significantly surpass CPU, GPU, and FPGA
performance for synaptic integration and learning operations. Here, we present
a memristor technology developed for use in kT-RAM, in particular
bi-directional incremental adaptation of conductance via short low-voltage 1.0
V, 1.0 microsecond pulses.
Viewpoint and Topic Modeling of Current Events
Authors: Kerry Zhang, Jussi Karlgren, Cheng Zhang, Jens Lagergren
Date: 2016-08-14 00:00:00
Tasks: Form, Topic Models

There are multiple sides to every story, and while statistical topic models
have been highly successful at topically summarizing the stories in corpora of
text documents, they do not explicitly address the issue of learning the
different sides, the viewpoints, expressed in the documents. In this paper, we
show how these viewpoints can be learned completely unsupervised and
represented in a human interpretable form. We use a novel approach of applying
CorrLDA2 for this purpose, which learns topic-viewpoint relations that can be
used to form groups of topics, where each group represents a viewpoint. A
corpus of documents about the Israeli-Palestinian conflict is then used to
demonstrate how a Palestinian and an Israeli viewpoint can be learned. By
leveraging the magnitudes and signs of the feature weights of a linear SVM, we
introduce a principled method to evaluate associations between topics and
viewpoints. With this, we demonstrate, both quantitatively and qualitatively,
that the learned topic groups are contextually coherent, and form consistently
correct topic-viewpoint associations.
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks
Authors: Sungho Shin, Wonyong Sung
Date: 2016-08-14 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Gesture recognition is a very essential technology for many wearable devices.
While previous algorithms are mostly based on statistical methods including the
hidden Markov model, we develop two dynamic hand gesture recognition techniques
using low complexity recurrent neural network (RNN) algorithms. One is based on
video signal and employs a combined structure of a convolutional neural network
(CNN) and an RNN. The other uses accelerometer data and only requires an RNN.
Fixed-point optimization that quantizes most of the weights into two bits is
conducted to optimize the amount of memory size for weight storage and reduce
the power consumption in hardware and software based implementations.
About Pyramid Structure in Convolutional Neural Networks
Authors: Ihsan Ullah, Alfredo Petrosino
Date: 2016-08-14 00:00:00

Deep convolutional neural networks (CNN) brought revolution without any doubt
to various challenging tasks, mainly in computer vision. However, their model
designing still requires attention to reduce number of learnable parameters,
with no meaningful reduction in performance. In this paper we investigate to
what extend CNN may take advantage of pyramid structure typical of biological
neurons. A generalized statement over convolutional layers from input till
fully connected layer is introduced that helps further in understanding and
designing a successful deep network. It reduces ambiguity, number of
parameters, and their size on disk without degrading overall accuracy.
Performance are shown on state-of-the-art models for MNIST, Cifar-10,
Cifar-100, and ImageNet-12 datasets. Despite more than 80% reduction in
parameters for Caffe_LENET, challenging results are obtained. Further, despite
10-20% reduction in training data along with 10-40% reduction in parameters for
AlexNet model and its variations, competitive results are achieved when
compared to similar well-engineered deeper architectures.
Bayesian Model Selection Methods for Mutual and Symmetric $k$-Nearest Neighbor Classification
Authors: Hyun-Chul Kim
Date: 2016-08-14 00:00:00
Tasks: Classification, General Classification, Model Selection, regression

The $k$-nearest neighbor classification method ($k$-NNC) is one of the
simplest nonparametric classification methods. The mutual $k$-NN classification
method (M$k$NNC) is a variant of $k$-NNC based on mutual neighborship. We
propose another variant of $k$-NNC, the symmetric $k$-NN classification method
(S$k$NNC) based on both mutual neighborship and one-sided neighborship. The
performance of M$k$NNC and S$k$NNC depends on the parameter $k$ as the one of
$k$-NNC does. We propose the ways how M$k$NN and S$k$NN classification can be
performed based on Bayesian mutual and symmetric $k$-NN regression methods with
the selection schemes for the parameter $k$. Bayesian mutual and symmetric
$k$-NN regression methods are based on Gaussian process models, and it turns
out that they can do M$k$NN and S$k$NN classification with new encodings of
target values (class labels). The simulation results show that the proposed
methods are better than or comparable to $k$-NNC, M$k$NNC and S$k$NNC with the
parameter $k$ selected by the leave-one-out cross validation method not only
for an artificial data set but also for real world data sets.