pwc-archive/papers-with-abstracts huggingface dataset:
Irreflexive and Hierarchical Relations as Translations
Authors: Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
Date: 2013-04-26 00:00:00

We consider the problem of embedding entities and relations of knowledge
bases in low-dimensional vector spaces. Unlike most existing approaches, which
are primarily efficient for modeling equivalence relations, our approach is
designed to explicitly model irreflexive relations, such as hierarchies, by
interpreting them as translations operating on the low-dimensional embeddings
of the entities. Preliminary experiments show that, despite its simplicity and
a smaller number of parameters than previous approaches, our approach achieves
state-of-the-art performance according to standard evaluation protocols on data
from WordNet and Freebase.
Question Answering Against Very-Large Text Collections
Authors: Leon Derczynski, Richard Shaw, Ben Solway, Jun Wang
Date: 2013-04-26 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

Question answering involves developing methods to extract useful information
from large collections of documents. This is done with specialised search
engines such as Answer Finder. The aim of Answer Finder is to provide an answer
to a question rather than a page listing related documents that may contain the
correct answer. So, a question such as "How tall is the Eiffel Tower" would
simply return "325m" or "1,063ft". Our task was to build on the current version
of Answer Finder by improving information retrieval, and also improving the
pre-processing involved in question series analysis.
A Convex Approach for Image Hallucination
Authors: Peter Innerhofer, Thomas Pock
Date: 2013-04-26 00:00:00
Tasks: Hallucination, Image Super-Resolution, Super-Resolution

In this paper we propose a global convex approach for image hallucination.
Altering the idea of classical multi image super resolution (SU) systems to
single image SU, we incorporate aligned images to hallucinate the output. Our
work is based on the paper of Tappen et al. where they use a non-convex model
for image hallucination. In comparison we formulate a convex primal
optimization problem and derive a fast converging primal-dual algorithm with a
global optimal solution. We use a database with face images to incorporate
high-frequency details to the high-resolution output. We show that we can
achieve state-of-the-art results by using a convex approach.
Cutting Recursive Autoencoder Trees
Authors: Christian Scheible, Hinrich Schuetze
Date: 2013-01-13 00:00:00
Tasks: General Classification

Deep Learning models enjoy considerable success in Natural Language
Processing. While deep architectures produce useful representations that lead
to improvements in various tasks, they are often difficult to interpret. This
makes the analysis of learned structures particularly difficult. In this paper,
we rely on empirical tests to see whether a particular structure makes sense.
We present an analysis of the Semi-Supervised Recursive Autoencoder, a
well-known model that produces structural representations of text. We show that
for certain tasks, the structure of the autoencoder can be significantly
reduced without loss of classification accuracy and we evaluate the produced
structures using human judgment.
Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images
Authors: M. Helmberger, M. Urschler, M. Pienn, Z. Balint, A. Olschewski, H. Bischof
Date: 2013-04-26 00:00:00
Tasks: Segmentation

We present a pulmonary vessel segmentation algorithm, which is fast, fully
automatic and robust. It uses a coarse segmentation of the airway tree and a
left and right lung labeled volume to restrict a vessel enhancement filter,
based on an offset medialness function, to the lungs. We show the application
of our algorithm on contrast-enhanced CT images, where we derive a clinical
parameter to detect pulmonary hypertension (PH) in patients. Results on a
dataset of 24 patients show that quantitative indices derived from the
segmentation are applicable to distinguish patients with and without PH.
Further work-in-progress results are shown on the VESSEL12 challenge dataset,
which is composed of non-contrast-enhanced scans, where we range in the
midfield of participating contestants.
Combinaison d'information visuelle, conceptuelle, et contextuelle pour la construction automatique de hierarchies semantiques adaptees a l'annotation d'images
Authors: Hichem Bannour, Céline Hudelot
Date: 2013-04-18 00:00:00
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity

This paper proposes a new methodology to automatically build semantic
hierarchies suitable for image annotation and classification. The building of
the hierarchy is based on a new measure of semantic similarity. The proposed
measure incorporates several sources of information: visual, conceptual and
contextual as we defined in this paper. The aim is to provide a measure that
best represents image semantics. We then propose rules based on this measure,
for the building of the final hierarchy, and which explicitly encode
hierarchical relationships between different concepts. Therefore, the built
hierarchy is used in a semantic hierarchical classification framework for image
annotation. Our experiments and results show that the hierarchy built improves
classification results.
  Ce papier propose une nouvelle methode pour la construction automatique de
hierarchies semantiques adaptees a la classification et a l'annotation
d'images. La construction de la hierarchie est basee sur une nouvelle mesure de
similarite semantique qui integre plusieurs sources d'informations: visuelle,
conceptuelle et contextuelle que nous definissons dans ce papier. L'objectif
est de fournir une mesure qui est plus proche de la semantique des images. Nous
proposons ensuite des regles, basees sur cette mesure, pour la construction de
la hierarchie finale qui encode explicitement les relations hierarchiques entre
les differents concepts. La hierarchie construite est ensuite utilisee dans un
cadre de classification semantique hierarchique d'images en concepts visuels.
Nos experiences et resultats montrent que la hierarchie construite permet
d'ameliorer les resultats de la classification.
Filament and Flare Detection in Hα image sequences
Authors: Gernot Riegler, Thomas Pock, Werner Pötzi, Astrid Veronig
Date: 2013-04-26 00:00:00

Solar storms can have a major impact on the infrastructure of the earth. Some
of the causing events are observable from ground in the H{\alpha} spectral
line. In this paper we propose a new method for the simultaneous detection of
flares and filaments in H{\alpha} image sequences. Therefore we perform several
preprocessing steps to enhance and normalize the images. Based on the intensity
values we segment the image by a variational approach. In a final
postprecessing step we derive essential properties to classify the events and
further demonstrate the performance by comparing our obtained results to the
data annotated by an expert. The information produced by our method can be used
for near real-time alerts and the statistical analysis of existing data by
solar physicists.
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
Authors: J. Tapson, G. Cohen, S. Afshar, K. Stiefel, Y. Buskila, R. Wang, T. J. Hamilton, A. van Schaik
Date: 2013-04-26 00:00:00

The advent of large scale neural computational platforms has highlighted the
lack of algorithms for synthesis of neural structures to perform predefined
cognitive tasks. The Neural Engineering Framework offers one such synthesis,
but it is most effective for a spike rate representation of neural information,
and it requires a large number of neurons to implement simple functions. We
describe a neural network synthesis method that generates synaptic connectivity
for neurons which process time-encoded neural signals, and which makes very
sparse use of neurons. The method allows the user to specify, arbitrarily,
neuronal characteristics such as axonal and dendritic delays, and synaptic
transfer functions, and then solves for the optimal input-output relationship
using computed dendritic weights. The method may be used for batch or online
learning and has an extremely fast optimization process. We demonstrate its use
in generating a network to recognize speech which is sparsely encoded as spike
times.
Digit Recognition in Handwritten Weather Records
Authors: Manuel Keglevic, Robert Sablatnig
Date: 2013-04-25 00:00:00
Tasks: Line Detection

This paper addresses the automatic recognition of handwritten temperature
values in weather records. The localization of table cells is based on line
detection using projection profiles. Further, a stroke-preserving line removal
method which is based on gradient images is proposed. The presented digit
recognition utilizes features which are extracted using a set of filters and a
Support Vector Machine classifier. It was evaluated on the MNIST and the USPS
dataset and our own database with about 17,000 RGB digit images. An accuracy of
99.36% per digit is achieved for the entire system using a set of 84 weather
records.
Introduction to Judea Pearl's Do-Calculus
Authors: Robert R. Tucci
Date: 2013-04-26 00:00:00

