pwc-archive/papers-with-abstracts huggingface dataset:
KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
Authors: Marina Danilevsky, Chi Wang, Nihit Desai, Jingyi Guo, Jiawei Han
Date: 2013-06-03 00:00:00
Tasks: Keyphrase Extraction, Keyphrase Generation

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework
for topical keyphrase generation and ranking. By shifting from the
unigram-centric traditional methods of unsupervised keyphrase extraction to a
phrase-centric approach, we are able to directly compare and rank phrases of
different lengths. We construct a topical keyphrase ranking function which
implements the four criteria that represent high quality topical keyphrases
(coverage, purity, phraseness, and completeness). The effectiveness of our
approach is demonstrated on two collections of content-representative titles in
the domains of Computer Science and Physics.
Convolutional Neural Networks learn compact local image descriptors
Authors: Christian Osendorfer, Justin Bayer, Patrick van der Smagt
Date: 2013-04-30 00:00:00

A standard deep convolutional neural network paired with a suitable loss
function learns compact local image descriptors that perform comparably to
state-of-the art approaches.
Dynamic Covariance Models for Multivariate Financial Time Series
Authors: Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
Date: 2013-05-18 00:00:00
Tasks: Time Series, Time Series Analysis

The accurate prediction of time-changing covariances is an important problem
in the modeling of multivariate financial data. However, some of the most
popular models suffer from a) overfitting problems and multiple local optima,
b) failure to capture shifts in market conditions and c) large computational
costs. To address these problems we introduce a novel dynamic model for
time-changing covariances. Over-fitting and local optima are avoided by
following a Bayesian approach instead of computing point estimates. Changes in
market conditions are captured by assuming a diffusion process in parameter
values, and finally computationally efficient and scalable inference is
performed using particle filters. Experiments with financial data show
excellent performance of the proposed method with respect to current standard
models.
Robust Text Detection in Natural Scene Images
Authors: Xu-Cheng Yin, Xuwang Yin, Kai-Zhu Huang, Hong-Wei Hao
Date: 2013-01-11 00:00:00
Tasks: Clustering, Metric Learning, Scene Text Detection, Text Detection

Text detection in natural scene images is an important prerequisite for many
content-based image analysis tasks. In this paper, we propose an accurate and
robust method for detecting texts in natural scene images. A fast and effective
pruning algorithm is designed to extract Maximally Stable Extremal Regions
(MSERs) as character candidates using the strategy of minimizing regularized
variations. Character candidates are grouped into text candidates by the
ingle-link clustering algorithm, where distance weights and threshold of the
clustering algorithm are learned automatically by a novel self-training
distance metric learning algorithm. The posterior probabilities of text
candidates corresponding to non-text are estimated with an character
classifier; text candidates with high probabilities are then eliminated and
finally texts are identified with a text classifier. The proposed system is
evaluated on the ICDAR 2011 Robust Reading Competition dataset; the f measure
is over 76% and is significantly better than the state-of-the-art performance
of 71%. Experimental results on a publicly available multilingual dataset also
show that our proposed method can outperform the other competitive method with
the f measure increase of over 9 percent. Finally, we have setup an online demo
of our proposed scene text detection system at
http://kems.ustb.edu.cn/learning/yin/dtext.
Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System
Authors: Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan
Date: 2013-06-02 00:00:00
Tasks: Prediction

Mobility-on-demand (MoD) systems have recently emerged as a promising
paradigm of one-way vehicle sharing for sustainable personal urban mobility in
densely populated cities. In this paper, we enhance the capability of a MoD
system by deploying robotic shared vehicles that can autonomously cruise the
streets to be hailed by users. A key challenge to managing the MoD system
effectively is that of real-time, fine-grained mobility demand sensing and
prediction. This paper presents a novel decentralized data fusion and active
sensing algorithm for real-time, fine-grained mobility demand sensing and
prediction with a fleet of autonomous robotic vehicles in a MoD system. Our
Gaussian process (GP)-based decentralized data fusion algorithm can achieve a
fine balance between predictive power and time efficiency. We theoretically
guarantee its predictive performance to be equivalent to that of a
sophisticated centralized sparse approximation for the GP model: The
computation of such a sparse approximate GP model can thus be distributed among
the MoD vehicles, hence achieving efficient and scalable demand prediction.
Though our decentralized active sensing strategy is devised to gather the most
informative demand data for demand prediction, it can achieve a dual effect of
fleet rebalancing to service the mobility demands. Empirical evaluation on
real-world mobility demand data shows that our proposed algorithm can achieve a
better balance between predictive accuracy and time efficiency than
state-of-the-art algorithms.
Declarative Modeling and Bayesian Inference of Dark Matter Halos
Authors: Gabriel Kronberger
Date: 2013-06-02 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming

