pwc-archive/papers-with-abstracts huggingface dataset:
Improved Calibration of Near-Infrared Spectra by Using Ensembles of Neural Network Models
Authors: A. Ukil, J. Bernasconi, H. Braendle, H. Buijs, S. Bonenfant
Date: 2015-03-18 00:00:00
Tasks: regression

IR or near-infrared (NIR) spectroscopy is a method used to identify a
compound or to analyze the composition of a material. Calibration of NIR
spectra refers to the use of the spectra as multivariate descriptors to predict
concentrations of the constituents. To build a calibration model,
state-of-the-art software predominantly uses linear regression techniques. For
nonlinear calibration problems, neural network-based models have proved to be
an interesting alternative. In this paper, we propose a novel extension of the
conventional neural network-based approach, the use of an ensemble of neural
network models. The individual neural networks are obtained by resampling the
available training data with bootstrapping or cross-validation techniques. The
results obtained for a realistic calibration example show that the
ensemble-based approach produces a significantly more accurate and robust
calibration model than conventional regression methods.
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient
Authors: Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi
Date: 2015-03-10 00:00:00
Tasks: Clustering, Matrix Completion

Nuclear-norm regularization plays a vital role in many learning tasks, such
as low-rank matrix recovery (MR), and low-rank representation (LRR). Solving
this problem directly can be computationally expensive due to the unknown rank
of variables or large-rank singular value decompositions (SVDs). To address
this, we propose a proximal Riemannian gradient (PRG) scheme which can
efficiently solve trace-norm regularized problems defined on real-algebraic
variety $\mMLr$ of real matrices of rank at most $r$. Based on PRG, we further
present a simple and novel subspace pursuit (SP) paradigm for general
trace-norm regularized problems without the explicit rank constraint $\mMLr$.
The proposed paradigm is very scalable by avoiding large-rank SVDs. Empirical
studies on several tasks, such as matrix completion and LRR based subspace
clustering, demonstrate the superiority of the proposed paradigms over existing
methods.
Improved LASSO
Authors: A. K. Md. Ehsanes Saleh, Enayetur Raheem
Date: 2015-03-17 00:00:00

We propose an improved LASSO estimation technique based on Stein-rule. We
shrink classical LASSO estimator using preliminary test, shrinkage, and
positive-rule shrinkage principle. Simulation results have been carried out for
various configurations of correlation coefficients ($r$), size of the parameter
vector ($\beta$), error variance ($\sigma^2$) and number of non-zero
coefficients ($k$) in the model parameter vector. Several real data examples
have been used to demonstrate the practical usefulness of the proposed
estimators. Our study shows that the risk ordering given by LSE $>$ LASSO $>$
Stein-type LASSO $>$ Stein-type positive rule LASSO, remains the same uniformly
in the divergence parameter $\Delta^2$ as in the traditional case.
Prediction Using Note Text: Synthetic Feature Creation with word2vec
Authors: Manuel Amunategui, Tristan Markwell, Yelena Rozenfeld
Date: 2015-03-17 00:00:00
Tasks: Prediction

word2vec affords a simple yet powerful approach of extracting quantitative
variables from unstructured textual data. Over half of healthcare data is
unstructured and therefore hard to model without involved expertise in data
engineering and natural language processing. word2vec can serve as a bridge to
quickly gather intelligence from such data sources.
  In this study, we ran 650 megabytes of unstructured, medical chart notes from
the Providence Health & Services electronic medical record through word2vec. We
used two different approaches in creating predictive variables and tested them
on the risk of readmission for patients with COPD (Chronic Obstructive Lung
Disease). As a comparative benchmark, we ran the same test using the LACE risk
model (a single score based on length of stay, acuity, comorbid conditions, and
emergency department visits).
  Using only free text and mathematical might, we found word2vec comparable to
LACE in predicting the risk of readmission of COPD patients.
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
Authors: Ha Quang Minh, Loris Bazzani, Vittorio Murino
Date: 2014-01-31 00:00:00
Tasks: MULTI-VIEW LEARNING, Object Recognition

