pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of algorithms that detect drug side effects using electronic healthcare databases
Authors: Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack Gibson, Richard Hubbard
Date: 2014-09-02 00:00:00

The electronic healthcare databases are starting to become more readily
available and are thought to have excellent potential for generating adverse
drug reaction signals. The Health Improvement Network (THIN) database is an
electronic healthcare database containing medical information on over 11
million patients that has excellent potential for detecting ADRs. In this paper
we apply four existing electronic healthcare database signal detecting
algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the
THIN database for a selection of drugs from six chosen drug families. This is
the first comparison of ADR signalling algorithms that includes MUTARA and HUNT
and enabled us to set a benchmark for the adverse drug reaction signalling
ability of the THIN database. The drugs were selectively chosen to enable a
comparison with previous work and for variety. It was found that no algorithm
was generally superior and the algorithms' natural thresholds act at variable
stringencies. Furthermore, none of the algorithms perform well at detecting
rare ADRs.
Transferring Landmark Annotations for Cross-Dataset Face Alignment
Authors: Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang
Date: 2014-09-02 00:00:00
Tasks: Face Alignment, Object Recognition

Dataset bias is a well known problem in object recognition domain. This issue, nonetheless, is rarely explored in face alignment research. In this study, we show that dataset plays an integral part of face alignment performance. Specifically, owing to face alignment dataset bias, training on one database and testing on another or unseen domain would lead to poor performance. Creating an unbiased dataset through combining various existing databases, however, is non-trivial as one has to exhaustively re-label the landmarks for standardisation. In this work, we propose a simple and yet effective method to bridge the disparate annotation spaces between databases, making datasets fusion possible. We show extensive results on combining various popular databases (LFW, AFLW, LFPW, HELEN) for improved cross-dataset and unseen data alignment.
On the Equivalence Between Deep NADE and Generative Stochastic Networks
Authors: Li Yao, Sherjil Ozair, Kyunghyun Cho, Yoshua Bengio
Date: 2014-09-02 00:00:00

Neural Autoregressive Distribution Estimators (NADEs) have recently been
shown as successful alternatives for modeling high dimensional multimodal
distributions. One issue associated with NADEs is that they rely on a
particular order of factorization for $P(\mathbf{x})$. This issue has been
recently addressed by a variant of NADE called Orderless NADEs and its deeper
version, Deep Orderless NADE. Orderless NADEs are trained based on a criterion
that stochastically maximizes $P(\mathbf{x})$ with all possible orders of
factorizations. Unfortunately, ancestral sampling from deep NADE is very
expensive, corresponding to running through a neural net separately predicting
each of the visible variables given some others. This work makes a connection
between this criterion and the training criterion for Generative Stochastic
Networks (GSNs). It shows that training NADEs in this way also trains a GSN,
which defines a Markov chain associated with the NADE model. Based on this
connection, we show an alternative way to sample from a trained Orderless NADE
that allows to trade-off computing time and quality of the samples: a 3 to
10-fold speedup (taking into account the waste due to correlations between
consecutive samples of the chain) can be obtained without noticeably reducing
the quality of the samples. This is achieved using a novel sampling procedure
for GSNs called annealed GSN sampling, similar to tempering methods that
combines fast mixing (obtained thanks to steps at high noise levels) with
accurate samples (obtained thanks to steps at low noise levels).
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
Authors: Caglar Gulcehre, Kyunghyun Cho, Razvan Pascanu, Yoshua Bengio
Date: 2013-11-07 00:00:00
Tasks: Object Recognition

In this paper we propose and investigate a novel nonlinear unit, called $L_p$
unit, for deep neural networks. The proposed $L_p$ unit receives signals from
several projections of a subset of units in the layer below and computes a
normalized $L_p$ norm. We notice two interesting interpretations of the $L_p$
unit. First, the proposed unit can be understood as a generalization of a
number of conventional pooling operators such as average, root-mean-square and
max pooling widely used in, for instance, convolutional neural networks (CNN),
HMAX models and neocognitrons. Furthermore, the $L_p$ unit is, to a certain
degree, similar to the recently proposed maxout unit (Goodfellow et al., 2013)
which achieved the state-of-the-art object recognition results on a number of
benchmark datasets. Secondly, we provide a geometrical interpretation of the
activation function based on which we argue that the $L_p$ unit is more
efficient at representing complex, nonlinear separating boundaries. Each $L_p$
unit defines a superelliptic boundary, with its exact shape defined by the
order $p$. We claim that this makes it possible to model arbitrarily shaped,
curved boundaries more efficiently by combining a few $L_p$ units of different
orders. This insight justifies the need for learning different orders for each
unit in the model. We empirically evaluate the proposed $L_p$ units on a number
of datasets and show that multilayer perceptrons (MLP) consisting of the $L_p$
units achieve the state-of-the-art results on a number of benchmark datasets.
Furthermore, we evaluate the proposed $L_p$ unit on the recently proposed deep
recurrent neural networks (RNN).
Multi-task Sparse Structure Learning
Authors: Andre R. Goncalves, Puja Das, Soumyadeep Chatterjee, Vidyashankar Sivakumar, Fernando J. Von Zuben, Arindam Banerjee
Date: 2014-09-01 00:00:00
Tasks: General Classification, Multi-Task Learning, regression

