pwc-archive/papers-with-abstracts huggingface dataset:
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
Authors: Tao Chen, Damian Borth, Trevor Darrell, Shih-Fu Chang
Date: 2014-10-30 00:00:00
Tasks: Classification, General Classification, Retrieval

This paper introduces a visual sentiment concept classification method based
on deep convolutional neural networks (CNNs). The visual sentiment concepts are
adjective noun pairs (ANPs) automatically discovered from the tags of web
photos, and can be utilized as effective statistical cues for detecting
emotions depicted in the images. Nearly one million Flickr images tagged with
these ANPs are downloaded to train the classifiers of the concepts. We adopt
the popular model of deep convolutional neural networks which recently shows
great performance improvement on classifying large-scale web-based image
dataset such as ImageNet. Our deep CNNs model is trained based on Caffe, a
newly developed deep learning framework. To deal with the biased training data
which only contains images with strong sentiment and to prevent overfitting, we
initialize the model with the model weights trained from ImageNet. Performance
evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called
DeepSentiBank) is significantly improved in both annotation accuracy and
retrieval performance, compared to its predecessors which mainly use binary SVM
classification models.
Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles
Authors: Dilek Küçük, Yusuf Arslan
Date: 2014-10-30 00:00:00
Tasks: Articles

Domain ontologies are important information sources for knowledge-based
systems. Yet, building domain ontologies from scratch is known to be a very
labor-intensive process. In this study, we present our semi-automatic approach
to building an ontology for the domain of wind energy which is an important
type of renewable energy with a growing share in electricity generation all
over the world. Related Wikipedia articles are first processed in an automated
manner to determine the basic concepts of the domain together with their
properties and next the concepts, properties, and relationships are organized
to arrive at the ultimate ontology. We also provide pointers to other
engineering ontologies which could be utilized together with the proposed wind
energy ontology in addition to its prospective application areas. The current
study is significant as, to the best of our knowledge, it proposes the first
considerably wide-coverage ontology for the wind energy domain and the ontology
is built through a semi-automatic process which makes use of the related Web
resources, thereby reducing the overall cost of the ontology building process.
An Online Algorithm for Learning Selectivity to Mixture Means
Authors: Matthew Lawlor, Steven Zucker
Date: 2014-10-30 00:00:00
Tasks: Tensor Decomposition, Triplet

We develop a biologically-plausible learning rule called Triplet BCM that
provably converges to the class means of general mixture models. This rule
generalizes the classical BCM neural rule, and provides a novel interpretation
of classical BCM as performing a kind of tensor decomposition. It achieves a
substantial generalization over classical BCM by incorporating triplets of
samples from the mixtures, which provides a novel information processing
interpretation to spike-timing-dependent plasticity. We provide complete proofs
of convergence of this learning rule, and an extended discussion of the
connection between BCM and tensor learning.
An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading
Authors: Balint Antal, Andras Hajdu
Date: 2014-10-30 00:00:00
Tasks: Diabetic Retinopathy Grading, General Classification

Reliable microaneurysm detection in digital fundus images is still an open
issue in medical image processing. We propose an ensemble-based framework to
improve microaneurysm detection. Unlike the well-known approach of considering
the output of multiple classifiers, we propose a combination of internal
components of microaneurysm detectors, namely preprocessing methods and
candidate extractors. We have evaluated our approach for microaneurysm
detection in an online competition, where this algorithm is currently ranked as
first and also on two other databases. Since microaneurysm detection is
decisive in diabetic retinopathy grading, we also tested the proposed method
for this task on the publicly available Messidor database, where a promising
AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification
based on the presence or absence of the microaneurysms.
An ensemble-based system for automatic screening of diabetic retinopathy
Authors: Balint Antal, Andras Hajdu
Date: 2014-10-30 00:00:00
Tasks: Specificity, valid