This paper presents a general vector-valued reproducing kernel Hilbert spaces
(RKHS) framework for the problem of learning an unknown functional dependency
between a structured input space and a structured output space. Our formulation
encompasses both Vector-valued Manifold Regularization and Co-regularized
Multi-view Learning, providing in particular a unifying framework linking these
two important learning approaches. In the case of the least square loss
function, we provide a closed form solution, which is obtained by solving a
system of linear equations. In the case of Support Vector Machine (SVM)
classification, our formulation generalizes in particular both the binary
Laplacian SVM to the multi-class, multi-view settings and the multi-class
Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is
obtained by solving a single quadratic optimization problem, as in standard
SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results
obtained on the task of object recognition, using several challenging datasets,
demonstrate the competitiveness of our algorithms compared with other
state-of-the-art methods.
Quantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop
Authors: Keyan Ghazi-Zahedi, Johannes Rauh
Date: 2015-03-17 00:00:00
Tasks: Artificial Life

The question how an agent is affected by its embodiment has attracted growing
attention in recent years. A new field of artificial intelligence has emerged,
which is based on the idea that intelligence cannot be understood without
taking into account embodiment. We believe that a formal approach to
quantifying the embodiment's effect on the agent's behaviour is beneficial to
the fields of artificial life and artificial intelligence. The contribution of
an agent's body and environment to its behaviour is also known as morphological
computation. Therefore, in this work, we propose a quantification of
morphological computation, which is based on an information decomposition of
the sensorimotor loop into shared, unique and synergistic information. In
numerical simulation based on a formal representation of the sensorimotor loop,
we show that the unique information of the body and environment is a good
measure for morphological computation. The results are compared to our
previously derived quantification of morphological computation.
Combining partially independent belief functions
Authors: Mouna Chebbah, Arnaud Martin, Boutheina Ben Yaghlane
Date: 2015-03-17 00:00:00

The theory of belief functions manages uncertainty and also proposes a set of
combination rules to aggregate opinions of several sources. Some combination
rules mix evidential information where sources are independent; other rules are
suited to combine evidential information held by dependent sources. In this
paper we have two main contributions: First we suggest a method to quantify
sources' degree of independence that may guide the choice of the more
appropriate set of combination rules. Second, we propose a new combination rule
that takes consideration of sources' degree of independence. The proposed
method is illustrated on generated mass functions.
3D Object Class Detection in the Wild
Authors: Bojan Pepik, Michael Stark, Peter Gehler, Tobias Ritschel, Bernt Schiele
Date: 2015-03-17 00:00:00
Tasks: Object, object-detection, Object Detection, Viewpoint Estimation

Object class detection has been a synonym for 2D bounding box localization
for the longest time, fueled by the success of powerful statistical learning
techniques, combined with robust image representations. Only recently, there
has been a growing interest in revisiting the promise of computer vision from
the early days: to precisely delineate the contents of a visual scene, object
by object, in 3D. In this paper, we draw from recent advances in object
detection and 2D-3D object lifting in order to design an object class detector
that is particularly tailored towards 3D object class detection. Our 3D object
class detection method consists of several stages gradually enriching the
object detection output with object viewpoint, keypoints and 3D shape
estimates. Following careful design, in each stage it constantly improves the
performance and achieves state-ofthe-art performance in simultaneous 2D
bounding box and viewpoint estimation on the challenging Pascal3D+ dataset.
PiMPeR: Piecewise Dense 3D Reconstruction from Multi-View and Multi-Illumination Images
Authors: Reza Sabzevari, Vittori Murino, Alessio Del Bue
Date: 2015-03-16 00:00:00
Tasks: 3D Reconstruction

