pwc-archive/papers-with-abstracts huggingface dataset:
La representación de la variación contextual mediante definiciones terminológicas flexibles
Authors: Antonio San Martín
Date: 2016-06-01 00:00:00

In this doctoral thesis, we apply premises of cognitive linguistics to
terminological definitions and present a proposal called the flexible
terminological definition. This consists of a set of definitions of the same
concept made up of a general definition (in this case, one encompassing the
entire environmental domain) along with additional definitions describing the
concept from the perspective of the subdomains in which it is relevant. Since
context is a determining factor in the construction of the meaning of lexical
units (including terms), we assume that terminological definitions can, and
should, reflect the effects of context, even though definitions have
traditionally been treated as the expression of meaning void of any contextual
effect. The main objective of this thesis is to analyze the effects of
contextual variation on specialized environmental concepts with a view to their
representation in terminological definitions. Specifically, we focused on
contextual variation based on thematic restrictions. To accomplish the
objectives of this doctoral thesis, we conducted an empirical study consisting
of the analysis of a set of contextually variable concepts and the creation of
a flexible definition for two of them. As a result of the first part of our
empirical study, we divided our notion of domain-dependent contextual variation
into three different phenomena: modulation, perspectivization and
subconceptualization. These phenomena are additive in that all concepts
experience modulation, some concepts also undergo perspectivization, and
finally, a small number of concepts are additionally subjected to
subconceptualization. In the second part, we applied these notions to
terminological definitions and we presented we presented guidelines on how to
build flexible definitions, from the extraction of knowledge to the actual
writing of the definition.
Neural Network Translation Models for Grammatical Error Correction
Authors: Shamil Chollampatt, Kaveh Taghipour, Hwee Tou Ng
Date: 2016-06-01 00:00:00
Tasks: Grammatical Error Correction, Machine Translation, Sentence, Translation

Phrase-based statistical machine translation (SMT) systems have previously
been used for the task of grammatical error correction (GEC) to achieve
state-of-the-art accuracy. The superiority of SMT systems comes from their
ability to learn text transformations from erroneous to corrected text, without
explicitly modeling error types. However, phrase-based SMT systems suffer from
limitations of discrete word representation, linear mapping, and lack of global
context. In this paper, we address these limitations by using two different yet
complementary neural network models, namely a neural network global lexicon
model and a neural network joint model. These neural networks can generalize
better by using continuous space representation of words and learn non-linear
mappings. Moreover, they can leverage contextual information from the source
sentence more effectively. By adding these two components, we achieve
statistically significant improvement in accuracy for grammatical error
correction over a state-of-the-art GEC system.
Multiview Rectification of Folded Documents
Authors: Shaodi You, Yasuyuki Matsushita, Sudipta Sinha, Yusuke Bou, Katsushi Ikeuchi
Date: 2016-06-01 00:00:00
Tasks: 3D Reconstruction

Digitally unwrapping images of paper sheets is crucial for accurate document
scanning and text recognition. This paper presents a method for automatically
rectifying curved or folded paper sheets from a few images captured from
multiple viewpoints. Prior methods either need expensive 3D scanners or model
deformable surfaces using over-simplified parametric representations. In
contrast, our method uses regular images and is based on general developable
surface models that can represent a wide variety of paper deformations. Our
main contribution is a new robust rectification method based on ridge-aware 3D
reconstruction of a paper sheet and unwrapping the reconstructed surface using
properties of developable surfaces via $\ell_1$ conformal mapping. We present
results on several examples including book pages, folded letters and shopping
receipts.
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso
Authors: Ning Xu, Jian Hong, Timothy C. G. Fisher
Date: 2016-06-01 00:00:00
Tasks: Model Selection

Model selection is difficult to analyse yet theoretically and empirically
important, especially for high-dimensional data analysis. Recently the least
absolute shrinkage and selection operator (Lasso) has been applied in the
statistical and econometric literature. Consis- tency of Lasso has been
established under various conditions, some of which are difficult to verify in
practice. In this paper, we study model selection from the perspective of
generalization ability, under the framework of structural risk minimization
(SRM) and Vapnik-Chervonenkis (VC) theory. The approach emphasizes the balance
between the in-sample and out-of-sample fit, which can be achieved by using
cross-validation to select a penalty on model complexity. We show that an exact
relationship exists between the generalization ability of a model and model
selection consistency. By implementing SRM and the VC inequality, we show that
Lasso is L2-consistent for model selection under assumptions similar to those
imposed on OLS. Furthermore, we derive a probabilistic bound for the distance
between the penalized extremum estimator and the extremum estimator without
penalty, which is dominated by overfitting. We also propose a new measurement
of overfitting, GR2, based on generalization ability, that converges to zero if
model selection is consistent. Using simulations, we demonstrate that the
proposed CV-Lasso algorithm performs well in terms of model selection and
overfitting control.
Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization
Authors: Hiroyuki Hanada, Atsushi Shibagaki, Jun Sakuma, Ichiro Takeuchi
Date: 2016-06-01 00:00:00
Tasks: General Classification, Small Data Image Classification

