pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Portfolio Theory: A Machine Learning Perspective
Authors: Yves-Laurent Kom Samo, Alexander Vervuurt
Date: 2016-05-09 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Management

In this paper we propose a novel application of Gaussian processes (GPs) to
financial asset allocation. Our approach is deeply rooted in Stochastic
Portfolio Theory (SPT), a stochastic analysis framework introduced by Robert
Fernholz that aims at flexibly analysing the performance of certain investment
strategies in stock markets relative to benchmark indices. In particular, SPT
has exhibited some investment strategies based on company sizes that, under
realistic assumptions, outperform benchmark indices with probability 1 over
certain time horizons. Galvanised by this result, we consider the inverse
problem that consists of learning (from historical data) an optimal investment
strategy based on any given set of trading characteristics, and using a
user-specified optimality criterion that may go beyond outperforming a
benchmark index. Although this inverse problem is of the utmost interest to
investment management practitioners, it can hardly be tackled using the SPT
framework. We show that our machine learning approach learns investment
strategies that considerably outperform existing SPT strategies in the US stock
market.
Learning the kernel matrix via predictive low-rank approximations
Authors: Martin Stražar, Tomaž Curk
Date: 2016-01-17 00:00:00
Tasks: regression

Efficient and accurate low-rank approximations of multiple data sources are
essential in the era of big data. The scaling of kernel-based learning
algorithms to large datasets is limited by the O(n^2) computation and storage
complexity of the full kernel matrix, which is required by most of the recent
kernel learning algorithms.
  We present the Mklaren algorithm to approximate multiple kernel matrices
learn a regression model, which is entirely based on geometrical concepts. The
algorithm does not require access to full kernel matrices yet it accounts for
the correlations between all kernels. It uses Incomplete Cholesky
decomposition, where pivot selection is based on least-angle regression in the
combined, low-dimensional feature space. The algorithm has linear complexity in
the number of data points and kernels. When explicit feature space induced by
the kernel can be constructed, a mapping from the dual to the primal Ridge
regression weights is used for model interpretation.
  The Mklaren algorithm was tested on eight standard regression datasets. It
outperforms contemporary kernel matrix approximation approaches when learning
with multiple kernels. It identifies relevant kernels, achieving highest
explained variance than other multiple kernel learning methods for the same
number of iterations. Test accuracy, equivalent to the one using full kernel
matrices, was achieved with at significantly lower approximation ranks. A
difference in run times of two orders of magnitude was observed when either the
number of samples or kernels exceeds 3000.
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
Authors: Chong You, Chun-Guang Li, Daniel P. Robinson, Rene Vidal
Date: 2016-05-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering, Image Clustering

State-of-the-art subspace clustering methods are based on expressing each
data point as a linear combination of other data points while regularizing the
matrix of coefficients with $\ell_1$, $\ell_2$ or nuclear norms. $\ell_1$
regularization is guaranteed to give a subspace-preserving affinity (i.e.,
there are no connections between points from different subspaces) under broad
theoretical conditions, but the clusters may not be connected. $\ell_2$ and
nuclear norm regularization often improve connectivity, but give a
subspace-preserving affinity only for independent subspaces. Mixed $\ell_1$,
$\ell_2$ and nuclear norm regularizations offer a balance between the
subspace-preserving and connectedness properties, but this comes at the cost of
increased computational complexity. This paper studies the geometry of the
elastic net regularizer (a mixture of the $\ell_1$ and $\ell_2$ norms) and uses
it to derive a provably correct and scalable active set method for finding the
optimal coefficients. Our geometric analysis also provides a theoretical
justification and a geometric interpretation for the balance between the
connectedness (due to $\ell_2$ regularization) and subspace-preserving (due to
$\ell_1$ regularization) properties for elastic net subspace clustering. Our
experiments show that the proposed active set method not only achieves
state-of-the-art clustering performance, but also efficiently handles
large-scale datasets.
On the Emergence of Shortest Paths by Reinforced Random Walks
Authors: Daniel R. Figueiredo, Michele Garetto
Date: 2016-05-09 00:00:00

The co-evolution between network structure and functional performance is a
fundamental and challenging problem whose complexity emerges from the intrinsic
interdependent nature of structure and function. Within this context, we
investigate the interplay between the efficiency of network navigation (i.e.,
path lengths) and network structure (i.e., edge weights). We propose a simple
and tractable model based on iterative biased random walks where edge weights
increase over time as function of the traversed path length. Under mild
assumptions, we prove that biased random walks will eventually only traverse
shortest paths in their journey towards the destination. We further
characterize the transient regime proving that the probability to traverse
non-shortest paths decays according to a power-law. We also highlight various
properties in this dynamic, such as the trade-off between exploration and
convergence, and preservation of initial network plasticity. We believe the
proposed model and results can be of interest to various domains where biased
random walks and decentralized navigation have been applied.
Dynamic Decomposition of Spatiotemporal Neural Signals
Authors: Luca Ambrogioni, Marcel A. J. van Gerven, Eric Maris
Date: 2016-05-09 00:00:00

Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals.
Multitask Learning for Sequence Labeling Tasks
Authors: Arvind Agarwal, Saurabh Kataria
Date: 2014-04-25 00:00:00

In this paper, we present a learning method for sequence labeling tasks in
which each example sequence has multiple label sequences. Our method learns
multiple models, one model for each label sequence. Each model computes the
joint probability of all label sequences given the example sequence. Although
each model considers all label sequences, its primary focus is only one label
sequence, and therefore, each model becomes a task-specific model, for the task
belonging to that primary label. Such multiple models are learned {\it
simultaneously} by facilitating the learning transfer among models through {\it
explicit parameter sharing}. We experiment the proposed method on two
applications and show that our method significantly outperforms the
state-of-the-art method.
GLEU Without Tuning
Authors: Courtney Napoles, Keisuke Sakaguchi, Matt Post, Joel Tetreault
Date: 2016-05-09 00:00:00

The GLEU metric was proposed for evaluating grammatical error corrections
using n-gram overlap with a set of reference sentences, as opposed to
precision/recall of specific annotated errors (Napoles et al., 2015). This
paper describes improvements made to the GLEU metric that address problems that
arise when using an increasing number of reference sets. Unlike the originally
presented metric, the modified metric does not require tuning. We recommend
that this version be used instead of the original version.
A Counterexample to the Forward Recursion in Fuzzy Critical Path Analysis Under Discrete Fuzzy Sets
Authors: Matthew J. Liberatore
Date: 2016-05-09 00:00:00

Fuzzy logic is an alternate approach for quantifying uncertainty relating to
activity duration. The fuzzy version of the backward recursion has been shown
to produce results that incorrectly amplify the level of uncertainty. However,
the fuzzy version of the forward recursion has been widely proposed as an
approach for determining the fuzzy set of critical path lengths. In this paper,
the direct application of the extension principle leads to a proposition that
must be satisfied in fuzzy critical path analysis. Using a counterexample it is
demonstrated that the fuzzy forward recursion when discrete fuzzy sets are used
to represent activity durations produces results that are not consistent with
the theory presented. The problem is shown to be the application of the fuzzy
maximum. Several methods presented in the literature are described and shown to
provide results that are consistent with the extension principle.
Studying the brain from adolescence to adulthood through sparse multi-view matrix factorisations
Authors: Zi Wang, Vyacheslav Karolis, Chiara Nosarti, Giovanni Montana
Date: 2016-05-09 00:00:00

Men and women differ in specific cognitive abilities and in the expression of
several neuropsychiatric conditions. Such findings could be attributed to sex
hormones, brain differences, as well as a number of environmental variables.
Existing research on identifying sex-related differences in brain structure
have predominantly used cross-sectional studies to investigate, for instance,
differences in average gray matter volumes (GMVs). In this article we explore
the potential of a recently proposed multi-view matrix factorisation (MVMF)
methodology to study structural brain changes in men and women that occur from
adolescence to adulthood. MVMF is a multivariate variance decomposition
technique that extends principal component analysis to "multi-view" datasets,
i.e. where multiple and related groups of observations are available. In this
application, each view represents a different age group. MVMF identifies latent
factors explaining shared and age-specific contributions to the observed
overall variability in GMVs over time. These latent factors can be used to
produce low-dimensional visualisations of the data that emphasise age-specific
effects once the shared effects have been accounted for. The analysis of two
datasets consisting of individuals born prematurely as well as healthy controls
provides evidence to suggest that the separation between males and females
becomes increasingly larger as the brain transitions from adolescence to
adulthood. We report on specific brain regions associated to these variance
effects.
Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices
Authors: Linda Marrakchi-Kacem, Alexandre Vignaud, Julien Sein, Johanne Germain, Thomas R Henry, Cyril Poupon, Lucie Hertz-Pannier, Stéphane Lehéricy, Olivier Colliot, Pierre-François Van de Moortele, Marie Chupin
Date: 2016-05-09 00:00:00
Tasks: Hippocampus