Probabilistic programming allows specification of probabilistic models in a
declarative manner. Recently, several new software systems and languages for
probabilistic programming have been developed on the basis of newly developed
and improved methods for approximate inference in probabilistic models. In this
contribution a probabilistic model for an idealized dark matter localization
problem is described. We first derive the probabilistic model for the inference
of dark matter locations and masses, and then show how this model can be
implemented using BUGS and Infer.NET, two software systems for probabilistic
programming. Finally, the different capabilities of both systems are discussed.
The presented dark matter model includes mainly non-conjugate factors, thus, it
is difficult to implement this model with Infer.NET.
Dynamic Ad Allocation: Bandits with Budgets
Authors: Aleksandrs Slivkins
Date: 2013-06-01 00:00:00
Tasks: Multi-Armed Bandits

We consider an application of multi-armed bandits to internet advertising
(specifically, to dynamic ad allocation in the pay-per-click model, with
uncertainty on the click probabilities). We focus on an important practical
issue that advertisers are constrained in how much money they can spend on
their ad campaigns. This issue has not been considered in the prior work on
bandit-based approaches for ad allocation, to the best of our knowledge.
  We define a simple, stylized model where an algorithm picks one ad to display
in each round, and each ad has a \emph{budget}: the maximal amount of money
that can be spent on this ad. This model admits a natural variant of UCB1, a
well-known algorithm for multi-armed bandits with stochastic rewards. We derive
strong provable guarantees for this algorithm.
An Analysis of the Connections Between Layers of Deep Neural Networks
Authors: Eugenio Culurciello, Jonghoon Jin, Aysegul Dundar, Jordan Bates
Date: 2013-06-01 00:00:00

We present an analysis of different techniques for selecting the connection
be- tween layers of deep neural networks. Traditional deep neural networks use
ran- dom connection tables between layers to keep the number of connections
small and tune to different image features. This kind of connection performs
adequately in supervised deep networks because their values are refined during
the training. On the other hand, in unsupervised learning, one cannot rely on
back-propagation techniques to learn the connections between layers. In this
work, we tested four different techniques for connecting the first layer of the
network to the second layer on the CIFAR and SVHN datasets and showed that the
accuracy can be im- proved up to 3% depending on the technique used. We also
showed that learning the connections based on the co-occurrences of the
features does not confer an advantage over a random connection table in small
networks. This work is helpful to improve the efficiency of connections between
the layers of unsupervised deep neural networks.
Image Inpainting by Kriging Interpolation Technique
Authors: Firas A. Jassim
Date: 2013-06-01 00:00:00
Tasks: Image Inpainting

Image inpainting is the art of predicting damaged regions of an image. The
manual way of image inpainting is a time consuming. Therefore, there must be an
automatic digital method for image inpainting that recovers the image from the
damaged regions. In this paper, a novel statistical image inpainting algorithm
based on Kriging interpolation technique was proposed. Kriging technique
automatically fills the damaged region in an image using the information
available from its surrounding regions in such away that it uses the spatial
correlation structure of points inside the k-by-k block. Kriging has the
ability to face the challenge of keeping the structure and texture information
as the size of damaged region heighten. Experimental results showed that,
Kriging has a high PSNR value when recovering a variety of test images from
scratches and text as damaged regions.
Towards Detection of Bottlenecks in Modular Systems
Authors: Mark Sh. Levin
Date: 2013-06-01 00:00:00
Tasks: Management

The paper describes some basic approaches to detection of bottlenecks in
composite (modular) systems. The following basic system bottlenecks detection
problems are examined: (1) traditional quality management approaches (Pareto
chart based method, multicriteria analysis as selection of Pareto-efficient
points, and/or multicriteria ranking), (2) selection of critical system
elements (critical components/modules, critical component interconnection), (3)
selection of interconnected system components as composite system faults (via
clique-based fusion), (4) critical elements (e.g., nodes) in networks, and (5)
predictive detection of system bottlenecks (detection of system components
based on forecasting of their parameters). Here, heuristic solving schemes are
used. Numerical examples illustrate the approaches.
Understanding ACT-R - an Outsider's Perspective
Authors: Jacob Whitehill
Date: 2013-06-01 00:00:00
Tasks: Articles

The ACT-R theory of cognition developed by John Anderson and colleagues
endeavors to explain how humans recall chunks of information and how they solve
problems. ACT-R also serves as a theoretical basis for "cognitive tutors",
i.e., automatic tutoring systems that help students learn mathematics, computer
programming, and other subjects. The official ACT-R definition is distributed
across a large body of literature spanning many articles and monographs, and
hence it is difficult for an "outsider" to learn the most important aspects of
the theory. This paper aims to provide a tutorial to the core components of the
ACT-R theory.
One-Class Support Measure Machines for Group Anomaly Detection
Authors: Krikamol Muandet, Bernhard Schölkopf
Date: 2013-03-01 00:00:00
Tasks: Anomaly Detection, Group Anomaly Detection

