pwc-archive/papers-with-abstracts huggingface dataset:
Penalty, Shrinkage, and Preliminary Test Estimators under Full Model Hypothesis
Authors: Enayetur Raheem, A. K. Md. Ehsanes Saleh
Date: 2015-03-24 00:00:00
Tasks: regression

This paper considers a multiple regression model and compares, under full
model hypothesis, analytically as well as by simulation, the performance
characteristics of some popular penalty estimators such as ridge regression,
LASSO, adaptive LASSO, SCAD, and elastic net versus Least Squares Estimator,
restricted estimator, preliminary test estimator, and Stein-type estimators
when the dimension of the parameter space is smaller than the sample space
dimension. We find that RR uniformly dominates LSE, RE, PTE, SE and PRSE while
LASSO, aLASSO, SCAD, and EN uniformly dominates LSE only. Further, it is
observed that neither penalty estimators nor Stein-type estimator dominate one
another.
A Note on Information-Directed Sampling and Thompson Sampling
Authors: Li Zhou
Date: 2015-03-24 00:00:00
Tasks: Thompson Sampling

This note introduce three Bayesian style Multi-armed bandit algorithms:
Information-directed sampling, Thompson Sampling and Generalized Thompson
Sampling. The goal is to give an intuitive explanation for these three
algorithms and their regret bounds, and provide some derivations that are
omitted in the original papers.
DistancePPG: Robust non-contact vital signs monitoring using a camera
Authors: Mayank Kumar, Ashok Veeraraghavan, Ashutosh Sabharval
Date: 2015-02-27 00:00:00

Vital signs such as pulse rate and breathing rate are currently measured
using contact probes. But, non-contact methods for measuring vital signs are
desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ
health tracking (e.g. on mobile phone and computers with webcams). Recently,
camera-based non-contact vital sign monitoring have been shown to be feasible.
However, camera-based vital sign monitoring is challenging for people with
darker skin tone, under low lighting conditions, and/or during movement of an
individual in front of the camera. In this paper, we propose distancePPG, a new
camera-based vital sign estimation algorithm which addresses these challenges.
DistancePPG proposes a new method of combining skin-color change signals from
different tracked regions of the face using a weighted average, where the
weights depend on the blood perfusion and incident light intensity in the
region, to improve the signal-to-noise ratio (SNR) of camera-based estimate.
One of our key contributions is a new automatic method for determining the
weights based only on the video recording of the subject. The gains in SNR of
camera-based PPG estimated using distancePPG translate into reduction of the
error in vital sign estimation, and thus expand the scope of camera-based vital
sign monitoring to potentially challenging scenarios. Further, a dataset will
be released, comprising of synchronized video recordings of face and pulse
oximeter based ground truth recordings from the earlobe for people with
different skin tones, under different lighting conditions and for various
motion scenarios.
The local low-dimensionality of natural images
Authors: Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz, Eero P. Simoncelli
Date: 2014-12-20 00:00:00
Tasks: Denoising

We develop a new statistical model for photographic images, in which the
local responses of a bank of linear filters are described as jointly Gaussian,
with zero mean and a covariance that varies slowly over spatial position. We
optimize sets of filters so as to minimize the nuclear norms of matrices of
their local activations (i.e., the sum of the singular values), thus
encouraging a flexible form of sparsity that is not tied to any particular
dictionary or coordinate system. Filters optimized according to this objective
are oriented and bandpass, and their responses exhibit substantial local
correlation. We show that images can be reconstructed nearly perfectly from
estimates of the local filter response covariances alone, and with minimal
degradation (either visual or MSE) from low-rank approximations of these
covariances. As such, this representation holds much promise for use in
applications such as denoising, compression, and texture representation, and
may form a useful substrate for hierarchical decompositions.
On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems
Authors: Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
Date: 2015-03-23 00:00:00
Tasks: valid