In this paper, an ensemble-based method for the screening of diabetic
retinopathy (DR) is proposed. This approach is based on features extracted from
the output of several retinal image processing algorithms, such as image-level
(quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms,
exudates) and anatomical (macula, optic disc) components. The actual decision
about the presence of the disease is then made by an ensemble of machine
learning classifiers. We have tested our approach on the publicly available
Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and
0.989 AUC are achieved in a disease/no-disease setting. These results are
highly competitive in this field and suggest that retinal image processing is a
valid approach for automatic DR screening.
Causal Inference through a Witness Protection Program
Authors: Ricardo Silva, Robin Evans
Date: 2014-06-02 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Bayesian Inference, Causal Inference

One of the most fundamental problems in causal inference is the estimation of
a causal effect when variables are confounded. This is difficult in an
observational study, because one has no direct evidence that all confounders
have been adjusted for. We introduce a novel approach for estimating causal
effects that exploits observational conditional independencies to suggest
"weak" paths in a unknown causal graph. The widely used faithfulness condition
of Spirtes et al. is relaxed to allow for varying degrees of "path
cancellations" that imply conditional independencies but do not rule out the
existence of confounding causal paths. The outcome is a posterior distribution
over bounds on the average causal effect via a linear programming approach and
Bayesian inference. We claim this approach should be used in regular practice
along with other default tools in observational studies.
A random forest system combination approach for error detection in digital dictionaries
Authors: Michael Bloodgood, Peng Ye, Paul Rodrigues, David Zajic, David Doermann
Date: 2014-10-30 00:00:00
Proceeding: WS 2012 4
Tasks: Language Modeling, Language Modelling, Optical Character Recognition, Optical Character Recognition (OCR)

When digitizing a print bilingual dictionary, whether via optical character
recognition or manual entry, it is inevitable that errors are introduced into
the electronic version that is created. We investigate automating the process
of detecting errors in an XML representation of a digitized print dictionary
using a hybrid approach that combines rule-based, feature-based, and language
model-based methods. We investigate combining methods and show that using
random forests is a promising approach. We find that in isolation, unsupervised
methods rival the performance of supervised methods. Random forests typically
require training data so we investigate how we can apply random forests to
combine individual base methods that are themselves unsupervised without
requiring large amounts of training data. Experiments reveal empirically that a
relatively small amount of data is sufficient and can potentially be further
reduced through specific selection criteria.
Tasks that Require, or can Benefit from, Matching Blank Nodes
Authors: Christina Lantzaki, Yannis Tzitzikas
Date: 2014-10-30 00:00:00

In various domains and cases, we observe the creation and usage of
information elements which are unnamed. Such elements do not have a name, or
may have a name that is not externally referable (usually meaningless and not
persistent over time). This paper discusses why we will never `escape' from the
problem of having to construct mappings between such unnamed elements in
information systems. Since unnamed elements nowadays occur very often in the
framework of the Semantic Web and Linked Data as blank nodes, the paper
describes scenarios that can benefit from methods that compute mappings between
the unnamed elements. For each scenario, the corresponding bnode matching
problem is formally defined. Based on this analysis, we try to reach to more a
general formulation of the problem, which can be useful for guiding the
required technological advances. To this end, the paper finally discusses
methods to realize blank node matching, the implementations that exist, and
identifies open issues and challenges.
What a Nasty day: Exploring Mood-Weather Relationship from Twitter
Authors: Jiwei Li, Xun Wang, Eduard Hovy
Date: 2014-10-30 00:00:00

While it has long been believed in psychology that weather somehow influences
human's mood, the debates have been going on for decades about how they are
correlated. In this paper, we try to study this long-lasting topic by
harnessing a new source of data compared from traditional psychological
researches: Twitter. We analyze 2 years' twitter data collected by twitter API
which amounts to $10\%$ of all postings and try to reveal the correlations
between multiple dimensional structure of human mood with meteorological
effects. Some of our findings confirm existing hypotheses, while others
contradict them. We are hopeful that our approach, along with the new data
source, can shed on the long-going debates on weather-mood correlation.
Novel methods for multilinear data completion and de-noising based on tensor-SVD
Authors: Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, Misha Kilmer
Date: 2014-07-07 00:00:00
Proceeding: CVPR 2014 6

