pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneous sparse estimation of canonical vectors in the p>>N setting
Authors: Irina Gaynanova, James G. Booth, Martin T. Wells
Date: 2014-03-24 00:00:00
Tasks: Classification Consistency, feature selection, Variable Selection

This article considers the problem of sparse estimation of canonical vectors
in linear discriminant analysis when $p\gg N$. Several methods have been
proposed in the literature that estimate one canonical vector in the two-group
case. However, $G-1$ canonical vectors can be considered if the number of
groups is $G$. In the multi-group context, it is common to estimate canonical
vectors in a sequential fashion. Moreover, separate prior estimation of the
covariance structure is often required. We propose a novel methodology for
direct estimation of canonical vectors. In contrast to existing techniques, the
proposed method estimates all canonical vectors at once, performs variable
selection across all the vectors and comes with theoretical guarantees on the
variable selection and classification consistency. First, we highlight the fact
that in the $N>p$ setting the canonical vectors can be expressed in a closed
form up to an orthogonal transformation. Secondly, we propose an extension of
this form to the $p\gg N$ setting and achieve feature selection by using a
group penalty. The resulting optimization problem is convex and can be solved
using a block-coordinate descent algorithm. The practical performance of the
method is evaluated through simulation studies as well as real data
applications.
On the Relationship between Sum-Product Networks and Bayesian Networks
Authors: Han Zhao, Mazen Melibari, Pascal Poupart
Date: 2015-01-06 00:00:00

In this paper, we establish some theoretical connections between Sum-Product
Networks (SPNs) and Bayesian Networks (BNs). We prove that every SPN can be
converted into a BN in linear time and space in terms of the network size. The
key insight is to use Algebraic Decision Diagrams (ADDs) to compactly represent
the local conditional probability distributions at each node in the resulting
BN by exploiting context-specific independence (CSI). The generated BN has a
simple directed bipartite graphical structure. We show that by applying the
Variable Elimination algorithm (VE) to the generated BN with ADD
representations, we can recover the original SPN where the SPN can be viewed as
a history record or caching of the VE inference process. To help state the
proof clearly, we introduce the notion of {\em normal} SPN and present a
theoretical analysis of the consistency and decomposability properties. We
conclude the paper with some discussion of the implications of the proof and
establish a connection between the depth of an SPN and a lower bound of the
tree-width of its corresponding BN.
Application of S-Transform on Hyper kurtosis based Modified Duo Histogram Equalized DIC images for Pre-cancer Detection
Authors: Sabyasachi Mukhopadhyay, Soham Mandal, Sawon Pratiher, Ritwik Barman, M. Venkatesh, Nirmalya Ghosh, Prasanta K. Panigrahi
Date: 2015-04-30 00:00:00

Our proposed hyper kurtosis based histogram equalized DIC images enhances the
contrast by preserving the brightness. The evolution and development of
precancerous activity among tissues are studied through S-transform (ST). The
significant variations of amplitude spectra can be observed due to increased
medium roughness from normal tissue were observed in time-frequency domain. The
randomness and inhomogeneity of the tissue structures among human normal and
different grades of DIC tissues is recognized by ST based timefrequency
analysis. This study offers a simpler and better way to recognize the
substantial changes among different stages of DIC tissues, which are reflected
by spatial information containing within the inhomogeneity structures of
different types of tissue.
Manipulation is Harder with Incomplete Votes
Authors: Palash Dey, Neeldhara Misra, Y. Narahari
Date: 2015-04-30 00:00:00

The Coalitional Manipulation (CM) problem has been studied extensively in the
literature for many voting rules. The CM problem, however, has been studied
only in the complete information setting, that is, when the manipulators know
the votes of the non-manipulators. A more realistic scenario is an incomplete
information setting where the manipulators do not know the exact votes of the
non- manipulators but may have some partial knowledge of the votes. In this
paper, we study a setting where the manipulators know a partial order for each
voter that is consistent with the vote of that voter. In this setting, we
introduce and study two natural computational problems - (1) Weak Manipulation
(WM) problem where the manipulators wish to vote in a way that makes their
preferred candidate win in at least one extension of the partial votes of the
non-manipulators; (2) Strong Manipulation (SM) problem where the manipulators
wish to vote in a way that makes their preferred candidate win in all possible
extensions of the partial votes of the non-manipulators. We study the
computational complexity of the WM and the SM problems for commonly used voting
rules such as plurality, veto, k-approval, k-veto, maximin, Copeland, and
Bucklin. Our key finding is that, barring a few exceptions, manipulation
becomes a significantly harder problem in the setting of incomplete votes.
Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis
Authors: Alexander Raß, Manuel Schmitt, Rolf Wanka
Date: 2015-04-30 00:00:00