We develop a novel framework to study smooth and strongly convex optimization
algorithms, both deterministic and stochastic. Focusing on quadratic functions
we are able to examine optimization algorithms as a recursive application of
linear operators. This, in turn, reveals a powerful connection between a class
of optimization algorithms and the analytic theory of polynomials whereby new
lower and upper bounds are derived. Whereas existing lower bounds for this
setting are only valid when the dimensionality scales with the number of
iterations, our lower bound holds in the natural regime where the
dimensionality is fixed. Lastly, expressing it as an optimal solution for the
corresponding optimization problem over polynomials, as formulated by our
framework, we present a novel systematic derivation of Nesterov's well-known
Accelerated Gradient Descent method. This rather natural interpretation of AGD
contrasts with earlier ones which lacked a simple, yet solid, motivation.
Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring
Authors: Robert Amelard, Christian Scharfenberger, Farnoud Kazemzadeh, Kaylen J. Pfisterer, Bill S. Lin, Alexander Wong, David A. Clausi
Date: 2015-03-23 00:00:00
Tasks: Photoplethysmography (PPG)

Photoplethysmography (PPG) devices are widely used for monitoring
cardiovascular function. However, these devices require skin contact, which
restrict their use to at-rest short-term monitoring using single-point
measurements. Photoplethysmographic imaging (PPGI) has been recently proposed
as a non-contact monitoring alternative by measuring blood pulse signals across
a spatial region of interest. Existing systems operate in reflectance mode, of
which many are limited to short-distance monitoring and are prone to temporal
changes in ambient illumination. This paper is the first study to investigate
the feasibility of long-distance non-contact cardiovascular monitoring at the
supermeter level using transmittance PPGI. For this purpose, a novel PPGI
system was designed at the hardware and software level using ambient correction
via temporally coded illumination (TCI) and signal processing for PPGI signal
extraction. Experimental results show that the processing steps yield a
substantially more pulsatile PPGI signal than the raw acquired signal,
resulting in statistically significant increases in correlation to ground-truth
PPG in both short- ($p \in [<0.0001, 0.040]$) and long-distance ($p \in
[<0.0001, 0.056]$) monitoring. The results support the hypothesis that
long-distance heart rate monitoring is feasible using transmittance PPGI,
allowing for new possibilities of monitoring cardiovascular function in a
non-contact manner.
Unsupervised POS Induction with Word Embeddings
Authors: Chu-Cheng Lin, Waleed Ammar, Chris Dyer, Lori Levin
Date: 2015-03-23 00:00:00
Proceeding: HLT 2015 5
Tasks: POS, Word Embeddings

Unsupervised word embeddings have been shown to be valuable as features in
supervised learning problems; however, their role in unsupervised problems has
been less thoroughly explored. In this paper, we show that embeddings can
likewise add value to the problem of unsupervised POS induction. In two
representative models of POS induction, we replace multinomial distributions
over the vocabulary with multivariate Gaussian distributions over word
embeddings and observe consistent improvements in eight languages. We also
analyze the effect of various choices while inducing word embeddings on
"downstream" POS induction results.
Continuous Localization and Mapping of a Pan Tilt Zoom Camera for Wide Area Tracking
Authors: Giuseppe Lisanti, Iacopo Masi, Federico Pernici, Alberto del Bimbo
Date: 2014-01-26 00:00:00
Tasks: Camera Calibration, Motion Compensation, Position

Pan-tilt-zoom (PTZ) cameras are powerful to support object identification and
recognition in far-field scenes. However, the effective use of PTZ cameras in
real contexts is complicated by the fact that a continuous on-line camera
calibration is needed and the absolute pan, tilt and zoom positional values
provided by the camera actuators cannot be used because are not synchronized
with the video stream. So, accurate calibration must be directly extracted from
the visual content of the frames. Moreover, the large and abrupt scale changes,
the scene background changes due to the camera operation and the need of camera
motion compensation make target tracking with these cameras extremely
challenging. In this paper, we present a solution that provides continuous
on-line calibration of PTZ cameras which is robust to rapid camera motion,
changes of the environment due to illumination or moving objects and scales
beyond thousands of landmarks. The method directly derives the relationship
between the position of a target in the 3D world plane and the corresponding
scale and position in the 2D image, and allows real-time tracking of multiple
targets with high and stable degree of accuracy even at far distances and any
zooming level.
Online classifier adaptation for cost-sensitive learning
Authors: Junlin Zhang, Jose Garcia
Date: 2015-03-23 00:00:00
Tasks: Classification, General Classification

