pwc-archive/papers-with-abstracts huggingface dataset:
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
Authors: Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
Date: 2014-11-06 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Diversity, Sentence, Structured Prediction

To cope with the high level of ambiguity faced in domains such as Computer
Vision or Natural Language processing, robust prediction methods often search
for a diverse set of high-quality candidate solutions or proposals. In
structured prediction problems, this becomes a daunting task, as the solution
space (image labelings, sentence parses, etc.) is exponentially large. We study
greedy algorithms for finding a diverse subset of solutions in
structured-output spaces by drawing new connections between submodular
functions over combinatorial item sets and High-Order Potentials (HOPs) studied
for graphical models. Specifically, we show via examples that when marginal
gains of submodular diversity functions allow structured representations, this
enables efficient (sub-linear time) approximate maximization by reducing the
greedy augmentation step to inference in a factor graph with appropriately
constructed HOPs. We discuss benefits, tradeoffs, and show that our
constructions lead to significantly better proposals.
Stochastic Variational Inference for Hidden Markov Models
Authors: Nicholas J. Foti, Jason Xu, Dillon Laird, Emily B. Fox
Date: 2014-11-06 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Stochastic Optimization, Variational Inference

Variational inference algorithms have proven successful for Bayesian analysis
in large data settings, with recent advances using stochastic variational
inference (SVI). However, such methods have largely been studied in independent
or exchangeable data settings. We develop an SVI algorithm to learn the
parameters of hidden Markov models (HMMs) in a time-dependent data setting. The
challenge in applying stochastic optimization in this setting arises from
dependencies in the chain, which must be broken to consider minibatches of
observations. We propose an algorithm that harnesses the memory decay of the
chain to adaptively bound errors arising from edge effects. We demonstrate the
effectiveness of our algorithm on synthetic experiments and a large genomics
dataset where a batch algorithm is computationally infeasible.
Learning Word Representations with Hierarchical Sparse Coding
Authors: Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah A. Smith
Date: 2014-06-08 00:00:00
Tasks: Sentence, Sentence Completion, Sentiment Analysis, Word Similarity

We propose a new method for learning word representations using hierarchical
regularization in sparse coding inspired by the linguistic study of word
meanings. We show an efficient learning algorithm based on stochastic proximal
methods that is significantly faster than previous approaches, making it
possible to perform hierarchical sparse coding on a corpus of billions of word
tokens. Experiments on various benchmark tasks---word similarity ranking,
analogies, sentence completion, and sentiment analysis---demonstrate that the
method outperforms or is competitive with state-of-the-art methods. Our word
representations are available at
\url{http://www.ark.cs.cmu.edu/dyogatam/wordvecs/}.
A Hybrid Recurrent Neural Network For Music Transcription
Authors: Siddharth Sigtia, Emmanouil Benetos, Nicolas Boulanger-Lewandowski, Tillman Weyde, Artur S. d'Avila Garcez, Simon Dixon
Date: 2014-11-06 00:00:00
Tasks: Music Transcription

We investigate the problem of incorporating higher-level symbolic score-like
information into Automatic Music Transcription (AMT) systems to improve their
performance. We use recurrent neural networks (RNNs) and their variants as
music language models (MLMs) and present a generative architecture for
combining these models with predictions from a frame level acoustic classifier.
We also compare different neural network architectures for acoustic modeling.
The proposed model computes a distribution over possible output sequences given
the acoustic input signal and we present an algorithm for performing a global
search for good candidate transcriptions. The performance of the proposed model
is evaluated on piano music from the MAPS dataset and we observe that the
proposed model consistently outperforms existing transcription methods.
Rapid Skill Capture in a First-Person Shooter
Authors: David Buckley, Ke Chen, Joshua Knowles
Date: 2014-11-05 00:00:00
Tasks: Game Design

Various aspects of computer game design, including adaptive elements of game
levels, characteristics of 'bot' behavior, and player matching in multiplayer
games, would ideally be sensitive to a player's skill level. Yet, while
difficulty and player learning have been explored in the context of games,
there has been little work analyzing skill per se, and how it pertains to a
player's input. To this end, we present a data set of 476 game logs from over
40 players of a first-person shooter game (Red Eclipse) as a basis of a case
study. We then analyze different metrics of skill and show that some of these
can be predicted using only a few seconds of keyboard and mouse input. We argue
that the techniques used here are useful for adapting games to match players'
skill levels rapidly, perhaps more rapidly than solutions based on performance
averaging such as TrueSkill.
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
Authors: Tom Claassen, Joris M. Mooij, Tom Heskes
Date: 2014-11-06 00:00:00
Tasks: Model Discovery, Selection bias

