pwc-archive/papers-with-abstracts huggingface dataset:
Pignistic Probability Transforms for Mixes of Low- and High-Probability Events
Authors: John J. Sudano
Date: 2015-05-27 00:00:00
Tasks: Decision Making, Vocal Bursts Intensity Prediction

In some real world information fusion situations, time critical decisions
must be made with an incomplete information set. Belief function theories
(e.g., Dempster-Shafer theory of evidence, Transferable Belief Model) have been
shown to provide a reasonable methodology for processing or fusing the
quantitative clues or information measurements that form the incomplete
information set. For decision making, the pignistic (from the Latin pignus, a
bet) probability transform has been shown to be a good method of using Beliefs
or basic belief assignments (BBAs) to make decisions. For many systems, one
need only address the most-probable elements in the set. For some critical
systems, one must evaluate the risk of wrong decisions and establish safe
probability thresholds for decision making. This adds a greater complexity to
decision making, since one must address all elements in the set that are above
the risk decision threshold. The problem is greatly simplified if most of the
probabilities fall below this threshold. Finding a probability transform that
properly represents mixes of low- and high-probability events is essential.
This article introduces four new pignistic probability transforms with an
implementation that uses the latest values of Beliefs, Plausibilities, or BBAs
to improve the pignistic probability estimates. Some of them assign smaller
values of probabilities for smaller values of Beliefs or BBAs than the Smets
pignistic transform. They also assign higher probability values for larger
values of Beliefs or BBAs than the Smets pignistic transform. These probability
transforms will assign a value of probability that converges faster to the
values below the risk threshold. A probability information content (PIC)
variable is also introduced that assigns an information content value to any
set of probability. Four operators are defined to help simplify the
derivations.
Qsmodels: ASP Planning in Interactive Gaming Environment
Authors: Luca Padovani, Alessandro Provetti
Date: 2015-05-27 00:00:00

Qsmodels is a novel application of Answer Set Programming to interactive
gaming environment. We describe a software architecture by which the behavior
of a bot acting inside the Quake 3 Arena can be controlled by a planner. The
planner is written as an Answer Set Program and is interpreted by the Smodels
solver.
A low variance consistent test of relative dependency
Authors: Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko
Date: 2014-06-15 00:00:00

We describe a novel non-parametric statistical hypothesis test of relative
dependence between a source variable and two candidate target variables. Such a
test enables us to determine whether one source variable is significantly more
dependent on a first target variable or a second. Dependence is measured via
the Hilbert-Schmidt Independence Criterion (HSIC), resulting in a pair of
empirical dependence measures (source-target 1, source-target 2). We test
whether the first dependence measure is significantly larger than the second.
Modeling the covariance between these HSIC statistics leads to a provably more
powerful test than the construction of independent HSIC statistics by
sub-sampling. The resulting test is consistent and unbiased, and (being based
on U-statistics) has favorable convergence properties. The test can be computed
in quadratic time, matching the computational complexity of standard empirical
HSIC estimators. The effectiveness of the test is demonstrated on several
real-world problems: we identify language groups from a multilingual corpus,
and we prove that tumor location is more dependent on gene expression than
chromosomal imbalances. Source code is available for download at
https://github.com/wbounliphone/reldep.
Understanding Zipf's law of word frequencies through sample-space collapse in sentence formation
Authors: Stefan Thurner, Rudolf Hanel, Bo Liu, Bernat Corominas-Murtra
Date: 2014-07-17 00:00:00
Tasks: Sentence, Text Generation

The formation of sentences is a highly structured and history-dependent
process. The probability of using a specific word in a sentence strongly
depends on the 'history' of word-usage earlier in that sentence. We study a
simple history-dependent model of text generation assuming that the
sample-space of word usage reduces along sentence formation, on average. We
first show that the model explains the approximate Zipf law found in word
frequencies as a direct consequence of sample-space reduction. We then
empirically quantify the amount of sample-space reduction in the sentences of
ten famous English books, by analysis of corresponding word-transition tables
that capture which words can follow any given word in a text. We find a highly
nested structure in these transition tables and show that this `nestedness' is
tightly related to the power law exponents of the observed word frequency
distributions. With the proposed model it is possible to understand that the
nestedness of a text can be the origin of the actual scaling exponent, and that
deviations from the exact Zipf law can be understood by variations of the
degree of nestedness on a book-by-book basis. On a theoretical level we are
able to show that in case of weak nesting, Zipf's law breaks down in a fast
transition. Unlike previous attempts to understand Zipf's law in language the
sample-space reducing model is not based on assumptions of multiplicative,
preferential, or self-organised critical mechanisms behind language formation,
but simply used the empirically quantifiable parameter 'nestedness' to
understand the statistics of word frequencies.
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews
Authors: Grégoire Mesnil, Tomas Mikolov, Marc'Aurelio Ranzato, Yoshua Bengio
Date: 2014-12-17 00:00:00
Tasks: Binary Classification, General Classification, Sentiment Analysis