In this paper we propose novel methods for completion (from limited samples)
and de-noising of multilinear (tensor) data and as an application consider 3-D
and 4- D (color) video data completion and de-noising. We exploit the recently
proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the
notion of multilinear rank and a related tensor nuclear norm was proposed in
[11] to characterize informational and structural complexity of multilinear
data. We first show that videos with linear camera motion can be represented
more efficiently using t-SVD compared to the approaches based on vectorizing or
flattening of the tensors. Since efficiency in representation implies
efficiency in recovery, we outline a tensor nuclear norm penalized algorithm
for video completion from missing entries. Application of the proposed
algorithm for video recovery from missing entries is shown to yield a superior
performance over existing methods. We also consider the problem of tensor
robust Principal Component Analysis (PCA) for de-noising 3-D video data from
sparse random corruptions. We show superior performance of our method compared
to the matrix robust PCA adapted to this setting as proposed in [4].
A Drifting-Games Analysis for Online Learning and Applications to Boosting
Authors: Haipeng Luo, Robert E. Schapire
Date: 2014-06-07 00:00:00
Proceeding: NeurIPS 2014 12

We provide a general mechanism to design online learning algorithms based on
a minimax analysis within a drifting-games framework. Different online learning
settings (Hedge, multi-armed bandit problems and online convex optimization)
are studied by converting into various kinds of drifting games. The original
minimax analysis for drifting games is then used and generalized by applying a
series of relaxations, starting from choosing a convex surrogate of the 0-1
loss function. With different choices of surrogates, we not only recover
existing algorithms, but also propose new algorithms that are totally
parameter-free and enjoy other useful properties. Moreover, our drifting-games
framework naturally allows us to study high probability bounds without
resorting to any concentration results, and also a generalized notion of regret
that measures how good the algorithm is compared to all but the top small
fraction of candidates. Finally, we translate our new Hedge algorithm into a
new adaptive boosting algorithm that is computationally faster as shown in
experiments, since it ignores a large number of examples on each round.
Extracting information from S-curves of language change
Authors: Fakhteh Ghanbarnejad, Martin Gerlach, Jose M. Miotto, Eduardo G. Altmann
Date: 2014-06-17 00:00:00

It is well accepted that adoption of innovations are described by S-curves
(slow start, accelerating period, and slow end). In this paper, we analyze how
much information on the dynamics of innovation spreading can be obtained from a
quantitative description of S-curves. We focus on the adoption of linguistic
innovations for which detailed databases of written texts from the last 200
years allow for an unprecedented statistical precision. Combining data analysis
with simulations of simple models (e.g., the Bass dynamics on complex networks)
we identify signatures of endogenous and exogenous factors in the S-curves of
adoption. We propose a measure to quantify the strength of these factors and
three different methods to estimate it from S-curves. We obtain cases in which
the exogenous factors are dominant (in the adoption of German orthographic
reforms and of one irregular verb) and cases in which endogenous factors are
dominant (in the adoption of conventions for romanization of Russian names and
in the regularization of most studied verbs). These results show that the shape
of S-curve is not universal and contains information on the adoption mechanism.
(published at "J. R. Soc. Interface, vol. 11, no. 101, (2014) 1044"; DOI:
http://dx.doi.org/10.1098/rsif.2014.1044)
Learning circuits with few negations
Authors: Eric Blais, Clément L. Canonne, Igor C. Oliveira, Rocco A. Servedio, Li-Yang Tan
Date: 2014-10-30 00:00:00
Tasks: Learning Theory, Negation