We study large-scale classification problems in changing environments where a
small part of the dataset is modified, and the effect of the data modification
must be quickly incorporated into the classifier. When the entire dataset is
large, even if the amount of the data modification is fairly small, the
computational cost of re-training the classifier would be prohibitively large.
In this paper, we propose a novel method for efficiently incorporating such a
data modification effect into the classifier without actually re-training it.
The proposed method provides bounds on the unknown optimal classifier with the
cost only proportional to the size of the data modification. We demonstrate
through numerical experiments that the proposed method provides sufficiently
tight bounds with negligible computational costs, especially when a small part
of the dataset is modified in a large-scale classification problem.
A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Properties
Authors: Frank Dylla, Jae Hee Lee, Till Mossakowski, Thomas Schneider, André Van Delden, Jasper Van De Ven, Diedrich Wolter
Date: 2016-06-01 00:00:00
Tasks: General Classification

Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic
knowledge representation, typically over infinite domains. The motivations for
employing QSTR techniques range from exploiting computational properties that
allow efficient reasoning to capture human cognitive concepts in a
computational framework. The notion of a qualitative calculus is one of the
most prominent QSTR formalisms. This article presents the first overview of all
qualitative calculi developed to date and their computational properties,
together with generalized definitions of the fundamental concepts and methods,
which now encompass all existing calculi. Moreover, we provide a classification
of calculi according to their algebraic properties.
A Credit Assignment Compiler for Joint Prediction
Authors: Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross
Date: 2014-06-07 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Prediction

Many machine learning applications involve jointly predicting multiple
mutually dependent output variables. Learning to search is a family of methods
where the complex decision problem is cast into a sequence of decisions via a
search space. Although these methods have shown promise both in theory and in
practice, implementing them has been burdensomely awkward. In this paper, we
show the search space can be defined by an arbitrary imperative program,
turning learning to search into a credit assignment compiler. Altogether with
the algorithmic improvements for the compiler, we radically reduce the
complexity of programming and the running time. We demonstrate the feasibility
of our approach on multiple joint prediction tasks. In all cases, we obtain
accuracies as high as alternative approaches, at drastically reduced execution
and programming time.
Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis
Authors: Md. Ashad Alam, Osamu Komori, Yu-Ping Wang
Date: 2016-06-01 00:00:00

In genome-wide interaction studies, to detect gene-gene interactions, most
methods are divided into two folds: single nucleotide polymorphisms (SNP) based
and gene-based methods. Basically, the methods based on the gene are more
effective than the methods based on a single SNP. Recent years, while the
kernel canonical correlation analysis (Classical kernel CCA) based U statistic
(KCCU) has proposed to detect the nonlinear relationship between genes. To
estimate the variance in KCCU, they have used resampling based methods which
are highly computationally intensive. In addition, classical kernel CCA is not
robust to contaminated data. We, therefore, first discuss robust kernel mean
element, the robust kernel covariance, and cross-covariance operators. Second,
we propose a method based on influence function to estimate the variance of the
KCCU. Third, we propose a nonparametric robust KCCU method based on robust
kernel CCA, which is designed for contaminated data and less sensitive to noise
than classical kernel CCA. Finally, we investigate the proposed methods to
synthesized data and imaging genetic data set. Based on gene ontology and
pathway analysis, the synthesized and genetics analysis demonstrate that the
proposed robust method shows the superior performance of the state-of-the-art
methods.
Hardness of the Pricing Problem for Chains in Barter Exchanges
Authors: Benjamin Plaut, John P. Dickerson, Tuomas Sandholm
Date: 2016-06-01 00:00:00

Kidney exchange is a barter market where patients trade willing but medically
incompatible donors. These trades occur via cycles, where each patient-donor
pair both gives and receives a kidney, and via chains, which begin with an
altruistic donor who does not require a kidney in return. For logistical
reasons, the maximum length of a cycle is typically limited to a small
constant, while chains can be much longer. Given a compatibility graph of
patient-donor pairs, altruists, and feasible potential transplants between
them, finding even a maximum-cardinality set of vertex-disjoint cycles and
chains is NP-hard. There has been much work on developing provably optimal
solvers that are efficient in practice. One of the leading techniques has been
branch and price, where column generation is used to incrementally bring cycles
and chains into the optimization model on an as-needed basis. In particular,
only positive-price columns need to be brought into the model. We prove that
finding a positive-price chain is NP-complete. This shows incorrectness of two
leading branch-and-price solvers that suggested polynomial-time chain pricing
algorithms.
Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis
Authors: Md. Ashad Alam, Yu-Ping Wang
Date: 2016-06-01 00:00:00