In this paper, we address the problem of dense 3D reconstruction from
multiple view images subject to strong lighting variations. In this regard, a
new piecewise framework is proposed to explicitly take into account the change
of illumination across several wide-baseline images. Unlike multi-view stereo
and multi-view photometric stereo methods, this pipeline deals with
wide-baseline images that are uncalibrated, in terms of both camera parameters
and lighting conditions. Such a scenario is meant to avoid use of any specific
imaging setup and provide a tool for normal users without any expertise. To the
best of our knowledge, this paper presents the first work that deals with such
unconstrained setting. We propose a coarse-to-fine approach, in which a coarse
mesh is first created using a set of geometric constraints and, then, fine
details are recovered by exploiting photometric properties of the scene.
Augmenting the fine details on the coarse mesh is done via a final optimization
step. Note that the method does not provide a generic solution for multi-view
photometric stereo problem but it relaxes several common assumptions of this
problem. The approach scales very well in size given its piecewise nature,
dealing with large scale optimization and with severe missing data. Experiments
on a benchmark dataset Robot data-set show the method performance against 3D
ground truth.
Ultra-Fast Shapelets for Time Series Classification
Authors: Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme
Date: 2015-03-17 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time series shapelets are discriminative subsequences and their similarity to
a time series can be used for time series classification. Since the discovery
of time series shapelets is costly in terms of time, the applicability on long
or multivariate time series is difficult. In this work we propose Ultra-Fast
Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast
Shapelets yield the same prediction quality as current state-of-the-art
shapelet-based time series classifiers that carefully select the shapelets by
being by up to three orders of magnitudes. Since this method allows a
ultra-fast shapelet discovery, using shapelets for long multivariate time
series classification becomes feasible.
  A method for using shapelets for multivariate time series is proposed and
Ultra-Fast Shapelets is proven to be successful in comparison to
state-of-the-art multivariate time series classifiers on 15 multivariate time
series datasets from various domains. Finally, time series derivatives that
have proven to be useful for other time series classifiers are investigated for
the shapelet-based classifiers. It is shown that they have a positive impact
and that they are easy to integrate with a simple preprocessing step, without
the need of adapting the shapelet discovery algorithm.
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests
Authors: Khaled Fawagreh, Mohamad Medhat Gaber, Eyad Elyan
Date: 2015-03-17 00:00:00
Tasks: Ensemble Pruning, Outlier Detection

Random Forest (RF) is an ensemble classification technique that was developed
by Breiman over a decade ago. Compared with other ensemble techniques, it has
proved its accuracy and superiority. Many researchers, however, believe that
there is still room for enhancing and improving its performance in terms of
predictive accuracy. This explains why, over the past decade, there have been
many extensions of RF where each extension employed a variety of techniques and
strategies to improve certain aspect(s) of RF. Since it has been proven
empirically that ensembles tend to yield better results when there is a
significant diversity among the constituent models, the objective of this paper
is twofolds. First, it investigates how an unsupervised learning technique,
namely, Local Outlier Factor (LOF) can be used to identify diverse trees in the
RF. Second, trees with the highest LOF scores are then used to produce an
extension of RF termed LOFB-DRF that is much smaller in size than RF, and yet
performs at least as good as RF, but mostly exhibits higher performance in
terms of accuracy. The latter refers to a known technique called ensemble
pruning. Experimental results on 10 real datasets prove the superiority of our
proposed extension over the traditional RF. Unprecedented pruning levels
reaching 99% have been achieved at the time of boosting the predictive accuracy
of the ensemble. The notably high pruning level makes the technique a good
candidate for real-time applications.
On Extreme Pruning of Random Forest Ensembles for Real-time Predictive Applications
Authors: Khaled Fawagreh, Mohamad Medhat Gaber, Eyad Elyan
Date: 2015-03-17 00:00:00
Tasks: Clustering, Diversity, Ensemble Pruning