OBJECTIVE:Motion-robust multi-slab imaging of hippocampal inner structure in
vivo at 7T.MATERIALS AND METHODS:Motion is a crucial issue for ultra-high
resolution imaging, such as can be achieved with 7T MRI. An acquisition
protocol was designed for imaging hippocampal inner structure at 7T. It relies
on a compromise between anatomical details visibility and robustness to motion.
In order to reduce acquisition time and motion artifacts, the full slab
covering the hippocampus was split into separate slabs with lower acquisition
time. A robust registration approach was implemented to combine the acquired
slabs within a final 3D-consistent high-resolution slab covering the whole
hippocampus. Evaluation was performed on 50 subjects overall, made of three
groups of subjects acquired using three acquisition settings; it focused on
three issues: visibility of hippocampal inner structure, robustness to motion
artifacts and registration procedure performance.RESULTS:Overall, T2-weighted
acquisitions with interleaved slabs proved robust. Multi-slab registration
yielded high quality datasets in 96 % of the subjects, thus compatible with
further analyses of hippocampal inner structure.CONCLUSION:Multi-slab
acquisition and registration setting is efficient for reducing acquisition time
and consequently motion artifacts for ultra-high resolution imaging of the
inner structure of the hippocampus.
Efficiency Evaluation of Character-level RNN Training Schedules
Authors: Cedric De Boom, Sam Leroux, Steven Bohez, Pieter Simoens, Thomas Demeester, Bart Dhoedt
Date: 2016-05-09 00:00:00
Tasks: Prediction

We present four training and prediction schedules from the same
character-level recurrent neural network. The efficiency of these schedules is
tested in terms of model effectiveness as a function of training time and
amount of training data seen. We show that the choice of training and
prediction schedule potentially has a considerable impact on the prediction
effectiveness for a given training budget.
Visual Instance Retrieval with Deep Convolutional Networks
Authors: Ali Sharif Razavian, Josephine Sullivan, Stefan Carlsson, Atsuto Maki
Date: 2014-12-20 00:00:00
Tasks: Image Retrieval, Retrieval

This paper provides an extensive study on the availability of image
representations based on convolutional networks (ConvNets) for the task of
visual instance retrieval. Besides the choice of convolutional layers, we
present an efficient pipeline exploiting multi-scale schemes to extract local
features, in particular, by taking geometric invariance into explicit account,
i.e. positions, scales and spatial consistency. In our experiments using five
standard image retrieval datasets, we demonstrate that generic ConvNet image
representations can outperform other state-of-the-art methods if they are
extracted appropriately.
Randomized Kaczmarz for Rank Aggregation from Pairwise Comparisons
Authors: Vivek S. Borkar, Nikhil Karamchandani, Sharad Mirani
Date: 2016-05-09 00:00:00

We revisit the problem of inferring the overall ranking among entities in the
framework of Bradley-Terry-Luce (BTL) model, based on available empirical data
on pairwise preferences. By a simple transformation, we can cast the problem as
that of solving a noisy linear system, for which a ready algorithm is available
in the form of the randomized Kaczmarz method. This scheme is provably
convergent, has excellent empirical performance, and is amenable to on-line,
distributed and asynchronous variants. Convergence, convergence rate, and error
analysis of the proposed algorithm are presented and several numerical
experiments are conducted whose results validate our theoretical findings.
Orientation Driven Bag of Appearances for Person Re-identification
Authors: Liqian Ma, Hong Liu, Liang Hu, Can Wang, Qianru Sun
Date: 2016-05-09 00:00:00
Tasks: Person Re-Identification

Person re-identification (re-id) consists of associating individual across
camera network, which is valuable for intelligent video surveillance and has
drawn wide attention. Although person re-identification research is making
progress, it still faces some challenges such as varying poses, illumination
and viewpoints. For feature representation in re-identification, existing works
usually use low-level descriptors which do not take full advantage of body
structure information, resulting in low representation ability.
%discrimination. To solve this problem, this paper proposes the mid-level
body-structure based feature representation (BSFR) which introduces body
structure pyramid for codebook learning and feature pooling in the vertical
direction of human body. Besides, varying viewpoints in the horizontal
direction of human body usually causes the data missing problem, $i.e.$, the
appearances obtained in different orientations of the identical person could
vary significantly. To address this problem, the orientation driven bag of
appearances (ODBoA) is proposed to utilize person orientation information
extracted by orientation estimation technic. To properly evaluate the proposed
approach, we introduce a new re-identification dataset (Market-1203) based on
the Market-1501 dataset and propose a new re-identification dataset (PKU-Reid).
Both datasets contain multiple images captured in different body orientations
for each person. Experimental results on three public datasets and two proposed
datasets demonstrate the superiority of the proposed approach, indicating the
effectiveness of body structure and orientation information for improving
re-identification performance.
Fuzzy Clustering Based Segmentation Of Vertebrae in T1-Weighted Spinal MR Images
Authors: Jiyo. S. Athertya, G. Saravana Kumar
Date: 2016-05-09 00:00:00
Tasks: Clustering, Diagnostic, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation in the medical domain is a challenging field owing to poor
resolution and limited contrast. The predominantly used conventional
segmentation techniques and the thresholding methods suffer from limitations
because of heavy dependence on user interactions. Uncertainties prevalent in an
image cannot be captured by these techniques. The performance further
deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy
C- means clustering for segmenting vertebral body from magnetic resonance image
owing to its unsupervised form of learning. The motivation for this work is
detection of spine geometry and proper localisation and labelling will enhance
the diagnostic output of a physician. The method is compared with Otsu
thresholding and K-means clustering to illustrate the robustness.The reference
standard for validation was the annotated images from the radiologist, and the
Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
The Controlled Natural Language of Randall Munroe's Thing Explainer
Authors: Tobias Kuhn
Date: 2016-05-09 00:00:00

