pwc-archive/papers-with-abstracts huggingface dataset:
SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis
Authors: Lorenzo Gatti, Marco Guerini, Marco Turchi
Date: 2015-10-30 00:00:00
Tasks: Sentiment Analysis, Vocal Bursts Intensity Prediction

Deriving prior polarity lexica for sentiment analysis - where positive or
negative scores are associated with words out of context - is a challenging
task. Usually, a trade-off between precision and coverage is hard to find, and
it depends on the methodology used to build the lexicon. Manually annotated
lexica provide a high precision but lack in coverage, whereas automatic
derivation from pre-existing knowledge guarantees high coverage at the cost of
a lower precision. Since the automatic derivation of prior polarities is less
time consuming than manual annotation, there has been a great bloom of these
approaches, in particular based on the SentiWordNet resource. In this paper, we
compare the most frequently used techniques based on SentiWordNet with newer
ones and blend them in a learning framework (a so called 'ensemble method'). By
taking advantage of manually built prior polarity lexica, our ensemble method
is better able to predict the prior value of unseen words and to outperform all
the other SentiWordNet approaches. Using this technique we have built
SentiWords, a prior polarity lexicon of approximately 155,000 words, that has
both a high precision and a high coverage. We finally show that in sentiment
analysis tasks, using our lexicon allows us to outperform both the single
metrics derived from SentiWordNet and popular manually annotated sentiment
lexica.
Turing's Red Flag
Authors: Toby Walsh
Date: 2015-10-30 00:00:00

Sometime in the future we will have to deal with the impact of AI's being
mistaken for humans. For this reason, I propose that any autonomous system
should be designed so that it is unlikely to be mistaken for anything besides
an autonomous sysem, and should identify itself at the start of any interaction
with another agent.
A Study of the Spatio-Temporal Correlations in Mobile Calls Networks
Authors: Romain Guigourès, Marc Boullé, Fabrice Rossi
Date: 2015-10-30 00:00:00

For the last few years, the amount of data has significantly increased in the
companies. It is the reason why data analysis methods have to evolve to meet
new demands. In this article, we introduce a practical analysis of a large
database from a telecommunication operator. The problem is to segment a
territory and characterize the retrieved areas owing to their inhabitant
behavior in terms of mobile telephony. We have call detail records collected
during five months in France. We propose a two stages analysis. The first one
aims at grouping source antennas which originating calls are similarly
distributed on target antennas and conversely for target antenna w.r.t. source
antenna. A geographic projection of the data is used to display the results on
a map of France. The second stage discretizes the time into periods between
which we note changes in distributions of calls emerging from the clusters of
source antennas. This enables an analysis of temporal changes of inhabitants
behavior in every area of the country.
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
Authors: Justin Domke
Date: 2015-09-30 00:00:00
Proceeding: NeurIPS 2015 12

Inference is typically intractable in high-treewidth undirected graphical
models, making maximum likelihood learning a challenge. One way to overcome
this is to restrict parameters to a tractable set, most typically the set of
tree-structured parameters. This paper explores an alternative notion of a
tractable set, namely a set of "fast-mixing parameters" where Markov chain
Monte Carlo (MCMC) inference can be guaranteed to quickly converge to the
stationary distribution. While it is common in practice to approximate the
likelihood gradient using samples obtained from MCMC, such procedures lack
theoretical guarantees. This paper proves that for any exponential family with
bounded sufficient statistics, (not just graphical models) when parameters are
constrained to a fast-mixing set, gradient descent with gradients approximated
by sampling will approximate the maximum likelihood solution inside the set
with high-probability. When unregularized, to find a solution epsilon-accurate
in log-likelihood requires a total amount of effort cubic in 1/epsilon,
disregarding logarithmic factors. When ridge-regularized, strong convexity
allows a solution epsilon-accurate in parameter distance with effort quadratic
in 1/epsilon. Both of these provide of a fully-polynomial time randomized
approximation scheme.
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition
Authors: Yu Zhang, Ekapol Chuangsuwanich, James Glass, Dong Yu
Date: 2015-10-30 00:00:00
Tasks: Prediction, speech-recognition, Speech Recognition, Transfer Learning