Multi-task learning (MTL) aims to improve generalization performance by
learning multiple related tasks simultaneously. While sometimes the underlying
task relationship structure is known, often the structure needs to be estimated
from data at hand. In this paper, we present a novel family of models for MTL,
applicable to regression and classification problems, capable of learning the
structure of task relationships. In particular, we consider a joint estimation
problem of the task relationship structure and the individual task parameters,
which is solved using alternating minimization. The task relationship structure
learning component builds on recent advances in structure learning of Gaussian
graphical models based on sparse estimators of the precision (inverse
covariance) matrix. We illustrate the effectiveness of the proposed model on a
variety of synthetic and benchmark datasets for regression and classification.
We also consider the problem of combining climate model outputs for better
projections of future climate, with focus on temperature in South America, and
show that the proposed model outperforms several existing methods for the
problem.
Measures of Entropy from Data Using Infinitely Divisible Kernels
Authors: Luis G. Sanchez Giraldo, Murali Rao, Jose C. Principe
Date: 2012-11-11 00:00:00
Tasks: Clustering, Dimensionality Reduction, Two-sample testing

Information theory provides principled ways to analyze different inference
and learning problems such as hypothesis testing, clustering, dimensionality
reduction, classification, among others. However, the use of information
theoretic quantities as test statistics, that is, as quantities obtained from
empirical data, poses a challenging estimation problem that often leads to
strong simplifications such as Gaussian models, or the use of plug in density
estimators that are restricted to certain representation of the data. In this
paper, a framework to non-parametrically obtain measures of entropy directly
from data using operators in reproducing kernel Hilbert spaces defined by
infinitely divisible kernels is presented. The entropy functionals, which bear
resemblance with quantum entropies, are defined on positive definite matrices
and satisfy similar axioms to those of Renyi's definition of entropy.
Convergence of the proposed estimators follows from concentration results on
the difference between the ordered spectrum of the Gram matrices and the
integral operators associated to the population quantities. In this way,
capitalizing on both the axiomatic definition of entropy and on the
representation power of positive definite kernels, the proposed measure of
entropy avoids the estimation of the probability distribution underlying the
data. Moreover, estimators of kernel-based conditional entropy and mutual
information are also defined. Numerical experiments on independence tests
compare favourably with state of the art.
Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model
Authors: Alexander Woodward, Tom Froese, Takashi Ikegami
Date: 2014-09-01 00:00:00

The state space of a conventional Hopfield network typically exhibits many
different attractors of which only a small subset satisfy constraints between
neurons in a globally optimal fashion. It has recently been demonstrated that
combining Hebbian learning with occasional alterations of normal neural states
avoids this problem by means of self-organized enlargement of the best basins
of attraction. However, so far it is not clear to what extent this process of
self-optimization is also operative in real brains. Here we demonstrate that it
can be transferred to more biologically plausible neural networks by
implementing a self-optimizing spiking neural network model. In addition, by
using this spiking neural network to emulate a Hopfield network with Hebbian
learning, we attempt to make a connection between rate-based and temporal
coding based neural systems. Although further work is required to make this
model more realistic, it already suggests that the efficacy of the
self-optimizing process is independent from the simplifying assumptions of a
conventional Hopfield network. We also discuss natural and cultural processes
that could be responsible for occasional alteration of neural firing patterns
in actual brains
Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks
Authors: Kyunghyun Cho, Xi Chen
Date: 2013-06-17 00:00:00
Tasks: Gesture Recognition