Random Forest (RF) is an ensemble supervised machine learning technique that
was developed by Breiman over a decade ago. Compared with other ensemble
techniques, it has proved its accuracy and superiority. Many researchers,
however, believe that there is still room for enhancing and improving its
performance accuracy. This explains why, over the past decade, there have been
many extensions of RF where each extension employed a variety of techniques and
strategies to improve certain aspect(s) of RF. Since it has been proven
empiricallthat ensembles tend to yield better results when there is a
significant diversity among the constituent models, the objective of this paper
is twofold. First, it investigates how data clustering (a well known diversity
technique) can be applied to identify groups of similar decision trees in an RF
in order to eliminate redundant trees by selecting a representative from each
group (cluster). Second, these likely diverse representatives are then used to
produce an extension of RF termed CLUB-DRF that is much smaller in size than
RF, and yet performs at least as good as RF, and mostly exhibits higher
performance in terms of accuracy. The latter refers to a known technique called
ensemble pruning. Experimental results on 15 real datasets from the UCI
repository prove the superiority of our proposed extension over the traditional
RF. Most of our experiments achieved at least 95% or above pruning level while
retaining or outperforming the RF accuracy.
Energy Sharing for Multiple Sensor Nodes with Finite Buffers
Authors: Sindhu Padakandla, Prabuchandran K. J, Shalabh Bhatnagar
Date: 2015-03-17 00:00:00
Tasks: Q-Learning

We consider the problem of finding optimal energy sharing policies that
maximize the network performance of a system comprising of multiple sensor
nodes and a single energy harvesting (EH) source. Sensor nodes periodically
sense the random field and generate data, which is stored in the corresponding
data queues. The EH source harnesses energy from ambient energy sources and the
generated energy is stored in an energy buffer. Sensor nodes receive energy for
data transmission from the EH source. The EH source has to efficiently share
the stored energy among the nodes in order to minimize the long-run average
delay in data transmission. We formulate the problem of energy sharing between
the nodes in the framework of average cost infinite-horizon Markov decision
processes (MDPs). We develop efficient energy sharing algorithms, namely
Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy
method as well as upper confidence bound (UCB). We extend these algorithms by
incorporating state and action space aggregation to tackle state-action space
explosion in the MDP. We also develop a cross entropy based method that
incorporates policy parameterization in order to find near optimal energy
sharing policies. Through simulations, we show that our algorithms yield energy
sharing policies that outperform the heuristic greedy method.
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
Authors: Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi
Date: 2014-11-30 00:00:00

In this paper, we develop an approach to exploiting kernel methods with
manifold-valued data. In many computer vision problems, the data can be
naturally represented as points on a Riemannian manifold. Due to the
non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision
and machine learning algorithms yield inferior results on such data. In this
paper, we define Gaussian radial basis function (RBF)-based positive definite
kernels on manifolds that permit us to embed a given manifold with a
corresponding metric in a high dimensional reproducing kernel Hilbert space.
These kernels make it possible to utilize algorithms developed for linear
spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with
any given metric is not always positive definite, we present a unified
framework for analyzing the positive definiteness of the Gaussian RBF on a
generic metric space. We then use the proposed framework to identify positive
definite kernels on two specific manifolds commonly encountered in computer
vision: the Riemannian manifold of symmetric positive definite matrices and the
Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a
Euclidean space. We show that many popular algorithms designed for Euclidean
spaces, such as support vector machines, discriminant analysis and principal
component analysis can be generalized to Riemannian manifolds with the help of
such positive definite Gaussian kernels.
How the symbol grounding of living organisms can be realized in artificial agents
Authors: J. H. van Hateren
Date: 2015-03-17 00:00:00