In this paper, we investigate the use of prediction-adaptation-correction
recurrent neural networks (PAC-RNNs) for low-resource speech recognition. A
PAC-RNN is comprised of a pair of neural networks in which a {\it correction}
network uses auxiliary information given by a {\it prediction} network to help
estimate the state probability. The information from the correction network is
also used by the prediction network in a recurrent loop. Our model outperforms
other state-of-the-art neural networks (DNNs, LSTMs) on IARPA-Babel tasks.
Moreover, transfer learning from a language that is similar to the target
language can help improve performance further.
CONQUER: Confusion Queried Online Bandit Learning
Authors: Daniel Barsky, Koby Crammer
Date: 2015-10-30 00:00:00

We present a new recommendation setting for picking out two items from a
given set to be highlighted to a user, based on contextual input. These two
items are presented to a user who chooses one of them, possibly stochastically,
with a bias that favours the item with the higher value. We propose a
second-order algorithm framework that members of it use uses relative
upper-confidence bounds to trade off exploration and exploitation, and some
explore via sampling. We analyze one algorithm in this framework in an
adversarial setting with only mild assumption on the data, and prove a regret
bound of $O(Q_T + \sqrt{TQ_T\log T} + \sqrt{T}\log T)$, where $T$ is the number
of rounds and $Q_T$ is the cumulative approximation error of item values using
a linear model. Experiments with product reviews from 33 domains show the
advantage of our methods over algorithms designed for related settings, and
that UCB based algorithms are inferior to greed or sampling based algorithms.
VISALOGY: Answering Visual Analogy Questions
Authors: Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi
Date: 2015-10-30 00:00:00
Proceeding: NeurIPS 2015 12

In this paper, we study the problem of answering visual analogy questions.
These questions take the form of image A is to image B as image C is to what.
Answering these questions entails discovering the mapping from image A to image
B and then extending the mapping to image C and searching for the image D such
that the relation from A to B holds for C to D. We pose this problem as
learning an embedding that encourages pairs of analogous images with similar
transformations to be close together using convolutional neural networks with a
quadruple Siamese architecture. We introduce a dataset of visual analogy
questions in natural images, and show first results of its kind on solving
analogy questions on natural images.
Robust Subspace Clustering via Tighter Rank Approximation
Authors: Zhao Kang, Chong Peng, Qiang Cheng
Date: 2015-10-30 00:00:00
Tasks: Clustering, Face Clustering, Motion Segmentation

Matrix rank minimization problem is in general NP-hard. The nuclear norm is
used to substitute the rank function in many recent studies. Nevertheless, the
nuclear norm approximation adds all singular values together and the
approximation error may depend heavily on the magnitudes of singular values.
This might restrict its capability in dealing with many practical problems. In
this paper, an arctangent function is used as a tighter approximation to the
rank function. We use it on the challenging subspace clustering problem. For
this nonconvex minimization problem, we develop an effective optimization
procedure based on a type of augmented Lagrange multipliers (ALM) method.
Extensive experiments on face clustering and motion segmentation show that the
proposed method is effective for rank approximation.
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
Authors: Jonas Mueller, Tommi Jaakkola
Date: 2015-10-30 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Hippocampus

We introduce principal differences analysis (PDA) for analyzing differences
between high-dimensional distributions. The method operates by finding the
projection that maximizes the Wasserstein divergence between the resulting
univariate populations. Relying on the Cramer-Wold device, it requires no
assumptions about the form of the underlying distributions, nor the nature of
their inter-class differences. A sparse variant of the method is introduced to
identify features responsible for the differences. We provide algorithms for
both the original minimax formulation as well as its semidefinite relaxation.
In addition to deriving some convergence results, we illustrate how the
approach may be applied to identify differences between cell populations in the
somatosensory cortex and hippocampus as manifested by single cell RNA-seq. Our
broader framework extends beyond the specific choice of Wasserstein divergence.
Testing Visual Attention in Dynamic Environments
Authors: Philip Bachman, David Krueger, Doina Precup
Date: 2015-10-30 00:00:00
Tasks: Variational Inference