This article contains detailed proofs and additional examples related to the
UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It
describes the FCI+ algorithm: a method for sound and complete causal model
discovery in the presence of latent confounders and/or selection bias, that has
worst case polynomial complexity of order $N^{2(k+1)}$ in the number of
independence tests, for sparse graphs over $N$ nodes, bounded by node degree
$k$. The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes
et al., 2000) that is also sound and complete, but has worst case complexity
exponential in $N$.
Convolutional Neural Network-based Place Recognition
Authors: Zetao Chen, Obadiah Lam, Adam Jacobson, Michael Milford
Date: 2014-11-06 00:00:00
Tasks: General Classification

Recently Convolutional Neural Networks (CNNs) have been shown to achieve
state-of-the-art performance on various classification tasks. In this paper, we
present for the first time a place recognition technique based on CNN models,
by combining the powerful features learnt by CNNs with a spatial and sequential
filter. Applying the system to a 70 km benchmark place recognition dataset we
achieve a 75% increase in recall at 100% precision, significantly outperforming
all previous state of the art techniques. We also conduct a comprehensive
performance comparison of the utility of features from all 21 layers for place
recognition, both for the benchmark dataset and for a second dataset with more
significant viewpoint changes.
Scalable Parallel Numerical CSP Solver
Authors: Daisuke Ishii, Kazuki Yoshizoe, Toyotaro Suzumura
Date: 2014-11-06 00:00:00

We present a parallel solver for numerical constraint satisfaction problems
(NCSPs) that can scale on a number of cores. Our proposed method runs worker
solvers on the available cores and simultaneously the workers cooperate for the
search space distribution and balancing. In the experiments, we attained up to
119-fold speedup using 256 cores of a parallel computer.
The Spaces of Data, Information, and Knowledge
Authors: Xiaoyu Chen, Dongming Wang
Date: 2014-11-06 00:00:00
Tasks: Retrieval

We study the data space $D$ of any given data set $X$ and explain how
functions and relations are defined over $D$. From $D$ and for a specific
domain $\Delta$ we construct the information space $I$ of $X$ by interpreting
variables, functions, and explicit relations over $D$ in $\Delta$ and by
including other relations that $D$ implies under the interpretation in
$\Delta$. Then from $I$ we build up the knowledge space $K$ of $X$ as the
product of two spaces $K_T$ and $K_P$, where $K_T$ is obtained from $I$ by
using the induction principle to generalize propositional relations to
quantified relations, the deduction principle to generate new relations, and
standard mechanisms to validate relations and $K_P$ is the space of
specifications of methods with operational instructions which are valid in
$K_T$. Through our construction of the three topological spaces the following
key observation is made clear: the retrieval of information from the given data
set for $\Delta$ consists essentially in mining domain objects and relations,
and the discovery of knowledge from the retrieved information consists
essentially in applying the induction and deduction principles to generate
propositions, synthesizing and modeling the information to generate
specifications of methods with operational instructions, and validating the
propositions and specifications. Based on this observation, efficient
approaches may be designed to discover profound knowledge automatically from
simple data, as demonstrated by the result of our study in the case of
geometry.
Learning to Discover Efficient Mathematical Identities
Authors: Wojciech Zaremba, Karol Kurach, Rob Fergus
Date: 2014-06-06 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Attribute

In this paper we explore how machine learning techniques can be applied to
the discovery of efficient mathematical identities. We introduce an attribute
grammar framework for representing symbolic expressions. Given a set of grammar
rules we build trees that combine different rules, looking for branches which
yield compositions that are analytically equivalent to a target expression, but
of lower computational complexity. However, as the size of the trees grows
exponentially with the complexity of the target expression, brute force search
is impractical for all but the simplest of expressions. Consequently, we
introduce two novel learning approaches that are able to learn from simpler
expressions to guide the tree search. The first of these is a simple n-gram
model, the other being a recursive neural-network. We show how these approaches
enable us to derive complex identities, beyond reach of brute-force search, or
human derivation.
A Generic Sample Splitting Approach for Refined Community Recovery in Stochastic Block Models
Authors: Jing Lei, Lingxue Zhu
Date: 2014-11-06 00:00:00
Tasks: Clustering

We propose and analyze a generic method for community recovery in stochastic
block models and degree corrected block models. This approach can exactly
recover the hidden communities with high probability when the expected node
degrees are of order $\log n$ or higher. Starting from a roughly correct
community partition given by some conventional community recovery algorithm,
this method refines the partition in a cross clustering step. Our results
simplify and extend some of the previous work on exact community recovery,
discovering the key role played by sample splitting. The proposed method is
simple and can be implemented with many practical community recovery
algorithms.
Electrocardiography Separation of Mother and Baby
Authors: Wei Wang
Date: 2014-11-05 00:00:00
Tasks: Electrocardiography (ECG)