A system with artificial intelligence usually relies on symbol manipulation,
at least partly and implicitly. However, the interpretation of the symbols -
what they represent and what they are about - is ultimately left to humans, as
designers and users of the system. How symbols can acquire meaning for the
system itself, independent of external interpretation, is an unsolved problem.
Some grounding of symbols can be obtained by embodiment, that is, by causally
connecting symbols (or sub-symbolic variables) to the physical environment,
such as in a robot with sensors and effectors. However, a causal connection as
such does not produce representation and aboutness of the kind that symbols
have for humans. Here I present a theory that explains how humans and other
living organisms have acquired the capability to have symbols and sub-symbolic
variables that represent, refer to, and are about something else. The theory
shows how reference can be to physical objects, but also to abstract objects,
and even how it can be misguided (errors in reference) or be about non-existing
objects. I subsequently abstract the primary components of the theory from
their biological context, and discuss how and under what conditions the theory
could be implemented in artificial agents. A major component of the theory is
the strong nonlinearity associated with (potentially unlimited)
self-reproduction. The latter is likely not acceptable in artificial systems.
It remains unclear if goals other than those inherently serving
self-reproduction can have aboutness and if such goals could be stabilized.
Hypoelliptic Diffusion Maps I: Tangent Bundles
Authors: Tingran Gao
Date: 2015-03-17 00:00:00
Tasks: Dimensionality Reduction

We introduce the concept of Hypoelliptic Diffusion Maps (HDM), a framework
generalizing Diffusion Maps in the context of manifold learning and
dimensionality reduction. Standard non-linear dimensionality reduction methods
(e.g., LLE, ISOMAP, Laplacian Eigenmaps, Diffusion Maps) focus on mining
massive data sets using weighted affinity graphs; Orientable Diffusion Maps and
Vector Diffusion Maps enrich these graphs by attaching to each node also some
local geometry. HDM likewise considers a scenario where each node possesses
additional structure, which is now itself of interest to investigate.
Virtually, HDM augments the original data set with attached structures, and
provides tools for studying and organizing the augmented ensemble. The goal is
to obtain information on individual structures attached to the nodes and on the
relationship between structures attached to nearby nodes, so as to study the
underlying manifold from which the nodes are sampled. In this paper, we analyze
HDM on tangent bundles, revealing its intimate connection with sub-Riemannian
geometry and a family of hypoelliptic differential operators. In a later paper,
we shall consider more general fibre bundles.
Guaranteed Scalable Learning of Latent Tree Models
Authors: Furong Huang, Niranjan U. N., Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar
Date: 2014-06-18 00:00:00
Tasks: parameter estimation

We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns models over small groups of variables and iteratively merges onto a global solution. The structure learning involves combinatorial operations such as minimum spanning tree construction and local recursive grouping; the parameter learning is based on the method of moments and on tensor decompositions. Our method is guaranteed to correctly recover the unknown tree structure and the model parameters with low sample complexity for the class of linear multivariate latent tree models which includes discrete and Gaussian distributions, and Gaussian mixtures. Our bulk asynchronous parallel algorithm is implemented in parallel and the parallel computation complexity increases only logarithmically with the number of variables and linearly with dimensionality of each variable.
Long Short-Term Memory Over Tree Structures
Authors: Xiaodan Zhu, Parinaz Sobhani, Hongyu Guo
Date: 2015-03-16 00:00:00
Tasks: Machine Translation, Natural Language Understanding, Semantic Composition, speech-recognition, Speech Recognition, Translation

The chain-structured long short-term memory (LSTM) has showed to be effective
in a wide range of problems such as speech recognition and machine translation.
In this paper, we propose to extend it to tree structures, in which a memory
cell can reflect the history memories of multiple child cells or multiple
descendant cells in a recursive process. We call the model S-LSTM, which
provides a principled way of considering long-distance interaction over
hierarchies, e.g., language or image parse structures. We leverage the models
for semantic composition to understand the meaning of text, a fundamental
problem in natural language understanding, and show that it outperforms a
state-of-the-art recursive model by replacing its composition layers with the
S-LSTM memory blocks. We also show that utilizing the given structures is
helpful in achieving a performance better than that without considering the
structures.