In this paper, we propose the problem of online cost-sensitive clas- sifier
adaptation and the first algorithm to solve it. We assume we have a base
classifier for a cost-sensitive classification problem, but it is trained with
respect to a cost setting different to the desired one. Moreover, we also have
some training data samples streaming to the algorithm one by one. The prob- lem
is to adapt the given base classifier to the desired cost setting using the
steaming training samples online. To solve this problem, we propose to learn a
new classifier by adding an adaptation function to the base classifier, and
update the adaptation function parameter according to the streaming data
samples. Given a input data sample and the cost of misclassifying it, we up-
date the adaptation function parameter by minimizing cost weighted hinge loss
and respecting previous learned parameter simultaneously. The proposed
algorithm is compared to both online and off-line cost-sensitive algorithms on
two cost-sensitive classification problems, and the experiments show that it
not only outperforms them one classification performances, but also requires
significantly less running time.
GSNs : Generative Stochastic Networks
Authors: Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent
Date: 2015-03-18 00:00:00
Tasks: Denoising

We introduce a novel training principle for probabilistic models that is an
alternative to maximum likelihood. The proposed Generative Stochastic Networks
(GSN) framework is based on learning the transition operator of a Markov chain
whose stationary distribution estimates the data distribution. Because the
transition distribution is a conditional distribution generally involving a
small move, it has fewer dominant modes, being unimodal in the limit of small
moves. Thus, it is easier to learn, more like learning to perform supervised
function approximation, with gradients that can be obtained by
back-propagation. The theorems provided here generalize recent work on the
probabilistic interpretation of denoising auto-encoders and provide an
interesting justification for dependency networks and generalized
pseudolikelihood (along with defining an appropriate joint distribution and
sampling mechanism, even when the conditionals are not consistent). We study
how GSNs can be used with missing inputs and can be used to sample subsets of
variables given the rest. Successful experiments are conducted, validating
these theoretical results, on two image datasets and with a particular
architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows
training to proceed with backprop, without the need for layerwise pretraining.
A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation
Authors: Toufiq Parag, Anirban Chakraborty, Stephen Plaza, Lou Scheffer
Date: 2014-06-05 00:00:00
Tasks: Clustering, Segmentation, Superpixels

Electron Microscopy (EM) image (or volume) segmentation has become
significantly important in recent years as an instrument for connectomics. This
paper proposes a novel agglomerative framework for EM segmentation. In
particular, given an over-segmented image or volume, we propose a novel
framework for accurately clustering regions of the same neuron. Unlike existing
agglomerative methods, the proposed context-aware algorithm divides superpixels
(over-segmented regions) of different biological entities into different
subsets and agglomerates them separately. In addition, this paper describes a
"delayed" scheme for agglomerative clustering that postpones some of the merge
decisions, pertaining to newly formed bodies, in order to generate a more
confident boundary prediction. We report significant improvements attained by
the proposed approach in segmentation accuracy over existing standard methods
on 2D and 3D datasets.
Vehicle Local Position Estimation System
Authors: Mrinal Haloi, Dinesh Babu Jayagopi
Date: 2015-03-23 00:00:00
Tasks: object-detection, Object Detection, Position