Sentiment analysis is a common task in natural language processing that aims
to detect polarity of a text document (typically a consumer review). In the
simplest settings, we discriminate only between positive and negative
sentiment, turning the task into a standard binary classification problem. We
compare several ma- chine learning approaches to this problem, and combine them
to achieve the best possible results. We show how to use for this task the
standard generative lan- guage models, which are slightly complementary to the
state of the art techniques. We achieve strong results on a well-known dataset
of IMDB movie reviews. Our results are easily reproducible, as we publish also
the code needed to repeat the experiments. This should simplify further advance
of the state of the art, as other researchers can combine their techniques with
ours with little effort.
New characterizations of minimum spanning trees and of saliency maps based on quasi-flat zones
Authors: Jean Cousty, Laurent Najman, Yukiko Kenmochi, Silvio GuimarÃ£es
Date: 2015-05-27 00:00:00

We study three representations of hierarchies of partitions: dendrograms
(direct representations), saliency maps, and minimum spanning trees. We provide
a new bijection between saliency maps and hierarchies based on quasi-flat zones
as used in image processing and characterize saliency maps and minimum spanning
trees as solutions to constrained minimization problems where the constraint is
quasi-flat zones preservation. In practice, these results form a toolkit for
new hierarchical methods where one can choose the most convenient
representation. They also invite us to process non-image data with
morphological hierarchies.
On using the Microsoft Kinect$^{\rm TM}$ sensors in the analysis of human motion
Authors: M. J. Malinowski, E. Matsinos, S. Roth
Date: 2014-12-04 00:00:00

The present paper aims at providing the theoretical background required for
investigating the use of the Microsoft Kinect$^{\rm TM}$ (`Kinect', for short)
sensors (original and upgraded) in the analysis of human motion. Our
methodology is developed in such a way that its application be easily adaptable
to comparative studies of other systems used in capturing human-motion data.
Our future plans include the application of this methodology to two situations:
first, in a comparative study of the performance of the two Kinect sensors;
second, in pursuing their validation on the basis of comparisons with a
marker-based system (MBS). One important feature in our approach is the
transformation of the MBS output into Kinect-output format, thus enabling the
analysis of the measurements, obtained from different systems, with the same
software application, i.e., the one we use in the analysis of Kinect-captured
data; one example of such a transformation, for one popular marker-placement
scheme (`Plug-in Gait'), is detailed. We propose that the similarity of the
output, obtained from the different systems, be assessed on the basis of the
comparison of a number of waveforms, representing the variation within the gait
cycle of quantities which are commonly used in the modelling of the human
motion. The data acquisition may involve commercially-available treadmills and
a number of velocity settings: for instance, walking-motion data may be
acquired at $5$ km/h, running-motion data at $8$ and $11$ km/h. We recommend
that particular attention be called to systematic effects associated with the
subject's knee and lower leg, as well as to the ability of the Kinect sensors
in reliably capturing the details in the asymmetry of the motion for the left
and right parts of the human body. The previous versions of the study have been
withdrawn due to the use of a non-representative database.
Learning Transferable Features with Deep Adaptation Networks
Authors: Mingsheng Long, Yue Cao, Jian-Min Wang, Michael. I. Jordan
Date: 2015-02-10 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Multi-Source Unsupervised Domain Adaptation

Recent studies reveal that a deep neural network can learn transferable
features which generalize well to novel tasks for domain adaptation. However,
as deep features eventually transition from general to specific along the
network, the feature transferability drops significantly in higher layers with
increasing domain discrepancy. Hence, it is important to formally reduce the
dataset bias and enhance the transferability in task-specific layers. In this
paper, we propose a new Deep Adaptation Network (DAN) architecture, which
generalizes deep convolutional neural network to the domain adaptation
scenario. In DAN, hidden representations of all task-specific layers are
embedded in a reproducing kernel Hilbert space where the mean embeddings of
different domain distributions can be explicitly matched. The domain
discrepancy is further reduced using an optimal multi-kernel selection method
for mean embedding matching. DAN can learn transferable features with
statistical guarantees, and can scale linearly by unbiased estimate of kernel
embedding. Extensive empirical evidence shows that the proposed architecture
yields state-of-the-art image classification error rates on standard domain
adaptation benchmarks.
Inner and Inter Label Propagation: Salient Object Detection in the Wild
Authors: Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price
Date: 2015-05-27 00:00:00
Tasks: Computational Efficiency, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Superpixels