Imaging genetic research has essentially focused on discovering unique and
co-association effects, but typically ignoring to identify outliers or atypical
objects in genetic as well as non-genetics variables. Identifying significant
outliers is an essential and challenging issue for imaging genetics and
multiple sources data analysis. Therefore, we need to examine for transcription
errors of identified outliers. First, we address the influence function (IF) of
kernel mean element, kernel covariance operator, kernel cross-covariance
operator, kernel canonical correlation analysis (kernel CCA) and multiple
kernel CCA. Second, we propose an IF of multiple kernel CCA, which can be
applied for more than two datasets. Third, we propose a visualization method to
detect influential observations of multiple sources of data based on the IF of
kernel CCA and multiple kernel CCA. Finally, the proposed methods are capable
of analyzing outliers of subjects usually found in biomedical applications, in
which the number of dimension is large. To examine the outliers, we use the
stem-and-leaf display. Experiments on both synthesized and imaging genetics
data (e.g., SNP, fMRI, and DNA methylation) demonstrate that the proposed
visualization can be applied effectively.
OpenSalicon: An Open Source Implementation of the Salicon Saliency Model
Authors: Christopher Thomas
Date: 2016-06-01 00:00:00

In this technical report, we present our publicly downloadable implementation
of the SALICON saliency model. At the time of this writing, SALICON is one of
the top performing saliency models on the MIT 300 fixation prediction dataset
which evaluates how well an algorithm is able to predict where humans would
look in a given image. Recently, numerous models have achieved state-of-the-art
performance on this benchmark, but none of the top 5 performing models
(including SALICON) are available for download. To address this issue, we have
created a publicly downloadable implementation of the SALICON model. It is our
hope that our model will engender further research in visual attention modeling
by providing a baseline for comparison of other algorithms and a platform for
extending this implementation. The model we provide supports both training and
testing, enabling researchers to quickly fine-tune the model on their own
dataset. We also provide a pre-trained model and code for those users who only
need to generate saliency maps for images without training their own model.
Seeing Behind the Camera: Identifying the Authorship of a Photograph
Authors: Christopher Thomas, Adriana Kovashka
Date: 2015-08-20 00:00:00
Proceeding: CVPR 2016 6

We introduce the novel problem of identifying the photographer behind a
photograph. To explore the feasibility of current computer vision techniques to
address this problem, we created a new dataset of over 180,000 images taken by
41 well-known photographers. Using this dataset, we examined the effectiveness
of a variety of features (low and high-level, including CNN features) at
identifying the photographer. We also trained a new deep convolutional neural
network for this task. Our results show that high-level features greatly
outperform low-level features. We provide qualitative results using these
learned models that give insight into our method's ability to distinguish
between photographers, and allow us to draw interesting conclusions about what
specific photographers shoot. We also demonstrate two applications of our
method.
Infant directed speech is consistent with teaching
Authors: Baxter S. Eaves Jr., Naomi H. Feldman, Thomas L. Griffiths, Patrick Shafto
Date: 2016-06-01 00:00:00

Infant-directed speech (IDS) has distinctive properties that differ from
adult-directed speech (ADS). Why it has these properties -- and whether they
are intended to facilitate language learning -- is matter of contention. We
argue that much of this disagreement stems from lack of a formal, guiding
theory of how phonetic categories should best be taught to infant-like
learners. In the absence of such a theory, researchers have relied on
intuitions about learning to guide the argument. We use a formal theory of
teaching, validated through experiments in other domains, as the basis for a
detailed analysis of whether IDS is well-designed for teaching phonetic
categories. Using the theory, we generate ideal data for teaching phonetic
categories in English. We qualitatively compare the simulated teaching data
with human IDS, finding that the teaching data exhibit many features of IDS,
including some that have been taken as evidence IDS is not for teaching. The
simulated data reveal potential pitfalls for experimentalists exploring the
role of IDS in language learning. Focusing on different formants and phoneme
sets leads to different conclusions, and the benefit of the teaching data to
learners is not apparent until a sufficient number of examples have been
provided. Finally, we investigate transfer of IDS to learning ADS. The teaching
data improves classification of ADS data, but only for the learner they were
generated to teach; not universally across all classes of learner. This
research offers a theoretically-grounded framework that empowers
experimentalists to systematically evaluate whether IDS is for teaching.
Applications of Probabilistic Programming (Master's thesis, 2015)
Authors: Yura N. Perov
Date: 2016-05-31 00:00:00
Tasks: Object Recognition, Probabilistic Programming