In this paper, a robust vehicle local position estimation with the help of
single camera sensor and GPS is presented. A modified Inverse Perspective
Mapping, illuminant Invariant techniques and object detection based approach is
used to localize the vehicle in the road. Vehicles current lane, its position
from road boundary and other cars are used to define its local position. For
this purpose Lane markings are detected using a Laplacian edge feature, robust
to shadowing. Effect of shadowing and extra sun light are removed using Lab
color space and illuminant invariant techniques. Lanes are assumed to be as
parabolic model and fitted using robust RANSAC. This method can reliably detect
all lanes of the road, estimate lane departure angle and local position of
vehicle relative to lanes, road boundary and other cars. Different type of
obstacle like pedestrians, vehicles are detected using HOG feature based
deformable part model.
A novel pLSA based Traffic Signs Classification System
Authors: Mrinal Haloi
Date: 2015-03-23 00:00:00
Tasks: Autonomous Driving, Classification, General Classification, Traffic Sign Recognition

In this work we developed a novel and fast traffic sign recognition system, a
very important part for advanced driver assistance system and for autonomous
driving. Traffic signs play a very vital role in safe driving and avoiding
accident. We have used image processing and topic discovery model pLSA to
tackle this challenging multiclass classification problem. Our algorithm is
consist of two parts, shape classification and sign classification for improved
accuracy. For processing and representation of image we have used bag of
features model with SIFT local descriptor. Where a visual vocabulary of size
300 words are formed using k-means codebook formation algorithm. We exploited
the concept that every image is a collection of visual topics and images having
same topics will belong to same category. Our algorithm is tested on German
traffic sign recognition benchmark (GTSRB) and gives very promising result near
to existing state of the art techniques.
Superpixelizing Binary MRF for Image Labeling Problems
Authors: Junyan Wang, Sai-Kit Yeung
Date: 2015-03-23 00:00:00
Tasks: Superpixels

Superpixels have become prevalent in computer vision. They have been used to
achieve satisfactory performance at a significantly smaller computational cost
for various tasks. People have also combined superpixels with Markov random
field (MRF) models. However, it often takes additional effort to formulate MRF
on superpixel-level, and to the best of our knowledge there exists no
principled approach to obtain this formulation. In this paper, we show how
generic pixel-level binary MRF model can be solved in the superpixel space. As
the main contribution of this paper, we show that a superpixel-level MRF can be
derived from the pixel-level MRF by substituting the superpixel representation
of the pixelwise label into the original pixel-level MRF energy. The resultant
superpixel-level MRF energy also remains submodular for a submodular
pixel-level MRF. The derived formula hence gives us a handy way to formulate
MRF energy in superpixel-level. In the experiments, we demonstrate the efficacy
of our approach on several computer vision problems.
Distributed Online Learning via Cooperative Contextual Bandits
Authors: Cem Tekin, Mihaela van der Schaar
Date: 2013-08-21 00:00:00
Tasks: Event Detection, Multi-Armed Bandits, Recommendation Systems

In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or request assistance from another learner. In the latter case,
the requester pays a cost and receives the reward but the provider learns the
information. In our framework, learners are modeled as cooperative contextual
bandits. Each learner seeks to maximize the expected reward from its arrivals,
which involves trading off the reward received from its own actions, the
information learned from its own actions, the reward received from the actions
requested of others and the cost paid for these actions - taking into account
what it has learned about the value of assistance from each other learner. We
develop distributed online learning algorithms and provide analytic bounds to
compare the efficiency of these with algorithms with the complete knowledge
(oracle) benchmark (in which the expected reward of every action in every
context is known by every learner). Our estimates show that regret - the loss
incurred by the algorithm - is sublinear in time. Our theoretical framework can
be used in many practical applications including Big Data mining, event
detection in surveillance sensor networks and distributed online recommendation
systems.
Advances in Bayesian Network Learning using Integer Programming
Authors: Mark Bartlett, James Cussens
Date: 2013-09-26 00:00:00