This is a purely pedagogical paper with no new results. The goal of the paper
is to give a fairly self-contained introduction to Judea Pearl's do-calculus,
including proofs of his 3 rules.
An Impossibility Result for High Dimensional Supervised Learning
Authors: Mohammad Hossein Rohban, Prakash Ishwar, Birant Orten, William C. Karl, Venkatesh Saligrama
Date: 2013-01-29 00:00:00
Tasks: General Classification, Vocal Bursts Intensity Prediction

We study high-dimensional asymptotic performance limits of binary supervised
classification problems where the class conditional densities are Gaussian with
unknown means and covariances and the number of signal dimensions scales faster
than the number of labeled training samples. We show that the Bayes error,
namely the minimum attainable error probability with complete distributional
knowledge and equally likely classes, can be arbitrarily close to zero and yet
the limiting minimax error probability of every supervised learning algorithm
is no better than a random coin toss. In contrast to related studies where the
classification difficulty (Bayes error) is made to vanish, we hold it constant
when taking high-dimensional limits. In contrast to VC-dimension based minimax
lower bounds that consider the worst case error probability over all
distributions that have a fixed Bayes error, our worst case is over the family
of Gaussian distributions with constant Bayes error. We also show that a
nontrivial asymptotic minimax error probability can only be attained for
parametric subsets of zero measure (in a suitable measure space). These results
expose the fundamental importance of prior knowledge and suggest that unless we
impose strong structural constraints, such as sparsity, on the parametric
space, supervised learning may be ineffective in high dimensional small sample
settings.
Euclidean Upgrade from a Minimal Number of Segments
Authors: Tanja Schilling, Tomas Pajdla
Date: 2013-04-25 00:00:00

In this paper, we propose an algebraic approach to upgrade a projective
reconstruction to a Euclidean one, and aim at computing the rectifying
homography from a minimal number of 9 segments of known length. Constraints are
derived from these segments which yield a set of polynomial equations that we
solve by means of Gr\"obner bases. We explain how a solver for such a system of
equations can be constructed from simplified template data. Moreover, we
present experiments that demonstrate that the given problem can be solved in
this way.
On latent position inference from doubly stochastic messaging activities
Authors: Nam H. Lee, Jordan Yoder, Minh Tang, Carey E. Priebe
Date: 2012-05-26 00:00:00
Tasks: Position

We model messaging activities as a hierarchical doubly stochastic point
process with three main levels, and develop an iterative algorithm for
inferring actors' relative latent positions from a stream of messaging activity
data. Each of the message-exchanging actors is modeled as a process in a latent
space. The actors' latent positions are assumed to be influenced by the
distribution of a much larger population over the latent space. Each actor's
movement in the latent space is modeled as being governed by two parameters
that we call confidence and visibility, in addition to dependence on the
population distribution. The messaging frequency between a pair of actors is
assumed to be inversely proportional to the distance between their latent
positions. Our inference algorithm is based on a projection approach to an
online filtering problem. The algorithm associates each actor with a
probability density-valued process, and each probability density is assumed to
be a mixture of basis functions. For efficient numerical experiments, we
further develop our algorithm for the case where the basis functions are
obtained by translating and scaling a standard Gaussian density.
Unsupervised Feature Learning for low-level Local Image Descriptors
Authors: Christian Osendorfer, Justin Bayer, Sebastian Urban, Patrick van der Smagt
Date: 2013-01-14 00:00:00
Tasks: Binarization, General Classification

Unsupervised feature learning has shown impressive results for a wide range
of input modalities, in particular for object classification tasks in computer
vision. Using a large amount of unlabeled data, unsupervised feature learning
methods are utilized to construct high-level representations that are
discriminative enough for subsequently trained supervised classification
algorithms. However, it has never been \emph{quantitatively} investigated yet
how well unsupervised learning methods can find \emph{low-level
representations} for image patches without any additional supervision. In this
paper we examine the performance of pure unsupervised methods on a low-level
correspondence task, a problem that is central to many Computer Vision
applications. We find that a special type of Restricted Boltzmann Machines
(RBMs) performs comparably to hand-crafted descriptors. Additionally, a simple
binarization scheme produces compact representations that perform better than
several state-of-the-art descriptors.
An implementation of the relational k-means algorithm
Authors: Balázs Szalkai
Date: 2013-04-25 00:00:00
Tasks: Clustering