The gesture recognition using motion capture data and depth sensors has
recently drawn more attention in vision recognition. Currently most systems
only classify dataset with a couple of dozens different actions. Moreover,
feature extraction from the data is often computational complex. In this paper,
we propose a novel system to recognize the actions from skeleton data with
simple, but effective, features using deep neural networks. Features are
extracted for each frame based on the relative positions of joints (PO),
temporal differences (TD), and normalized trajectories of motion (NT). Given
these features a hybrid multi-layer perceptron is trained, which simultaneously
classifies and reconstructs input data. We use deep autoencoder to visualize
learnt features, and the experiments show that deep neural networks can capture
more discriminative information than, for instance, principal component
analysis can. We test our system on a public database with 65 classes and more
than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our
knowledge, the state of the art result for such a large dataset.
Multi-tensor Completion for Estimating Missing Values in Video Data
Authors: Chao Li, Lili Guo, Andrzej Cichocki
Date: 2014-09-01 00:00:00
Tasks: Missing Values

Many tensor-based data completion methods aim to solve image and video
in-painting problems. But, all methods were only developed for a single
dataset. In most of real applications, we can usually obtain more than one
dataset to reflect one phenomenon, and all the datasets are mutually related in
some sense. Thus one question raised whether such the relationship can improve
the performance of data completion or not? In the paper, we proposed a novel
and efficient method by exploiting the relationship among datasets for
multi-video data completion. Numerical results show that the proposed method
significantly improve the performance of video in-painting, particularly in the
case of very high missing percentage.
Storing sequences in binary tournament-based neural networks
Authors: Xiaoran Jiang, Vincent Gripon, Claude Berrou, Michael Rabbat
Date: 2014-09-01 00:00:00
Tasks: Retrieval

An extension to a recently introduced architecture of clique-based neural
networks is presented. This extension makes it possible to store sequences with
high efficiency. To obtain this property, network connections are provided with
orientation and with flexible redundancy carried by both spatial and temporal
redundancy, a mechanism of anticipation being introduced in the model. In
addition to the sequence storage with high efficiency, this new scheme also
offers biological plausibility. In order to achieve accurate sequence
retrieval, a double layered structure combining hetero-association and
auto-association is also proposed.
Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams
Authors: Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng, Yingke Chen
Date: 2014-09-01 00:00:00
Tasks: Decision Making

Planning for ad hoc teamwork is challenging because it involves agents
collaborating without any prior coordination or communication. The focus is on
principled methods for a single agent to cooperate with others. This motivates
investigating the ad hoc teamwork problem in the context of individual decision
making frameworks. However, individual decision making in multiagent settings
faces the task of having to reason about other agents' actions, which in turn
involves reasoning about others. An established approximation that
operationalizes this approach is to bound the infinite nesting from below by
introducing level 0 models. We show that a consequence of the finitely-nested
modeling is that we may not obtain optimal team solutions in cooperative
settings. We address this limitation by including models at level 0 whose
solutions involve learning. We demonstrate that the learning integrated into
planning in the context of interactive dynamic influence diagrams facilitates
optimal team behavior, and is applicable to ad hoc teamwork.
A Geometric Approach For Fully Automatic Chromosome Segmentation
Authors: Shervin Minaee, Mehran Fotouhi, Babak Hossein Khalaj
Date: 2011-12-18 00:00:00
Tasks: Segmentation

A fundamental task in human chromosome analysis is chromosome segmentation.
Segmentation plays an important role in chromosome karyotyping. The first step
in segmentation is to remove intrusive objects such as stain debris and other
noises. The next step is detection of touching and overlapping chromosomes, and
the final step is separation of such chromosomes. Common methods for separation
between touching chromosomes are interactive and require human intervention for
correct separation between touching and overlapping chromosomes. In this paper,
a geometric-based method is used for automatic detection of touching and
overlapping chromosomes and separating them. The proposed scheme performs
segmentation in two phases. In the first phase, chromosome clusters are
detected using three geometric criteria, and in the second phase, chromosome
clusters are separated using a cut-line. Most of earlier methods did not work
properly in case of chromosome clusters that contained more than two
chromosomes. Our method, on the other hand, is quite efficient in separation of
such chromosome clusters. At each step, one separation will be performed and
this algorithm is repeated until all individual chromosomes are separated.
Another important point about the proposed method is that it uses the geometric
features of chromosomes which are independent of the type of images and it can
easily be applied to any type of images such as binary images and does not
require multispectral images as well. We have applied our method to a database
containing 62 touching and partially overlapping chromosomes and a success rate
of 91.9% is achieved.
Towards a Calculus of Echo State Networks
Authors: Alireza Goudarzi, Darko Stefanovic
Date: 2014-09-01 00:00:00