Monotone Boolean functions, and the monotone Boolean circuits that compute
them, have been intensively studied in complexity theory. In this paper we
study the structure of Boolean functions in terms of the minimum number of
negations in any circuit computing them, a complexity measure that interpolates
between monotone functions and the class of all functions. We study this
generalization of monotonicity from the vantage point of learning theory,
giving near-matching upper and lower bounds on the uniform-distribution
learnability of circuits in terms of the number of negations they contain. Our
upper bounds are based on a new structural characterization of negation-limited
circuits that extends a classical result of A. A. Markov. Our lower bounds,
which employ Fourier-analytic tools from hardness amplification, give new
results even for circuits with no negations (i.e. monotone functions).
On Estimating $L_2^2$ Divergence
Authors: Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Date: 2014-10-30 00:00:00

We give a comprehensive theoretical characterization of a nonparametric
estimator for the $L_2^2$ divergence between two continuous distributions. We
first bound the rate of convergence of our estimator, showing that it is
$\sqrt{n}$-consistent provided the densities are sufficiently smooth. In this
smooth regime, we then show that our estimator is asymptotically normal,
construct asymptotic confidence intervals, and establish a Berry-Ess\'{e}en
style inequality characterizing the rate of convergence to normality. We also
show that this estimator is minimax optimal.
Estimating the Accuracies of Multiple Classifiers Without Labeled Data
Authors: Ariel Jaffe, Boaz Nadler, Yuval Kluger
Date: 2014-07-29 00:00:00

In various situations one is given only the predictions of multiple
classifiers over a large unlabeled test data. This scenario raises the
following questions: Without any labeled data and without any a-priori
knowledge about the reliability of these different classifiers, is it possible
to consistently and computationally efficiently estimate their accuracies?
Furthermore, also in a completely unsupervised manner, can one construct a more
accurate unsupervised ensemble classifier? In this paper, focusing on the
binary case, we present simple, computationally efficient algorithms to solve
these questions. Furthermore, under standard classifier independence
assumptions, we prove our methods are consistent and study their asymptotic
error. Our approach is spectral, based on the fact that the off-diagonal
entries of the classifiers' covariance matrix and 3-d tensor are rank-one. We
illustrate the competitive performance of our algorithms via extensive
experiments on both artificial and real datasets.
Towards Learning Object Affordance Priors from Technical Texts
Authors: Nicholas H. Kirk
Date: 2014-10-30 00:00:00
Tasks: Common Sense Reasoning, Object

Everyday activities performed by artificial assistants can potentially be
executed naively and dangerously given their lack of common sense knowledge.
This paper presents conceptual work towards obtaining prior knowledge on the
usual modality (passive or active) of any given entity, and their affordance
estimates, by extracting high-confidence ability modality semantic relations (X
can Y relationship) from non-figurative texts, by analyzing co-occurrence of
grammatical instances of subjects and verbs, and verbs and objects. The
discussion includes an outline of the concept, potential and limitations, and
possible feature and learning framework adoption.
Efficient Decision-Making by Volume-Conserving Physical Object
Authors: Song-Ju Kim, Masashi Aono, Etsushi Nameda
Date: 2014-10-30 00:00:00
Tasks: Decision Making, Object

We demonstrate that any physical object, as long as its volume is conserved
when coupled with suitable operations, provides a sophisticated decision-making
capability. We consider the problem of finding, as accurately and quickly as
possible, the most profitable option from a set of options that gives
stochastic rewards. These decisions are made as dictated by a physical object,
which is moved in a manner similar to the fluctuations of a rigid body in a
tug-of-war game. Our analytical calculations validate statistical reasons why
our method exhibits higher efficiency than conventional algorithms.
Robust sketching for multiple square-root LASSO problems
Authors: Vu Pham, Laurent El Ghaoui, Arturo Fernandez
Date: 2014-10-30 00:00:00