In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.
Unsupervised Cross-Domain Word Representation Learning
Authors: Danushka Bollegala, Takanori Maehara, Ken-ichi Kawarabayashi
Date: 2015-05-27 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Domain Adaptation, Representation Learning, Sentiment Analysis, Sentiment Classification

Meaning of a word varies from one domain to another. Despite this important
domain dependence in word semantics, existing word representation learning
methods are bound to a single domain. Given a pair of
\emph{source}-\emph{target} domains, we propose an unsupervised method for
learning domain-specific word representations that accurately capture the
domain-specific aspects of word semantics. First, we select a subset of
frequent words that occur in both domains as \emph{pivots}. Next, we optimize
an objective function that enforces two constraints: (a) for both source and
target domain documents, pivots that appear in a document must accurately
predict the co-occurring non-pivots, and (b) word representations learnt for
pivots must be similar in the two domains. Moreover, we propose a method to
perform domain adaptation using the learnt word representations. Our proposed
method significantly outperforms competitive baselines including the
state-of-the-art domain-insensitive word representations, and reports best
sentiment classification accuracies for all domain-pairs in a benchmark
dataset.
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning
Authors: Stefan Hadjis, Firas Abuzaid, Ce Zhang, Christopher Ré
Date: 2015-04-16 00:00:00
Tasks: CPU, Deep Learning, GPU

We present Caffe con Troll (CcT), a fully compatible end-to-end version of
the popular framework Caffe with rebuilt internals. We built CcT to examine the
performance characteristics of training and deploying general-purpose
convolutional neural networks across different hardware architectures. We find
that, by employing standard batching optimizations for CPU training, we achieve
a 4.5x throughput improvement over Caffe on popular networks like CaffeNet.
Moreover, with these improvements, the end-to-end training time for CNNs is
directly proportional to the FLOPS delivered by the CPU, which enables us to
efficiently train hybrid CPU-GPU systems for CNNs.
A U.S. Research Roadmap for Human Computation
Authors: Pietro Michelucci, Lea Shanley, Janis Dickinson, Haym Hirsh
Date: 2015-05-26 00:00:00

The Web has made it possible to harness human cognition en masse to achieve
new capabilities. Some of these successes are well known; for example Wikipedia
has become the go-to place for basic information on all things; Duolingo
engages millions of people in real-life translation of text, while
simultaneously teaching them to speak foreign languages; and fold.it has
enabled public-driven scientific discoveries by recasting complex biomedical
challenges into popular online puzzle games. These and other early successes
hint at the tremendous potential for future crowd-powered capabilities for the
benefit of health, education, science, and society. In the process, a new field
called Human Computation has emerged to better understand, replicate, and
improve upon these successes through scientific research. Human Computation
refers to the science that underlies online crowd-powered systems and was the
topic of a recent visioning activity in which a representative cross-section of
researchers, industry practitioners, visionaries, funding agency
representatives, and policy makers came together to understand what makes
crowd-powered systems successful. Teams of experts considered past, present,
and future human computation systems to explore which kinds of crowd-powered
systems have the greatest potential for societal impact and which kinds of
research will best enable the efficient development of new crowd-powered
systems to achieve this impact. This report summarize the products and findings
of those activities as well as the unconventional process and activities
employed by the workshop, which were informed by human computation research.
Belief Flows of Robust Online Learning
Authors: Pedro A. Ortega, Koby Crammer, Daniel D. Lee
Date: 2015-05-26 00:00:00
Tasks: General Classification, regression, Thompson Sampling

This paper introduces a new probabilistic model for online learning which
dynamically incorporates information from stochastic gradients of an arbitrary
loss function. Similar to probabilistic filtering, the model maintains a
Gaussian belief over the optimal weight parameters. Unlike traditional Bayesian
updates, the model incorporates a small number of gradient evaluations at
locations chosen using Thompson sampling, making it computationally tractable.
The belief is then transformed via a linear flow field which optimally updates
the belief distribution using rules derived from information theoretic
principles. Several versions of the algorithm are shown using different
constraints on the flow field and compared with conventional online learning
algorithms. Results are given for several classification tasks including
logistic regression and multilayer neural networks.
Understanding Image Virality
Authors: Arturo Deza, Devi Parikh
Date: 2015-03-08 00:00:00
Proceeding: CVPR 2015 6
Tasks: Attribute, Marketing