Particle Swarm Optimization (PSO) is a nature-inspired meta-heuristic for
solving continuous optimization problems. In the literature, the potential of
the particles of swarm has been used to show that slightly modified PSO
guarantees convergence to local optima. Here we show that under specific
circumstances the unmodified PSO, even with swarm parameters known (from the
literature) to be good, almost surely does not yield convergence to a local
optimum is provided. This undesirable phenomenon is called stagnation. For this
purpose, the particles' potential in each dimension is analyzed mathematically.
Additionally, some reasonable assumptions on the behavior if the particles'
potential are made. Depending on the objective function and, interestingly, the
number of particles, the potential in some dimensions may decrease much faster
than in other dimensions. Therefore, these dimensions lose relevance, i.e., the
contribution of their entries to the decisions about attractor updates becomes
insignificant and, with positive probability, they never regain relevance. If
Brownian Motion is assumed to be an approximation of the time-dependent drop of
potential, practical, i.e., large values for this probability are calculated.
Finally, on chosen multidimensional polynomials of degree two, experiments are
provided showing that the required circumstances occur quite frequently.
Furthermore, experiments are provided showing that even when the very simple
sphere function is processed the described stagnation phenomenon occurs.
Consequently, unmodified PSO does not converge to any local optimum of the
chosen functions for tested parameter settings.
Hierarchical Subquery Evaluation for Active Learning on a Graph
Authors: Oisin Mac Aodha, Neill D. F. Campbell, Jan Kautz, Gabriel J. Brostow
Date: 2015-04-30 00:00:00
Proceeding: CVPR 2014 6
Tasks: Active Learning, graph construction, Informativeness

To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
  Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.
Lateral Connections in Denoising Autoencoders Support Supervised Learning
Authors: Antti Rasmus, Harri Valpola, Tapani Raiko
Date: 2015-04-30 00:00:00
Tasks: Denoising, General Classification

We show how a deep denoising autoencoder with lateral connections can be used
as an auxiliary unsupervised learning task to support supervised learning. The
proposed model is trained to minimize simultaneously the sum of supervised and
unsupervised cost functions by back-propagation, avoiding the need for
layer-wise pretraining. It improves the state of the art significantly in the
permutation-invariant MNIST classification task.
Texts in, meaning out: neural language models in semantic similarity task for Russian
Authors: Andrey Kutuzov, Igor Andreev
Date: 2015-04-30 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Distributed vector representations for natural language vocabulary get a lot
of attention in contemporary computational linguistics. This paper summarizes
the experience of applying neural network language models to the task of
calculating semantic similarity for Russian. The experiments were performed in
the course of Russian Semantic Similarity Evaluation track, where our models
took from the 2nd to the 5th position, depending on the task.
  We introduce the tools and corpora used, comment on the nature of the shared
task and describe the achieved results. It was found out that Continuous
Skip-gram and Continuous Bag-of-words models, previously successfully applied
to English material, can be used for semantic modeling of Russian as well.
Moreover, we show that texts in Russian National Corpus (RNC) provide an
excellent training material for such models, outperforming other, much larger
corpora. It is especially true for semantic relatedness tasks (although
stacking models trained on larger corpora on top of RNC models improves
performance even more).
  High-quality semantic vectors learned in such a way can be used in a variety
of linguistic tasks and promise an exciting field for further study.
A Multicore Tool for Constraint Solving
Authors: Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro
Date: 2015-02-13 00:00:00

*** To appear in IJCAI 2015 proceedings *** In Constraint Programming (CP), a
portfolio solver uses a variety of different solvers for solving a given
Constraint Satisfaction / Optimization Problem. In this paper we introduce
sunny-cp2: the first parallel CP portfolio solver that enables a dynamic,
cooperative, and simultaneous execution of its solvers in a multicore setting.
It incorporates state-of-the-art solvers, providing also a usable and
configurable framework. Empirical results are very promising. sunny-cp2 can
even outperform the performance of the oracle solver which always selects the
best solver of the portfolio for a given problem.
Proceedings of The 39th Annual Workshop of the Austrian Association for Pattern Recognition (OAGM), 2015
Authors: Sebastian Hegenbart, Roland Kwitt, Andreas Uhl
Date: 2015-04-30 00:00:00

The 39th annual workshop of the Austrian Association for Pattern Recognition
(OAGM/AAPR) provides a platform for presentation and discussion of research
progress as well as research projects within the OAGM/AAPR community.
Detecting and ordering adjectival scalemates
Authors: Emiel van Miltenburg
Date: 2015-04-30 00:00:00