We investigate attention as the active pursuit of useful information. This
contrasts with attention as a mechanism for the attenuation of irrelevant
information. We also consider the role of short-term memory, whose use is
critical to any model incapable of simultaneously perceiving all information on
which its output depends. We present several simple synthetic tasks, which
become considerably more interesting when we impose strong constraints on how a
model can interact with its input, and on how long it can take to produce its
output. We develop a model with a different structure from those seen in
previous work, and we train it using stochastic variational inference with a
learned proposal distribution.
Improving the Speed of Response of Learning Algorithms Using Multiple Models
Authors: Kumpati S. Narendra, Snehasis Mukhopadyhay, Yu Wang
Date: 2015-10-16 00:00:00

This is the first of a series of papers that the authors propose to write on
the subject of improving the speed of response of learning systems using
multiple models. During the past two decades, the first author has worked on
numerous methods for improving the stability, robustness, and performance of
adaptive systems using multiple models and the other authors have collaborated
with him on some of them. Independently, they have also worked on several
learning methods, and have considerable experience with their advantages and
limitations. In particular, they are well aware that it is common knowledge
that machine learning is in general very slow. Numerous attempts have been made
by researchers to improve the speed of convergence of algorithms in different
contexts. In view of the success of multiple model based methods in improving
the speed of convergence in adaptive systems, the authors believe that the same
approach will also prove fruitful in the domain of learning. In this paper, a
first attempt is made to use multiple models for improving the speed of
response of the simplest learning schemes that have been studied. i.e. Learning
Automata.
A Deep Siamese Network for Scene Detection in Broadcast Videos
Authors: Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Date: 2015-10-29 00:00:00
Tasks: Scene Segmentation

We present a model that automatically divides broadcast videos into coherent
scenes by learning a distance measure between shots. Experiments are performed
to demonstrate the effectiveness of our approach by comparing our algorithm
against recent proposals for automatic scene segmentation. We also propose an
improved performance measure that aims to reduce the gap between numerical
evaluation and expected results, and propose and release a new benchmark
dataset.
Spiking Deep Networks with LIF Neurons
Authors: Eric Hunsberger, Chris Eliasmith
Date: 2015-10-29 00:00:00
Tasks: General Classification, image-classification, Image Classification

We train spiking deep networks using leaky integrate-and-fire (LIF) neurons,
and achieve state-of-the-art results for spiking networks on the CIFAR-10 and
MNIST datasets. This demonstrates that biologically-plausible spiking LIF
neurons can be integrated into deep networks can perform as well as other
spiking models (e.g. integrate-and-fire). We achieved this result by softening
the LIF response function, such that its derivative remains bounded, and by
training the network with noise to provide robustness against the variability
introduced by spikes. Our method is general and could be applied to other
neuron types, including those used on modern neuromorphic hardware. Our work
brings more biological realism into modern image classification models, with
the hope that these models can inform how the brain performs this difficult
task. It also provides new methods for training deep networks to run on
neuromorphic hardware, with the aim of fast, power-efficient image
classification for robotics applications.
Approximate Counting in SMT and Value Estimation for Probabilistic Programs
Authors: Dmitry Chistikov, Rayna Dimitrova, Rupak Majumdar
Date: 2014-11-03 00:00:00