We propose one-class support measure machines (OCSMMs) for group anomaly
detection which aims at recognizing anomalous aggregate behaviors of data
points. The OCSMMs generalize well-known one-class support vector machines
(OCSVMs) to a space of probability measures. By formulating the problem as
quantile estimation on distributions, we can establish an interesting
connection to the OCSVMs and variable kernel density estimators (VKDEs) over
the input space on which the distributions are defined, bridging the gap
between large-margin methods and kernel density estimators. In particular, we
show that various types of VKDEs can be considered as solutions to a class of
regularization problems studied in this paper. Experiments on Sloan Digital Sky
Survey dataset and High Energy Particle Physics dataset demonstrate the
benefits of the proposed framework in real-world applications.
Universal Induction with Varying Sets of Combinators
Authors: Alexey Potapov, Sergey Rodionov
Date: 2013-06-01 00:00:00

Universal induction is a crucial issue in AGI. Its practical applicability
can be achieved by the choice of the reference machine or representation of
algorithms agreed with the environment. This machine should be updatable for
solving subsequent tasks more efficiently. We study this problem on an example
of combinatory logic as the very simple Turing-complete reference machine,
which enables modifying program representations by introducing different sets
of primitive combinators. Genetic programming system is used to search for
combinator expressions, which are easily decomposed into sub-expressions being
recombined in crossover. Our experiments show that low-complexity induction or
prediction tasks can be solved by the developed system (much more efficiently
than using brute force); useful combinators can be revealed and included into
the representation simplifying more difficult tasks. However, optimal sets of
combinators depend on the specific task, so the reference machine should be
adaptively chosen in coordination with the search engine.
Harmony search algorithm for the container storage problem
Authors: I. Ayachi, R. Kammarti, M. Ksouri, P. Borne, Lagis Ecole Centrale de Lille, Lacs Ecole Nationale des Ingenieurs de Tunis
Date: 2013-06-01 00:00:00
Tasks: valid

Recently a new metaheuristic called harmony search was developed. It mimics
the behaviors of musicians improvising to find the better state harmony. In
this paper, this algorithm is described and applied to solve the container
storage problem in the harbor. The objective of this problem is to determine a
valid containers arrangement, which meets customers delivery deadlines, reduces
the number of container rehandlings and minimizes the ship idle time. In this
paper, an adaptation of the harmony search algorithm to the container storage
problem is detailed and some experimental results are presented and discussed.
The proposed approach was compared to a genetic algorithm previously applied to
the same problem and recorded a good results.
Online Learning with Switching Costs and Other Adaptive Adversaries
Authors: Nicolo Cesa-Bianchi, Ofer Dekel, Ohad Shamir
Date: 2013-02-18 00:00:00
Proceeding: NeurIPS 2013 12

We study the power of different types of adaptive (nonoblivious) adversaries
in the setting of prediction with expert advice, under both full-information
and bandit feedback. We measure the player's performance using a new notion of
regret, also known as policy regret, which better captures the adversary's
adaptiveness to the player's behavior. In a setting where losses are allowed to
drift, we characterize ---in a nearly complete manner--- the power of adaptive
adversaries with bounded memories and switching costs. In particular, we show
that with switching costs, the attainable rate with bandit feedback is
$\widetilde{\Theta}(T^{2/3})$. Interestingly, this rate is significantly worse
than the $\Theta(\sqrt{T})$ rate attainable with switching costs in the
full-information case. Via a novel reduction from experts to bandits, we also
show that a bounded memory adversary can force $\widetilde{\Theta}(T^{2/3})$
regret even in the full information case, proving that switching costs are
easier to control than bounded memory adversaries. Our lower bounds rely on a
new stochastic adversary strategy that generates loss processes with strong
dependencies.
Expectation-maximization for logistic regression
Authors: James G. Scott, Liang Sun
Date: 2013-05-31 00:00:00
Tasks: regression

We present a family of expectation-maximization (EM) algorithms for binary
and negative-binomial logistic regression, drawing a sharp connection with the
variational-Bayes algorithm of Jaakkola and Jordan (2000). Indeed, our results
allow a version of this variational-Bayes approach to be re-interpreted as a
true EM algorithm. We study several interesting features of the algorithm, and
of this previously unrecognized connection with variational Bayes. We also
generalize the approach to sparsity-promoting priors, and to an online method
whose convergence properties are easily established. This latter method
compares favorably with stochastic-gradient descent in situations with marked
collinearity.
Graph-based Generalization Bounds for Learning Binary Relations
Authors: Ben London, Bert Huang, Lise Getoor
Date: 2013-02-21 00:00:00
Tasks: Entity Resolution, Generalization Bounds, Link Prediction