This paper presents a pattern-based method that can be used to infer
adjectival scales, such as <lukewarm, warm, hot>, from a corpus. Specifically,
the proposed method uses lexical patterns to automatically identify and order
pairs of scalemates, followed by a filtering phase in which unrelated pairs are
discarded. For the filtering phase, several different similarity measures are
implemented and compared. The model presented in this paper is evaluated using
the current standard, along with a novel evaluation set, and shown to be at
least as good as the current state-of-the-art.
An Automated Images-to-Graphs Framework for High Resolution Connectomics
Authors: William Gray Roncal, Dean M. Kleissas, Joshua T. Vogelstein, Priya Manavalan, Kunal Lillaney, Michael Pekala, Randal Burns, R. Jacob Vogelstein, Carey E. Priebe, Mark A. Chevillet, Gregory D. Hager
Date: 2014-11-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Reconstructing a map of neuronal connectivity is a critical challenge in
contemporary neuroscience. Recent advances in high-throughput serial section
electron microscopy (EM) have produced massive 3D image volumes of nanoscale
brain tissue for the first time. The resolution of EM allows for individual
neurons and their synaptic connections to be directly observed. Recovering
neuronal networks by manually tracing each neuronal process at this scale is
unmanageable, and therefore researchers are developing automated image
processing modules. Thus far, state-of-the-art algorithms focus only on the
solution to a particular task (e.g., neuron segmentation or synapse
identification).
  In this manuscript we present the first fully automated images-to-graphs
pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue
and produces a brain graph without any human interaction). To evaluate overall
performance and select the best parameters and methods, we also develop a
metric to assess the quality of the output graphs. We evaluate a set of
algorithms and parameters, searching possible operating points to identify the
best available brain graph for our assessment metric. Finally, we deploy a
reference end-to-end version of the pipeline on a large, publicly available
data set. This provides a baseline result and framework for community analysis
and future algorithm development and testing. All code and data derivatives
have been made publicly available toward eventually unlocking new biofidelic
computational primitives and understanding of neuropathologies.
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
Authors: Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko
Date: 2014-12-15 00:00:00
Proceeding: HLT 2015 5
Tasks: Sentence, Text Generation, Video Captioning

Solving the visual symbol grounding problem has long been a goal of
artificial intelligence. The field appears to be advancing closer to this goal
with recent breakthroughs in deep learning for natural language grounding in
static images. In this paper, we propose to translate videos directly to
sentences using a unified deep neural network with both convolutional and
recurrent structure. Described video datasets are scarce, and most existing
methods have been applied to toy domains with a small vocabulary of possible
words. By transferring knowledge from 1.2M+ images with category labels and
100,000+ images with captions, our method is able to create sentence
descriptions of open-domain videos with large vocabularies. We compare our
approach with recent work using language generation metrics, subject, verb, and
object prediction accuracy, and a human evaluation.
Exploiting Local Features from Deep Networks for Image Retrieval
Authors: Joe Yue-Hei Ng, Fan Yang, Larry S. Davis
Date: 2015-04-20 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

Deep convolutional neural networks have been successfully applied to image
classification tasks. When these same networks have been applied to image
retrieval, the assumption has been made that the last layers would give the
best performance, as they do in classification. We show that for instance-level
image retrieval, lower layers often perform better than the last layers in
convolutional neural networks. We present an approach for extracting
convolutional features from different layers of the networks, and adopt VLAD
encoding to encode features into a single vector for each image. We investigate
the effect of different layers and scales of input images on the performance of
convolutional features using the recent deep networks OxfordNet and GoogLeNet.
Experiments demonstrate that intermediate layers or higher layers with finer
scales produce better results for image retrieval, compared to the last layer.
When using compressed 128-D VLAD descriptors, our method obtains
state-of-the-art results and outperforms other VLAD and CNN based approaches on
two out of three test datasets. Our work provides guidance for transferring
deep networks trained on image classification to image retrieval tasks.
Detecting Concept-level Emotion Cause in Microblogging
Authors: Shuangyong Song, Yao Meng
Date: 2015-04-30 00:00:00

In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead
of the mere word-level models, to discover causes of microblogging users'
diversified emotions on specific hot event. A modified topic-supervised biterm
topic model is utilized in CECM to detect emotion topics' in event-related
tweets, and then context-sensitive topical PageRank is utilized to detect
meaningful multiword expressions as emotion causes. Experimental results on a
dataset from Sina Weibo, one of the largest microblogging websites in China,
show CECM can better detect emotion causes than baseline methods.
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
Authors: Lorenzo Livi, Alessandro Giuliani, Antonello Rizzi
Date: 2014-07-28 00:00:00