#SMT, or model counting for logical theories, is a well-known hard problem
that generalizes such tasks as counting the number of satisfying assignments to
a Boolean formula and computing the volume of a polytope. In the realm of
satisfiability modulo theories (SMT) there is a growing need for model counting
solvers, coming from several application domains (quantitative information
flow, static analysis of probabilistic programs). In this paper, we show a
reduction from an approximate version of #SMT to SMT.
  We focus on the theories of integer arithmetic and linear real arithmetic. We
propose model counting algorithms that provide approximate solutions with
formal bounds on the approximation error. They run in polynomial time and make
a polynomial number of queries to the SMT solver for the underlying theory,
exploiting "for free" the sophisticated heuristics implemented within modern
SMT solvers. We have implemented the algorithms and used them to solve the
value problem for a model of loop-free probabilistic programs with
nondeterminism.
Towards a General Framework for Actual Causation Using CP-logic
Authors: Sander Beckers, Joost Vennekens
Date: 2014-10-26 00:00:00

Since Pearl's seminal work on providing a formal language for causality, the
subject has garnered a lot of interest among philosophers and researchers in
artificial intelligence alike. One of the most debated topics in this context
regards the notion of actual causation, which concerns itself with specific -
as opposed to general - causal claims. The search for a proper formal
definition of actual causation has evolved into a controversial debate, that is
pervaded with ambiguities and confusion. The goal of our research is twofold.
First, we wish to provide a clear way to compare competing definitions. Second,
we also want to improve upon these definitions so they can be applied to a more
diverse range of instances, including non-deterministic ones. To achieve these
goals we will provide a general, abstract definition of actual causation,
formulated in the context of the expressive language of CP-logic (Causal
Probabilistic logic). We will then show that three recent definitions by Ned
Hall (originally formulated for structural models) and a definition of our own
(formulated for CP-logic directly) can be viewed and directly compared as
instantiations of this abstract definition, which allows them to deal with a
broader range of examples.
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
Authors: Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
Date: 2015-10-29 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: BIG-bench Machine Learning

Monte Carlo sampling for Bayesian posterior inference is a common approach used in machine learning. The Markov Chain Monte Carlo procedures that are used are often discrete-time analogues of associated stochastic differential equations (SDEs). These SDEs are guaranteed to leave invariant the required posterior distribution. An area of current research addresses the computational benefits of stochastic gradient methods in this setting. Existing techniques rely on estimating the variance or covariance of the subsampling error, and typically assume constant variance. In this article, we propose a covariance-controlled adaptive Langevin thermostat that can effectively dissipate parameter-dependent noise while maintaining a desired target distribution. The proposed method achieves a substantial speedup over popular alternative schemes for large-scale machine learning applications.
Visual Tracking via Nonnegative Regularization Multiple Locality Coding
Authors: Fanghui Liu, Tao Zhou, Irene Y. H. Gu, Jie Yang
Date: 2015-10-05 00:00:00
Tasks: Object Tracking, Visual Tracking

This paper presents a novel object tracking method based on approximated
Locality-constrained Linear Coding (LLC). Rather than using a non-negativity
constraint on encoding coefficients to guarantee these elements nonnegative, in
this paper, the non-negativity constraint is substituted for a conventional
$\ell_2$ norm regularization term in approximated LLC to obtain the similar
nonnegative effect. And we provide a detailed and adequate explanation in
theoretical analysis to clarify the rationality of this replacement. Instead of
specifying fixed K nearest neighbors to construct the local dictionary, a
series of different dictionaries with pre-defined numbers of nearest neighbors
are selected. Weights of these various dictionaries are also learned from
approximated LLC in the similar framework. In order to alleviate tracking
drifts, we propose a simple and efficient occlusion detection method. The
occlusion detection criterion mainly depends on whether negative templates are
selected to represent the severe occluded target. Both qualitative and
quantitative evaluations on several challenging sequences show that the
proposed tracking algorithm achieves favorable performance compared with other
state-of-the-art methods.
Nonconvex Penalization in Sparse Estimation: An Approach Based on the Bernstein Function
Authors: Zhihua Zhang
Date: 2015-10-29 00:00:00
Tasks: General Classification, regression