A C# implementation of a generalized k-means variant called relational
k-means is described here. Relational k-means is a generalization of the
well-known k-means clustering method which works for non-Euclidean scenarios as
well. The input is an arbitrary distance matrix, as opposed to the traditional
k-means method, where the clustered objects need to be identified with vectors.
Inverse Density as an Inverse Problem: The Fredholm Equation Approach
Authors: Qichao Que, Mikhail Belkin
Date: 2013-04-20 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Transfer Learning

In this paper we address the problem of estimating the ratio $\frac{q}{p}$
where $p$ is a density function and $q$ is another density, or, more generally
an arbitrary function. Knowing or approximating this ratio is needed in various
problems of inference and integration, in particular, when one needs to average
a function with respect to one probability distribution, given a sample from
another. It is often referred as {\it importance sampling} in statistical
inference and is also closely related to the problem of {\it covariate shift}
in transfer learning as well as to various MCMC methods. It may also be useful
for separating the underlying geometry of a space, say a manifold, from the
density function defined on it.
  Our approach is based on reformulating the problem of estimating
$\frac{q}{p}$ as an inverse problem in terms of an integral operator
corresponding to a kernel, and thus reducing it to an integral equation, known
as the Fredholm problem of the first kind. This formulation, combined with the
techniques of regularization and kernel methods, leads to a principled
kernel-based framework for constructing algorithms and for analyzing them
theoretically.
  The resulting family of algorithms (FIRE, for Fredholm Inverse Regularized
Estimator) is flexible, simple and easy to implement.
  We provide detailed theoretical analysis including concentration bounds and
convergence rates for the Gaussian kernel in the case of densities defined on
$\R^d$, compact domains in $\R^d$ and smooth $d$-dimensional sub-manifolds of
the Euclidean space.
  We also show experimental results including applications to classification
and semi-supervised learning within the covariate shift framework and
demonstrate some encouraging experimental comparisons. We also show how the
parameters of our algorithms can be chosen in a completely unsupervised manner.
An Improved Approach for Word Ambiguity Removal
Authors: Priti Saktel, Urmila Shrawankar
Date: 2013-04-25 00:00:00
Tasks: Word Sense Disambiguation

Word ambiguity removal is a task of removing ambiguity from a word, i.e.
correct sense of word is identified from ambiguous sentences. This paper
describes a model that uses Part of Speech tagger and three categories for word
sense disambiguation (WSD). Human Computer Interaction is very needful to
improve interactions between users and computers. For this, the Supervised and
Unsupervised methods are combined. The WSD algorithm is used to find the
efficient and accurate sense of a word based on domain information. The
accuracy of this work is evaluated with the aim of finding best suitable domain
of word.
Low-rank optimization for distance matrix completion
Authors: B. Mishra, G. Meyer, R. Sepulchre
Date: 2013-04-24 00:00:00
Tasks: Matrix Completion

This paper addresses the problem of low-rank distance matrix completion. This
problem amounts to recover the missing entries of a distance matrix when the
dimension of the data embedding space is possibly unknown but small compared to
the number of considered data points. The focus is on high-dimensional
problems. We recast the considered problem into an optimization problem over
the set of low-rank positive semidefinite matrices and propose two efficient
algorithms for low-rank distance matrix completion. In addition, we propose a
strategy to determine the dimension of the embedding space. The resulting
algorithms scale to high-dimensional problems and monotonically converge to a
global solution of the problem. Finally, numerical experiments illustrate the
good performance of the proposed algorithms on benchmarks.
Inference and learning in probabilistic logic programs using weighted Boolean formulas
Authors: Daan Fierens, Guy Van Den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt
Date: 2013-04-25 00:00:00
Tasks: parameter estimation

Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. This paper investigates how classical
inference and learning tasks known from the graphical model community can be
tackled for probabilistic logic programs. Several such tasks such as computing
the marginals given evidence and learning from (partial) interpretations have
not really been addressed for probabilistic logic programs before.
  The first contribution of this paper is a suite of efficient algorithms for
various inference tasks. It is based on a conversion of the program and the
queries and evidence to a weighted Boolean formula. This allows us to reduce
the inference tasks to well-studied tasks such as weighted model counting,
which can be solved using state-of-the-art methods known from the graphical
model and knowledge compilation literature. The second contribution is an
algorithm for parameter estimation in the learning from interpretations
setting. The algorithm employs Expectation Maximization, and is built on top of
the developed inference algorithms.
  The proposed approach is experimentally evaluated. The results show that the
inference algorithms improve upon the state-of-the-art in probabilistic logic
programming and that it is indeed possible to learn the parameters of a
probabilistic logic program from interpretations.
k-Modulus Method for Image Transformation
Authors: Firas A. Jassim
Date: 2013-04-24 00:00:00
Tasks: Image Compression

In this paper, we propose a new algorithm to make a novel spatial image
transformation. The proposed approach aims to reduce the bit depth used for
image storage. The basic technique for the proposed transformation is based of
the modulus operator. The goal is to transform the whole image into multiples
of predefined integer. The division of the whole image by that integer will
guarantee that the new image surely less in size from the original image. The
k-Modulus Method could not be used as a stand alone transform for image
compression because of its high compression ratio. It could be used as a scheme
embedded in other image processing fields especially compression. According to
its high PSNR value, it could be amalgamated with other methods to facilitate
the redundancy criterion.
Comparison of several reweighted l1-algorithms for solving cardinality minimization problems
Authors: Mohammad Javad Abdi
Date: 2013-04-24 00:00:00

Reweighted l1-algorithms have attracted a lot of attention in the field of
applied mathematics. A unified framework of such algorithms has been recently
proposed by Zhao and Li. In this paper we construct a few new examples of
reweighted l1-methods. These functions are certain concave approximations of
the l0-norm function. We focus on the numerical comparison between some new and
existing reweighted l1-algorithms. We show how the change of parameters in
reweighted algorithms may affect the performance of the algorithms for finding
the solution of the cardinality minimization problem. In our experiments, the
problem data were generated according to different statistical distributions,
and we test the algorithms on different sparsity level of the solution of the
problem. Our numerical results demonstrate that the reweighted l1-method is one
of the efficient methods for locating the solution of the cardinality
minimization problem.
Fixed-rank matrix factorizations and Riemannian low-rank optimization
Authors: B. Mishra, G. Meyer, S. Bonnabel, R. Sepulchre
Date: 2012-09-03 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

Motivated by the problem of learning a linear regression model whose
parameter is a large fixed-rank non-symmetric matrix, we consider the
optimization of a smooth cost function defined on the set of fixed-rank
matrices. We adopt the geometric framework of optimization on Riemannian
quotient manifolds. We study the underlying geometries of several well-known
fixed-rank matrix factorizations and then exploit the Riemannian quotient
geometry of the search space in the design of a class of gradient descent and
trust-region algorithms. The proposed algorithms generalize our previous
results on fixed-rank symmetric positive semidefinite matrices, apply to a
broad range of applications, scale to high-dimensional problems and confer a
geometric basis to recent contributions on the learning of fixed-rank
non-symmetric matrices. We make connections with existing algorithms in the
context of low-rank matrix completion and discuss relative usefulness of the
proposed framework. Numerical experiments suggest that the proposed algorithms
compete with the state-of-the-art and that manifold optimization offers an
effective and versatile framework for the design of machine learning algorithms
that learn a fixed-rank matrix.