Virality of online content on social networking websites is an important but
esoteric phenomenon often studied in fields like marketing, psychology and data
mining. In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score
using Reddit metadata. We train classifiers with state-of-the-art image
features to predict virality of individual images, relative virality in pairs
of images, and the dominant topic of a viral image. We also compare machine
performance to human performance on these tasks. We find that computers perform
poorly with low level features, and high level information is critical for
predicting virality. We encode semantic information through relative
attributes. We identify the 5 key visual attributes that correlate with
virality. We create an attribute-based characterization of images that can
predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes)
-- better than humans at 60.12%. Finally, we study how human prediction of
image virality varies with different `contexts' in which the images are viewed,
such as the influence of neighbouring images, images recently viewed, as well
as the image title or caption. This work is a first step in understanding the
complex but important phenomenon of image virality. Our datasets and
annotations will be made publicly available.
An Overview of the Asymptotic Performance of the Family of the FastICA Algorithms
Authors: Tianwen Wei
Date: 2015-05-26 00:00:00
Tasks: Form

This contribution summarizes the results on the asymptotic performance of
several variants of the FastICA algorithm. A number of new closed-form
expressions are presented.
Some Open Problems in Optimal AdaBoost and Decision Stumps
Authors: Joshua Belanich, Luis E. Ortiz
Date: 2015-05-26 00:00:00
Tasks: Binary Classification

The significance of the study of the theoretical and practical properties of
AdaBoost is unquestionable, given its simplicity, wide practical use, and
effectiveness on real-world datasets. Here we present a few open problems
regarding the behavior of "Optimal AdaBoost," a term coined by Rudin,
Daubechies, and Schapire in 2004 to label the simple version of the standard
AdaBoost algorithm in which the weak learner that AdaBoost uses always outputs
the weak classifier with lowest weighted error among the respective hypothesis
class of weak classifiers implicit in the weak learner. We concentrate on the
standard, "vanilla" version of Optimal AdaBoost for binary classification that
results from using an exponential-loss upper bound on the misclassification
training error. We present two types of open problems. One deals with general
weak hypotheses. The other deals with the particular case of decision stumps,
as often and commonly used in practice. Answers to the open problems can have
immediate significant impact to (1) cementing previously established results on
asymptotic convergence properties of Optimal AdaBoost, for finite datasets,
which in turn can be the start to any convergence-rate analysis; (2)
understanding the weak-hypotheses class of effective decision stumps generated
from data, which we have empirically observed to be significantly smaller than
the typically obtained class, as well as the effect on the weak learner's
running time and previously established improved bounds on the generalization
performance of Optimal AdaBoost classifiers; and (3) shedding some light on the
"self control" that AdaBoost tends to exhibit in practice.
Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices
Authors: Marco Congedo, Bijan Afsari, Alexandre Barachant, Maher Moakher
Date: 2015-05-26 00:00:00
Tasks: Time Series Analysis

We explore the connection between two problems that have arisen independently
in the signal processing and related fields: the estimation of the geometric
mean of a set of symmetric positive definite (SPD) matrices and their
approximate joint diagonalization (AJD). Today there is a considerable interest
in estimating the geometric mean of a SPD matrix set in the manifold of SPD
matrices endowed with the Fisher information metric. The resulting mean has
several important invariance properties and has proven very useful in diverse
engineering applications such as biomedical and image data processing. While
for two SPD matrices the mean has an algebraic closed form solution, for a set
of more than two SPD matrices it can only be estimated by iterative algorithms.
However, none of the existing iterative algorithms feature at the same time
fast convergence, low computational complexity per iteration and guarantee of
convergence. For this reason, recently other definitions of geometric mean
based on symmetric divergence measures, such as the Bhattacharyya divergence,
have been considered. The resulting means, although possibly useful in
practice, do not satisfy all desirable invariance properties. In this paper we
consider geometric means of co-variance matrices estimated on high-dimensional
time-series, assuming that the data is generated according to an instantaneous
mixing model, which is very common in signal processing. We show that in these
circumstances we can approximate the Fisher information geometric mean by
employing an efficient AJD algorithm. Our approximation is in general much
closer to the Fisher information geometric mean as compared to its competitors
and verifies many invariance properties. Furthermore, convergence is
guaranteed, the computational complexity is low and the convergence rate is
quadratic. The accuracy of this new geometric mean approximation is
demonstrated by means of simulations.