Extraction of Electrocardiography (ECG or EKG) signals of mother and baby is
a challenging task, because one single device is used and it receives a mixture
of multiple heart beats. In this paper, we would like to design a filter to
separate the signals from each other.
Optical Character Recognition, Using K-Nearest Neighbors
Authors: Wei Wang
Date: 2014-11-05 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

The problem of optical character recognition, OCR, has been widely discussed
in the literature. Having a hand-written text, the program aims at recognizing
the text. Even though there are several approaches to this issue, it is still
an open problem. In this paper we would like to propose an approach that uses
K-nearest neighbors algorithm, and has the accuracy of more than 90%. The
training and run time is also very short.
Distributed Policy Evaluation Under Multiple Behavior Strategies
Authors: Sergio Valcarcel Macua, Jianshu Chen, Santiago Zazo, Ali H. Sayed
Date: 2013-12-30 00:00:00
Tasks: Reinforcement Learning

We apply diffusion strategies to develop a fully-distributed cooperative
reinforcement learning algorithm in which agents in a network communicate only
with their immediate neighbors to improve predictions about their environment.
The algorithm can also be applied to off-policy learning, meaning that the
agents can predict the response to a behavior different from the actual
policies they are following. The proposed distributed strategy is efficient,
with linear complexity in both computation time and memory footprint. We
provide a mean-square-error performance analysis and establish convergence
under constant step-size updates, which endow the network with continuous
learning capabilities. The results show a clear gain from cooperation: when the
individual agents can estimate the solution, cooperation increases stability
and reduces bias and variance of the prediction error; but, more importantly,
the network is able to approach the optimal solution even when none of the
individual agents can (e.g., when the individual behavior policies restrict
each agent to sample a small portion of the state space).
Online SLAM with Any-time Self-calibration and Automatic Change Detection
Authors: Nima Keivan, Gabe Sibley
Date: 2014-11-05 00:00:00
Tasks: Change Detection

A framework for online simultaneous localization, mapping and
self-calibration is presented which can detect and handle significant change in
the calibration parameters. Estimates are computed in constant-time by
factoring the problem and focusing on segments of the trajectory that are most
informative for the purposes of calibration. A novel technique is presented to
detect the probability that a significant change is present in the calibration
parameters. The system is then able to re-calibrate. Maximum likelihood
trajectory and map estimates are computed using an asynchronous and adaptive
optimization. The system requires no prior information and is able to
initialize without any special motions or routines, or in the case where
observability over calibration parameters is delayed. The system is
experimentally validated to calibrate camera intrinsic parameters for a
nonlinear camera model on a monocular dataset featuring a significant zoom
event partway through, and achieves high accuracy despite unknown initial
calibration parameters. Self-calibration and re-calibration parameters are
shown to closely match estimates computed using a calibration target. The
accuracy of the system is demonstrated with SLAM results that achieve sub-1%
distance-travel error even in the presence of significant re-calibration
events.
Application of Multi-core Parallel Programming to a Combination of Ant Colony Optimization and Genetic Algorithm
Authors: Rishita Kalyani
Date: 2014-11-05 00:00:00

This Paper will deal with a combination of Ant Colony and Genetic Programming
Algorithm to optimize Travelling Salesmen problem (NP-Hard). However, the
complexity of the algorithm requires considerable computational time and
resources. Parallel implementation can reduce the computational time. In this
paper, emphasis in the parallelizing section is given to Multi-core
architecture and Multi-Processor Systems which is developed and used almost
everywhere today and hence, multi-core parallelization to the combination of
algorithm is achieved by OpenMP library by Intel Corporation.
Edge Detection based on Kernel Density Estimation
Authors: Osvaldo Pereira, Esley Torre, Yasel Garcés, Roberto Rodríguez
Date: 2014-11-05 00:00:00
Tasks: Density Estimation, Edge Detection

Edges of an image are considered a crucial type of information. These can be
extracted by applying edge detectors with different methodology. Edge detection
is a vital step in computer vision tasks, because it is an essential issue for
pattern recognition and visual interpretation. In this paper, we propose a new
method for edge detection in images, based on the estimation by kernel of the
probability density function. In our algorithm, pixels in the image with
minimum value of density function are labeled as edges. The boundary between
two homogeneous regions is defined in two domains: the spatial/lattice domain
and the range/color domain. Extensive experimental evaluations proved that our
edge detection method is significantly a competitive algorithm.
Controlling false discoveries in high-dimensional situations: Boosting with stability selection
Authors: Benjamin Hofner, Luigi Boccuto, Markus Göker
Date: 2014-11-05 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