We consider the problem of learning Bayesian networks (BNs) from complete
discrete data. This problem of discrete optimisation is formulated as an
integer program (IP). We describe the various steps we have taken to allow
efficient solving of this IP. These are (i) efficient search for cutting
planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal)
BNs and (iii) tightening the linear relaxation of the IP. After relating this
BN learning problem to set covering and the multidimensional 0-1 knapsack
problem, we present our empirical results. These show improvements, sometimes
dramatic, over earlier results.
Off-Policy Reward Shaping with Ensembles
Authors: Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowe
Date: 2015-02-11 00:00:00
Tasks: Reinforcement Learning

Potential-based reward shaping (PBRS) is an effective and popular technique
to speed up reinforcement learning by leveraging domain knowledge. While PBRS
is proven to always preserve optimal policies, its effect on learning speed is
determined by the quality of its potential function, which, in turn, depends on
both the underlying heuristic and the scale. Knowing which heuristic will prove
effective requires testing the options beforehand, and determining the
appropriate scale requires tuning, both of which introduce additional sample
complexity. We formulate a PBRS framework that reduces learning speed, but does
not incur extra sample complexity. For this, we propose to simultaneously learn
an ensemble of policies, shaped w.r.t. many heuristics and on a range of
scales. The target policy is then obtained by voting. The ensemble needs to be
able to efficiently and reliably learn off-policy: requirements fulfilled by
the recent Horde architecture, which we take as our basis. We demonstrate
empirically that (1) our ensemble policy outperforms both the base policy, and
its single-heuristic components, and (2) an ensemble over a general range of
scales performs at least as well as one with optimally tuned components.
A Probabilistic Interpretation of Sampling Theory of Graph Signals
Authors: Akshay Gadde, Antonio Ortega
Date: 2015-03-23 00:00:00
Tasks: General Classification

We give a probabilistic interpretation of sampling theory of graph signals.
To do this, we first define a generative model for the data using a pairwise
Gaussian random field (GRF) which depends on the graph. We show that, under
certain conditions, reconstructing a graph signal from a subset of its samples
by least squares is equivalent to performing MAP inference on an approximation
of this GRF which has a low rank covariance matrix. We then show that a
sampling set of given size with the largest associated cut-off frequency, which
is optimal from a sampling theoretic point of view, minimizes the worst case
predictive covariance of the MAP estimate on the GRF. This interpretation also
gives an intuitive explanation for the superior performance of the sampling
theoretic approach to active semi-supervised classification.
Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast
Authors: Lakshmi Devasena C
Date: 2015-03-23 00:00:00

Predicting the Credit Defaulter is a perilous task of Financial Industries
like Banks. Ascertaining non-payer before giving loan is a significant and
conflict-ridden task of the Banker. Classification techniques are the better
choice for predictive analysis like finding the claimant, whether he/she is an
unpretentious customer or a cheat. Defining the outstanding classifier is a
risky assignment for any industrialist like a banker. This allow computer
science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive
problems. This research work investigates the productivity of LADTree
Classifier and REPTree Classifier for the credit risk prediction and compares
their fitness through various measures. German credit dataset has been taken
and used to predict the credit risk with a help of open source machine learning
tool.
Using Distance Estimation and Deep Learning to Simplify Calibration in Food Calorie Measurement
Authors: Pallavi Kuhad, Abdulsalam Yassine, Shervin Shirmohammadi
Date: 2015-02-11 00:00:00
Tasks: Cloud Computing

High calorie intake in the human body on the one hand, has proved harmful in
numerous occasions leading to several diseases and on the other hand, a
standard amount of calorie intake has been deemed essential by dieticians to
maintain the right balance of calorie content in human body. As such,
researchers have proposed a variety of automatic tools and systems to assist
users measure their calorie in-take. In this paper, we consider the category of
those tools that use image processing to recognize the food, and we propose a
method for fully automatic and user-friendly calibration of the dimension of
the food portion sizes, which is needed in order to measure food portion weight
and its ensuing amount of calories. Experimental results show that our method,
which uses deep learning, mobile cloud computing, distance estimation and size
calibration inside a mobile device, leads to an accuracy improvement to 95% on
average compared to previous work