This paper builds upon the fundamental work of Niwa et al. [34], which
provides the unique possibility to analyze the relative aggregation/folding
propensity of the elements of the entire Escherichia coli (E. coli) proteome in
a cell-free standardized microenvironment. The hardness of the problem comes
from the superposition between the driving forces of intra- and inter-molecule
interactions and it is mirrored by the evidences of shift from folding to
aggregation phenotypes by single-point mutations [10]. Here we apply several
state-of-the-art classification methods coming from the field of structural
pattern recognition, with the aim to compare different representations of the
same proteins gathered from the Niwa et al. data base; such representations
include sequences and labeled (contact) graphs enriched with chemico-physical
attributes. By this comparison, we are able to identify also some interesting
general properties of proteins. Notably, (i) we suggest a threshold around 250
residues discriminating "easily foldable" from "hardly foldable" molecules
consistent with other independent experiments, and (ii) we highlight the
relevance of contact graph spectra for folding behavior discrimination and
characterization of the E. coli solubility data. The soundness of the
experimental results presented in this paper is proved by the statistically
relevant relationships discovered among the chemico-physical description of
proteins and the developed cost matrix of substitution used in the various
discrimination systems.
FIFTH system for general-purpose connectionist computation
Authors: Anthony Di Franco
Date: 2015-04-29 00:00:00

To date, work on formalizing connectionist computation in a way that is at
least Turing-complete has focused on recurrent architectures and developed
equivalences to Turing machines or similar super-Turing models, which are of
more theoretical than practical significance. We instead develop connectionist
computation within the framework of information propagation networks extended
with unbounded recursion, which is related to constraint logic programming and
is more declarative than the semantics typically used in practical programming,
but is still formally known to be Turing-complete. This approach yields
contributions to the theory and practice of both connectionist computation and
programming languages. Connectionist computations are carried out in a way that
lets them communicate with, and be understood and interrogated directly in
terms of the high-level semantics of a general-purpose programming language.
Meanwhile, difficult (unbounded-dimension, NP-hard) search problems in
programming that have previously been left to the programmer to solve in a
heuristic, domain-specific way are solved uniformly a priori in a way that
approximately achieves information-theoretic limits on performance.
A Deep Learning Model for Structured Outputs with High-order Interaction
Authors: Hongyu Guo, Xiaodan Zhu, Martin Renqiang Min
Date: 2015-04-29 00:00:00
Tasks: Classification, General Classification, regression, Vocal Bursts Intensity Prediction

Many real-world applications are associated with structured data, where not
only input but also output has interplay. However, typical classification and
regression models often lack the ability of simultaneously exploring high-order
interaction within input and that within output. In this paper, we present a
deep learning model aiming to generate a powerful nonlinear functional mapping
from structured input to structured output. More specifically, we propose to
integrate high-order hidden units, guided discriminative pretraining, and
high-order auto-encoders for this purpose. We evaluate the model with three
datasets, and obtain state-of-the-art performances among competitive methods.
Our current work focuses on structured output regression, which is a less
explored area, although the model can be extended to handle structured label
classification.
Who Spoke What? A Latent Variable Framework for the Joint Decoding of Multiple Speakers and their Keywords
Authors: Harshavardhan Sundar, Thippur V. Sreenivas
Date: 2015-04-29 00:00:00

In this paper, we present a latent variable (LV) framework to identify all
the speakers and their keywords given a multi-speaker mixture signal. We
introduce two separate LVs to denote active speakers and the keywords uttered.
The dependency of a spoken keyword on the speaker is modeled through a
conditional probability mass function. The distribution of the mixture signal
is expressed in terms of the LV mass functions and speaker-specific-keyword
models. The proposed framework admits stochastic models, representing the
probability density function of the observation vectors given that a particular
speaker uttered a specific keyword, as speaker-specific-keyword models. The LV
mass functions are estimated in a Maximum Likelihood framework using the
Expectation Maximization (EM) algorithm. The active speakers and their keywords
are detected as modes of the joint distribution of the two LVs. In mixture
signals, containing two speakers uttering the keywords simultaneously, the
proposed framework achieves an accuracy of 82% for detecting both the speakers
and their respective keywords, using Student's-t mixture models as
speaker-specific-keyword models.
Improved repeatability measures for evaluating performance of feature detectors
Authors: Shoaib Ehsan, Nadia Kanwal, Adrian F. Clark, Klaus D. McDonald-Maier
Date: 2015-04-29 00:00:00

The most frequently employed measure for performance characterisation of
local feature detectors is repeatability, but it has been observed that this
does not necessarily mirror actual performance. Presented are improved
repeatability formulations which correlate much better with the true
performance of feature detectors. Comparative results for several
state-of-the-art feature detectors are presented using these measures; it is
found that Hessian-based detectors are generally superior at identifying
features when images are subject to various geometric and photometric
transformations.