It is rare that texts or entire books written in a Controlled Natural
Language (CNL) become very popular, but exactly this has happened with a book
that has been published last year. Randall Munroe's Thing Explainer uses only
the 1'000 most often used words of the English language together with drawn
pictures to explain complicated things such as nuclear reactors, jet engines,
the solar system, and dishwashers. This restricted language is a very
interesting new case for the CNL community. I describe here its place in the
context of existing approaches on Controlled Natural Languages, and I provide a
first analysis from a scientific perspective, covering the word production
rules and word distributions.
A Bayesian approach to constrained single- and multi-objective optimization
Authors: Paul Feliot, Julien Bect, Emmanuel Vazquez
Date: 2015-10-02 00:00:00

This article addresses the problem of derivative-free (single- or
multi-objective) optimization subject to multiple inequality constraints. Both
the objective and constraint functions are assumed to be smooth, non-linear and
expensive to evaluate. As a consequence, the number of evaluations that can be
used to carry out the optimization is very limited, as in complex industrial
design optimization problems. The method we propose to overcome this difficulty
has its roots in both the Bayesian and the multi-objective optimization
literatures. More specifically, an extended domination rule is used to handle
objectives and constraints in a unified way, and a corresponding expected
hyper-volume improvement sampling criterion is proposed. This new criterion is
naturally adapted to the search of a feasible point when none is available, and
reduces to existing Bayesian sampling criteria---the classical Expected
Improvement (EI) criterion and some of its constrained/multi-objective
extensions---as soon as at least one feasible point is available. The
calculation and optimization of the criterion are performed using Sequential
Monte Carlo techniques. In particular, an algorithm similar to the subset
simulation method, which is well known in the field of structural reliability,
is used to estimate the criterion. The method, which we call BMOO (for Bayesian
Multi-Objective Optimization), is compared to state-of-the-art algorithms for
single- and multi-objective constrained optimization.
Machine Learning Techniques with Ontology for Subjective Answer Evaluation
Authors: M. Syamala Devi, Himani Mittal
Date: 2016-05-09 00:00:00
Tasks: BIG-bench Machine Learning

Computerized Evaluation of English Essays is performed using Machine learning
techniques like Latent Semantic Analysis (LSA), Generalized LSA, Bilingual
Evaluation Understudy and Maximum Entropy. Ontology, a concept map of domain
knowledge, can enhance the performance of these techniques. Use of Ontology
makes the evaluation process holistic as presence of keywords, synonyms, the
right word combination and coverage of concepts can be checked. In this paper,
the above mentioned techniques are implemented both with and without Ontology
and tested on common input data consisting of technical answers of Computer
Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools
such as MATLAB, Prot\'eg\'e, etc. Ten questions from Computer Graphics with
sixty answers for each question are used for testing. The results are analyzed
and it is concluded that the results are more accurate with use of Ontology.
Learning Discriminative Features with Class Encoder
Authors: Hailin Shi, Xiangyu Zhu, Zhen Lei, Shengcai Liao, Stan Z. Li
Date: 2016-05-09 00:00:00
Tasks: Face Recognition, Translation, Unsupervised Pre-training

Deep neural networks usually benefit from unsupervised pre-training, e.g.
auto-encoders. However, the classifier further needs supervised fine-tuning
methods for good discrimination. Besides, due to the limits of full-connection,
the application of auto-encoders is usually limited to small, well aligned
images. In this paper, we incorporate the supervised information to propose a
novel formulation, namely class-encoder, whose training objective is to
reconstruct a sample from another one of which the labels are identical.
Class-encoder aims to minimize the intra-class variations in the feature space,
and to learn a good discriminative manifolds on a class scale. We impose the
class-encoder as a constraint into the softmax for better supervised training,
and extend the reconstruction on feature-level to tackle the parameter size
issue and translation issue. The experiments show that the class-encoder helps
to improve the performance on benchmarks of classification and face
recognition. This could also be a promising direction for fast training of face
recognition models.