In this paper we study nonconvex penalization using Bernstein functions whose
first-order derivatives are completely monotone. The Bernstein function can
induce a class of nonconvex penalty functions for high-dimensional sparse
estimation problems. We derive a thresholding function based on the Bernstein
penalty and discuss some important mathematical properties in sparsity
modeling. We show that a coordinate descent algorithm is especially appropriate
for regression problems penalized by the Bernstein function. We also consider
the application of the Bernstein penalty in classification problems and devise
a proximal alternating linearized minimization method. Based on theory of the
Kurdyka-Lojasiewicz inequality, we conduct convergence analysis of these
alternating iteration procedures. We particularly exemplify a family of
Bernstein nonconvex penalties based on a generalized Gamma measure and conduct
empirical analysis for this family.
Transalg: a Tool for Translating Procedural Descriptions of Discrete Functions to SAT
Authors: Ilya Otpuschennikov, Alexander Semenov, Stepan Kochemazov
Date: 2014-05-07 00:00:00
Tasks: Translation

In this paper we present the Transalg system, designed to produce SAT
encodings for discrete functions, written as programs in a specific language.
Translation of such programs to SAT is based on propositional encoding methods
for formal computing models and on the concept of symbolic execution. We used
the Transalg system to make SAT encodings for a number of cryptographic
functions.
Higher-order asymptotics for the parametric complexity
Authors: James G. Dowty
Date: 2015-10-01 00:00:00
Tasks: Model Selection

The parametric complexity is the key quantity in the minimum description
length (MDL) approach to statistical model selection. Rissanen and others have
shown that the parametric complexity of a statistical model approaches a simple
function of the Fisher information volume of the model as the sample size $n$
goes to infinity. This paper derives higher-order asymptotic expansions for the
parametric complexity, in the case of exponential families and independent and
identically distributed data. These higher-order approximations are calculated
for some examples and are shown to have better finite-sample behaviour than
Rissanen's approximation. The higher-order terms are given as expressions
involving cumulants (or, more naturally, the Amari-Chentsov tensors), and these
terms are likely to be interesting in themselves since they arise naturally
from the general information-theoretic principles underpinning MDL. The
derivation given here specializes to an alternative and arguably simpler proof
of Rissanen's result (for the case considered here), proving for the first time
that his approximation is $O(n^{-1})$.
The Singular Value Decomposition, Applications and Beyond
Authors: Zhihua Zhang
Date: 2015-10-29 00:00:00
Tasks: BIG-bench Machine Learning, Matrix Completion

The singular value decomposition (SVD) is not only a classical theory in
matrix computation and analysis, but also is a powerful tool in machine
learning and modern data analysis. In this tutorial we first study the basic
notion of SVD and then show the central role of SVD in matrices. Using
majorization theory, we consider variational principles of singular values and
eigenvalues. Built on SVD and a theory of symmetric gauge functions, we discuss
unitarily invariant norms, which are then used to formulate general results for
matrix low rank approximation. We study the subdifferentials of unitarily
invariant norms. These results would be potentially useful in many machine
learning problems such as matrix completion and matrix data classification.
Finally, we discuss matrix low rank approximation and its recent developments
such as randomized SVD, approximate matrix multiplication, CUR decomposition,
and Nystrom approximation. Randomized algorithms are important approaches to
large scale SVD as well as fast matrix computations.
Automatic Synthesis of Geometry Problems for an Intelligent Tutoring System
Authors: Chris Alvin, Sumit Gulwani, Rupak Majumdar, Supratik Mukhopadhyay
Date: 2015-10-29 00:00:00

This paper presents an intelligent tutoring system, GeoTutor, for Euclidean
Geometry that is automatically able to synthesize proof problems and their
respective solutions given a geometric figure together with a set of properties
true of it. GeoTutor can provide personalized practice problems that address
student deficiencies in the subject matter.