This thesis describes work on two applications of probabilistic programming: the learning of probabilistic program code given specifications, in particular program code of one-dimensional samplers; and the facilitation of sequential Monte Carlo inference with help of data-driven proposals. The latter is presented with experimental results on a linear Gaussian model and a non-parametric dependent Dirichlet process mixture of objects model for object recognition and tracking. In Chapter 1 we provide a brief introduction to probabilistic programming. In Chapter 2 we present an approach to automatic discovery of samplers in the form of probabilistic programs. We formulate a Bayesian approach to this problem by specifying a grammar-based prior over probabilistic program code. We use an approximate Bayesian computation method to learn the programs, whose executions generate samples that statistically match observed data or analytical characteristics of distributions of interest. In our experiments we leverage different probabilistic programming systems to perform Markov chain Monte Carlo sampling over the space of programs. Experimental results have demonstrated that, using the proposed methodology, we can learn approximate and even some exact samplers. Finally, we show that our results are competitive with regard to genetic programming methods. In Chapter 3, we describe a way to facilitate sequential Monte Carlo inference in probabilistic programming using data-driven proposals. In particular, we develop a distance-based proposal for the non-parametric dependent Dirichlet process mixture of objects model. We implement this approach in the probabilistic programming system Anglican, and show that for that model data-driven proposals provide significant performance improvements. We also explore the possibility of using neural networks to improve data-driven proposals.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Authors: Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell
Date: 2014-11-17 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Description, Retrieval, Video Recognition

Models based on deep convolutional networks have dominated recent image
interpretation tasks; we investigate whether models which are also recurrent,
or "temporally deep", are effective for tasks involving sequences, visual and
otherwise. We develop a novel recurrent convolutional architecture suitable for
large-scale visual learning which is end-to-end trainable, and demonstrate the
value of these models on benchmark video recognition tasks, image description
and retrieval problems, and video narration challenges. In contrast to current
models which assume a fixed spatio-temporal receptive field or simple temporal
averaging for sequential processing, recurrent convolutional models are "doubly
deep"' in that they can be compositional in spatial and temporal "layers". Such
models may have advantages when target concepts are complex and/or training
data are limited. Learning long-term dependencies is possible when
nonlinearities are incorporated into the network state updates. Long-term RNN
models are appealing in that they directly can map variable-length inputs
(e.g., video frames) to variable length outputs (e.g., natural language text)
and can model complex temporal dynamics; yet they can be optimized with
backpropagation. Our recurrent long-term models are directly connected to
modern visual convnet models and can be jointly trained to simultaneously learn
temporal dynamics and convolutional perceptual representations. Our results
show such models have distinct advantages over state-of-the-art models for
recognition or generation which are separately defined and/or optimized.
Quantifying the probable approximation error of probabilistic inference programs
Authors: Marco F. Cusumano-Towner, Vikash K. Mansinghka
Date: 2016-05-31 00:00:00

This paper introduces a new technique for quantifying the approximation error
of a broad class of probabilistic inference programs, including ones based on
both variational and Monte Carlo approaches. The key idea is to derive a
subjective bound on the symmetrized KL divergence between the distribution
achieved by an approximate inference program and its true target distribution.
The bound's validity (and subjectivity) rests on the accuracy of two auxiliary
probabilistic programs: (i) a "reference" inference program that defines a gold
standard of accuracy and (ii) a "meta-inference" program that answers the
question "what internal random choices did the original approximate inference
program probably make given that it produced a particular result?" The paper
includes empirical results on inference problems drawn from linear regression,
Dirichlet process mixture modeling, HMMs, and Bayesian networks. The
experiments show that the technique is robust to the quality of the reference
inference program and that it can detect implementation bugs that are not
apparent from predictive performance.
Uncertain programming model for multi-item solid transportation problem
Authors: Hasan Dalman
Date: 2016-05-31 00:00:00

In this paper, an uncertain Multi-objective Multi-item Solid Transportation
Problem (MMSTP) based on uncertainty theory is presented. In the model,
transportation costs, supplies, demands and conveyances parameters are taken to
be uncertain parameters. There are restrictions on some items and conveyances
of the model. Therefore, some particular items cannot be transported by some
exceptional conveyances. Using the advantage of uncertainty theory, the MMSTP
is first converted into an equivalent deterministic MMSTP. By applying convex
combination method and minimizing distance function method, the deterministic
MMSTP is reduced into single objective programming problems. Thus, both single
objective programming problems are solved using Maple 18.02 optimization
toolbox. Finally, a numerical example is given to illustrate the performance of
the models.