Reservoir computing is a recent trend in neural networks which uses the
dynamical perturbations on the phase space of a system to compute a desired
target function. We present how one can formulate an expectation of system
performance in a simple class of reservoir computing called echo state
networks. In contrast with previous theoretical frameworks, which only reveal
an upper bound on the total memory in the system, we analytically calculate the
entire memory curve as a function of the structure of the system and the
properties of the input and the target function. We demonstrate the precision
of our framework by validating its result for a wide range of system sizes and
spectral radii. Our analytical calculation agrees with numerical simulations.
To the best of our knowledge this work presents the first exact analytical
characterization of the memory curve in echo state networks.
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees
Authors: Mohammad J. Taghizadeh, Reza Parhizkar, Philip N. Garner, Herve Bourlard, Afsaneh Asaei
Date: 2014-08-31 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

This paper addresses the problem of ad hoc microphone array calibration where
only partial information about the distances between microphones is available.
We construct a matrix consisting of the pairwise distances and propose to
estimate the missing entries based on a novel Euclidean distance matrix
completion algorithm by alternative low-rank matrix completion and projection
onto the Euclidean distance space. This approach confines the recovered matrix
to the EDM cone at each iteration of the matrix completion algorithm. The
theoretical guarantees of the calibration performance are obtained considering
the random and locally structured missing entries as well as the measurement
noise on the known distances. This study elucidates the links between the
calibration error and the number of microphones along with the noise level and
the ratio of missing distances. Thorough experiments on real data recordings
and simulated setups are conducted to demonstrate these theoretical insights. A
significant improvement is achieved by the proposed Euclidean distance matrix
completion algorithm over the state-of-the-art techniques for ad hoc microphone
array calibration.
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
Authors: Quan Wang
Date: 2012-07-15 00:00:00
Tasks: Dimensionality Reduction, Face Recognition, General Classification

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experimental results to compare the performance of kernel PCA and standard PCA for classification problems. We also implement the kernel PCA-based ASMs, and use it to construct human face models.
A Plug&Play P300 BCI Using Information Geometry
Authors: Alexandre Barachant, Marco Congedo
Date: 2014-08-30 00:00:00
Tasks: ERP, General Classification

This paper presents a new classification methods for Event Related Potentials
(ERP) based on an Information geometry framework. Through a new estimation of
covariance matrices, this work extend the use of Riemannian geometry, which was
previously limited to SMR-based BCI, to the problem of classification of ERPs.
As compared to the state-of-the-art, this new method increases performance,
reduces the number of data needed for the calibration and features good
generalisation across sessions and subjects. This method is illustrated on data
recorded with the P300-based game brain invaders. Finally, an online and
adaptive implementation is described, where the BCI is initialized with generic
parameters derived from a database and continuously adapt to the individual,
allowing the user to play the game without any calibration while keeping a high
accuracy.
Kernel Coding: General Formulation and Special Cases
Authors: Mehrtash Harandi, Mathieu Salzmann
Date: 2014-08-30 00:00:00
Tasks: Dictionary Learning

Representing images by compact codes has proven beneficial for many visual
recognition tasks. Most existing techniques, however, perform this coding step
directly in image feature space, where the distributions of the different
classes are typically entangled. In contrast, here, we study the problem of
performing coding in a high-dimensional Hilbert space, where the classes are
expected to be more easily separable. To this end, we introduce a general
coding formulation that englobes the most popular techniques, such as bag of
words, sparse coding and locality-based coding, and show how this formulation
and its special cases can be kernelized. Importantly, we address several
aspects of learning in our general formulation, such as kernel learning,
dictionary learning and supervised kernel coding. Our experimental evaluation
on several visual recognition tasks demonstrates the benefits of performing
coding in Hilbert space, and in particular of jointly learning the kernel, the
dictionary and the classifier.
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
Authors: Mehrtash Harandi, Richard Hartley, Brian Lovell, Conrad Sanderson
Date: 2014-08-30 00:00:00
Tasks: Action Recognition, Dictionary Learning, Face Recognition, General Classification, Material Classification, Temporal Action Localization

This paper introduces sparse coding and dictionary learning for Symmetric
Positive Definite (SPD) matrices, which are often used in machine learning,
computer vision and related areas. Unlike traditional sparse coding schemes
that work in vector spaces, in this paper we discuss how SPD matrices can be
described by sparse combination of dictionary atoms, where the atoms are also
SPD matrices. We propose to seek sparse coding by embedding the space of SPD
matrices into Hilbert spaces through two types of Bregman matrix divergences.
This not only leads to an efficient way of performing sparse coding, but also
an online and iterative scheme for dictionary learning. We apply the proposed
methods to several computer vision tasks where images are represented by region
covariance matrices. Our proposed algorithms outperform state-of-the-art
methods on a wide range of classification tasks, including face recognition,
action recognition, material classification and texture categorization.