We investigate the generalizability of learned binary relations: functions
that map pairs of instances to a logical indicator. This problem has
application in numerous areas of machine learning, such as ranking, entity
resolution and link prediction. Our learning framework incorporates an example
labeler that, given a sequence $X$ of $n$ instances and a desired training size
$m$, subsamples $m$ pairs from $X \times X$ without replacement. The challenge
in analyzing this learning scenario is that pairwise combinations of random
variables are inherently dependent, which prevents us from using traditional
learning-theoretic arguments. We present a unified, graph-based analysis, which
allows us to analyze this dependence using well-known graph identities. We are
then able to bound the generalization error of learned binary relations using
Rademacher complexity and algorithmic stability. The rate of uniform
convergence is partially determined by the labeler's subsampling process. We
thus examine how various assumptions about subsampling affect generalization;
under a natural random subsampling process, our bounds guarantee
$\tilde{O}(1/\sqrt{n})$ uniform convergence.
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss
Authors: Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
Date: 2013-03-07 00:00:00
Tasks: Relational Reasoning, Tensor Decomposition

We propose a modular framework for multi-relational learning via tensor
decomposition. In our learning setting, the training data contains multiple
types of relationships among a set of objects, which we represent by a sparse
three-mode tensor. The goal is to predict the values of the missing entries. To
do so, we model each relationship as a function of a linear combination of
latent factors. We learn this latent representation by computing a low-rank
tensor decomposition, using quasi-Newton optimization of a weighted objective
function. Sparsity in the observed data is captured by the weighted objective,
leading to improved accuracy when training data is limited. Exploiting sparsity
also improves efficiency, potentially up to an order of magnitude over
unweighted approaches. In addition, our framework accommodates arbitrary
combinations of smooth, task-specific loss functions, making it better suited
for learning different types of relations. For the typical cases of real-valued
functions and binary relations, we propose several loss functions and derive
the associated parameter gradients. We evaluate our method on synthetic and
real data, showing significant improvements in both accuracy and scalability
over related factorization techniques.
Adaptive low rank and sparse decomposition of video using compressive sensing
Authors: Fei Yang, Hong Jiang, Zuowei Shen, Wei Deng, Dimitris Metaxas
Date: 2013-02-06 00:00:00
Tasks: Compressive Sensing, Video Reconstruction

We address the problem of reconstructing and analyzing surveillance videos
using compressive sensing. We develop a new method that performs video
reconstruction by low rank and sparse decomposition adaptively. Background
subtraction becomes part of the reconstruction. In our method, a background
model is used in which the background is learned adaptively as the compressive
measurements are processed. The adaptive method has low latency, and is more
robust than previous methods. We will present experimental results to
demonstrate the advantages of the proposed method.
Neural Networks Built from Unreliable Components
Authors: Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav Varshney
Date: 2013-01-26 00:00:00
Tasks: Retrieval

Recent advances in associative memory design through strutured pattern sets
and graph-based inference algorithms have allowed the reliable learning and
retrieval of an exponential number of patterns. Both these and classical
associative memories, however, have assumed internally noiseless computational
nodes. This paper considers the setting when internal computations are also
noisy. Even if all components are noisy, the final error probability in recall
can often be made exceedingly small, as we characterize. There is a threshold
phenomenon. We also show how to optimize inference algorithm parameters when
knowing statistical properties of internal noise.
A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images
Authors: Reshu Agarwal, Pritam Ranjan, Hugh Chipman
Date: 2013-04-15 00:00:00
Tasks: regression

Classification of satellite images is a key component of many remote sensing
applications. One of the most important products of a raw satellite image is
the classified map which labels the image pixels into meaningful classes.
Though several parametric and non-parametric classifiers have been developed
thus far, accurate labeling of the pixels still remains a challenge. In this
paper, we propose a new reliable multiclass-classifier for identifying class
labels of a satellite image in remote sensing applications. The proposed
multiclass-classifier is a generalization of a binary classifier based on the
flexible ensemble of regression trees model called Bayesian Additive Regression
Trees (BART). We used three small areas from the LANDSAT 5 TM image, acquired
on August 15, 2009 (path/row: 08/29, L1T product, UTM map projection) over
Kings County, Nova Scotia, Canada to classify the land-use. Several prediction
accuracy and uncertainty measures have been used to compare the reliability of
the proposed classifier with the state-of-the-art classifiers in remote
sensing.
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Authors: Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar
Date: 2011-05-04 00:00:00
Tasks: regression

We compare the risk of ridge regression to a simple variant of ordinary least
squares, in which one simply projects the data onto a finite dimensional
subspace (as specified by a Principal Component Analysis) and then performs an
ordinary (un-regularized) least squares regression in this subspace. This note
shows that the risk of this ordinary least squares method is within a constant
factor (namely 4) of the risk of ridge regression.