Modern biotechnologies often result in high-dimensional data sets with much
more variables than observations (n $\ll$ p). These data sets pose new
challenges to statistical analysis: Variable selection becomes one of the most
important tasks in this setting. We assess the recently proposed flexible
framework for variable selection called stability selection. By the use of
resampling procedures, stability selection adds a finite sample error control
to high-dimensional variable selection procedures such as Lasso or boosting. We
consider the combination of boosting and stability selection and present
results from a detailed simulation study that provides insights into the
usefulness of this combination. Limitations are discussed and guidance on the
specification and tuning of stability selection is given. The interpretation of
the used error bounds is elaborated and insights for practical data analysis
are given. The results will be used to detect differentially expressed
phenotype measurements in patients with autism spectrum disorders. All methods
are implemented in the freely available R package stabs.
Using Twitter to predict football outcomes
Authors: Stylianos Kampakis, Andreas Adamides
Date: 2014-11-05 00:00:00
Tasks: Game of Football

Twitter has been proven to be a notable source for predictive modelling on
various domains such as the stock market, the dissemination of diseases or
sports outcomes. However, such a study has not been conducted in football
(soccer) so far. The purpose of this research was to study whether data mined
from Twitter can be used for this purpose. We built a set of predictive models
for the outcome of football games of the English Premier League for a 3 month
period based on tweets and we studied whether these models can overcome
predictive models which use only historical data and simple football
statistics. Moreover, combined models are constructed using both Twitter and
historical data. The final results indicate that data mined from Twitter can
indeed be a useful source for predicting games in the Premier League. The final
Twitter-based model performs significantly better than chance when measured by
Cohen's kappa and is comparable to the model that uses simple statistics and
historical data. Combining both models raises the performance higher than it
was achieved by each individual model. Thereby, this study provides evidence
that Twitter derived features can indeed provide useful information for the
prediction of football (soccer) outcomes.
Variational Depth from Focus Reconstruction
Authors: Michael Moeller, Martin Benning, Carola Schönlieb, Daniel Cremers
Date: 2014-08-01 00:00:00

This paper deals with the problem of reconstructing a depth map from a
sequence of differently focused images, also known as depth from focus or shape
from focus. We propose to state the depth from focus problem as a variational
problem including a smooth but nonconvex data fidelity term, and a convex
nonsmooth regularization, which makes the method robust to noise and leads to
more realistic depth maps. Additionally, we propose to solve the nonconvex
minimization problem with a linearized alternating directions method of
multipliers (ADMM), allowing to minimize the energy very efficiently. A
numerical comparison to classical methods on simulated as well as on real data
is presented.
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD
Authors: Miguel Ángel Bautista, Antonio Hernández-Vela, Sergio Escalera, Laura Igual, Oriol Pujol, Josep Moya, Verónica Violant, María Teresa Anguera
Date: 2014-10-16 00:00:00
Tasks: Dynamic Time Warping, Gesture Recognition

We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Authors: Ken Chatfield, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Date: 2014-05-14 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

The latest generation of Convolutional Neural Networks (CNN) have achieved
impressive results in challenging benchmarks on image recognition and object
detection, significantly raising the interest of the community in these
methods. Nevertheless, it is still unclear how different CNN methods compare
with each other and with previous state-of-the-art shallow representations such
as the Bag-of-Visual-Words and the Improved Fisher Vector. This paper conducts
a rigorous evaluation of these new techniques, exploring different deep
architectures and comparing them on a common ground, identifying and disclosing
important implementation details. We identify several useful properties of
CNN-based representations, including the fact that the dimensionality of the
CNN output layer can be reduced significantly without having an adverse effect
on performance. We also identify aspects of deep and shallow methods that can
be successfully shared. In particular, we show that the data augmentation
techniques commonly applied to CNN-based methods can also be applied to shallow
methods, and result in an analogous performance boost. Source code and models
to reproduce the experiments in the paper is made publicly available.
Tensor object classification via multilinear discriminant analysis network
Authors: Rui Zeng, Jiasong Wu, Lotfi Senhadji, Huazhong Shu
Date: 2014-11-05 00:00:00
Tasks: Classification, Decoder, General Classification, Object

This paper proposes a multilinear discriminant analysis network (MLDANet) for
the recognition of multidimensional objects, known as tensor objects. The
MLDANet is a variation of linear discriminant analysis network (LDANet) and
principal component analysis network (PCANet), both of which are the recently
proposed deep learning algorithms. The MLDANet consists of three parts: 1) The
encoder learned by MLDA from tensor data. 2) Features maps ob-tained from
decoder. 3) The use of binary hashing and histogram for feature pooling. A
learning algorithm for MLDANet is described. Evaluations on UCF11 database
indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA + LDA,
and MLDA in terms of classification for tensor objects.