Many learning tasks, such as cross-validation, parameter search, or
leave-one-out analysis, involve multiple instances of similar problems, each
instance sharing a large part of learning data with the others. We introduce a
robust framework for solving multiple square-root LASSO problems, based on a
sketch of the learning data that uses low-rank approximations. Our approach
allows a dramatic reduction in computational effort, in effect reducing the
number of observations from $m$ (the number of observations to start with) to
$k$ (the number of singular values retained in the low-rank model), while not
sacrificing---sometimes even improving---the statistical performance.
Theoretical analysis, as well as numerical experiments on both synthetic and
real data, illustrate the efficiency of the method in large scale applications.
Notes on Noise Contrastive Estimation and Negative Sampling
Authors: Chris Dyer
Date: 2014-10-30 00:00:00
Tasks: Binary Classification, parameter estimation

Estimating the parameters of probabilistic models of language such as maxent
models and probabilistic neural models is computationally difficult since it
involves evaluating partition functions by summing over an entire vocabulary,
which may be millions of word types in size. Two closely related
strategies---noise contrastive estimation (Mnih and Teh, 2012; Mnih and
Kavukcuoglu, 2013; Vaswani et al., 2013) and negative sampling (Mikolov et al.,
2012; Goldberg and Levy, 2014)---have emerged as popular solutions to this
computational problem, but some confusion remains as to which is more
appropriate and when. This document explicates their relationships to each
other and to other estimation techniques. The analysis shows that, although
they are superficially similar, NCE is a general parameter estimation technique
that is asymptotically unbiased, while negative sampling is best understood as
a family of binary classification models that are useful for learning word
representations but not as a general-purpose estimator.
Do Artificial Reinforcement-Learning Agents Matter Morally?
Authors: Brian Tomasik
Date: 2014-10-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Artificial reinforcement learning (RL) is a widely used technique in
artificial intelligence that provides a general method for training agents to
perform a wide variety of behaviours. RL as used in computer science has
striking parallels to reward and punishment learning in animal and human
brains. I argue that present-day artificial RL agents have a very small but
nonzero degree of ethical importance. This is particularly plausible for views
according to which sentience comes in degrees based on the abilities and
complexities of minds, but even binary views on consciousness should assign
nonzero probability to RL programs having morally relevant experiences. While
RL programs are not a top ethical priority today, they may become more
significant in the coming decades as RL is increasingly applied to industry,
robotics, video games, and other areas. I encourage scientists, philosophers,
and citizens to begin a conversation about our ethical duties to reduce the
harm that we inflict on powerless, voiceless RL agents.
ConceptVision: A Flexible Scene Classification Framework
Authors: Ahmet Iscen, Eren Golge, Ilker Sarac, Pinar Duygulu
Date: 2014-01-03 00:00:00
Tasks: Classification, General Classification, Scene Classification

We introduce ConceptVision, a method that aims for high accuracy in
categorizing large number of scenes, while keeping the model relatively simpler
and efficient for scalability. The proposed method combines the advantages of
both low-level representations and high-level semantic categories, and
eliminates the distinctions between different levels through the definition of
concepts. The proposed framework encodes the perspectives brought through
different concepts by considering them in concept groups. Different
perspectives are ensembled for the final decision. Extensive experiments are
carried out on benchmark datasets to test the effects of different concepts,
and methods used to ensemble. Comparisons with state-of-the-art studies show
that we can achieve better results with incorporation of concepts in different
levels with different perspectives.
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
Authors: Paul Rodrigues, David Zajic, David Doermann, Michael Bloodgood, Peng Ye
Date: 2014-10-29 00:00:00
Tasks: Language Modeling, Language Modelling

Dictionaries are often developed using tools that save to Extensible Markup
Language (XML)-based standards. These standards often allow high-level
repeating elements to represent lexical entries, and utilize descendants of
these repeating elements to represent the structure within each lexical entry,
in the form of an XML tree. In many cases, dictionaries are published that have
errors and inconsistencies that are expensive to find manually. This paper
discusses a method for dictionary writers to quickly audit structural
regularity across entries in a dictionary by using statistical language
modeling. The approach learns the patterns of XML nodes that could occur within
an XML tree, and then calculates the probability of each XML tree in the
dictionary against these patterns to look for entries that diverge from the
norm.