pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Sample Size Determination of Vibration Signals in Machine Learning Approach to Fault Diagnosis of Roller Bearings
Authors: Siddhant Sahu, V. Sugumaran
Date: 2014-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Fault Diagnosis

Sample size determination for a data set is an important statistical process
for analyzing the data to an optimum level of accuracy and using minimum
computational work. The applications of this process are credible in every
domain which deals with large data sets and high computational work. This study
uses Bayesian analysis for determination of minimum sample size of vibration
signals to be considered for fault diagnosis of a bearing using pre-defined
parameters such as the inverse standard probability and the acceptable margin
of error. Thus an analytical formula for sample size determination is
introduced. The fault diagnosis of the bearing is done using a machine learning
approach using an entropy-based J48 algorithm. The following method will help
researchers involved in fault diagnosis to determine minimum sample size of
data for analysis for a good statistical stability and precision.
Interpreting social cues to generate credible affective reactions of virtual job interviewers
Authors: Hazael Jones, Nicolas Sabouret, Ionut Damian, Tobias Baur, Elisabeth André, Kaśka Porayska-Pomsta, Paola Rizzo
Date: 2014-02-20 00:00:00

In this paper we describe a mechanism of generating credible affective
reactions in a virtual recruiter during an interaction with a user. This is
done using communicative performance computation based on the behaviours of the
user as detected by a recognition module. The proposed software pipeline is
part of the TARDIS system which aims to aid young job seekers in acquiring job
interview related social skills. In this context, our system enables the
virtual recruiter to realistically adapt and react to the user in real-time.
The Complexity of Repairing, Adjusting, and Aggregating of Extensions in Abstract Argumentation
Authors: Eun Jung Kim, Sebastian Ordyniak, Stefan Szeider
Date: 2014-02-25 00:00:00
Tasks: Abstract Argumentation

We study the computational complexity of problems that arise in abstract
argumentation in the context of dynamic argumentation, minimal change, and
aggregation. In particular, we consider the following problems where always an
argumentation framework F and a small positive integer k are given.
  - The Repair problem asks whether a given set of arguments can be modified
into an extension by at most k elementary changes (i.e., the extension is of
distance k from the given set).
  - The Adjust problem asks whether a given extension can be modified by at
most k elementary changes into an extension that contains a specified argument.
  - The Center problem asks whether, given two extensions of distance k,
whether there is a "center" extension that is a distance at most (k-1) from
both given extensions.
  We study these problems in the framework of parameterized complexity, and
take the distance k as the parameter. Our results covers several different
semantics, including admissible, complete, preferred, semi-stable and stable
semantics.
Inductive Logic Boosting
Authors: Wang-Zhou Dai, Zhi-Hua Zhou
Date: 2014-02-25 00:00:00
Tasks: Inductive logic programming, Relational Reasoning

Recent years have seen a surge of interest in Probabilistic Logic Programming
(PLP) and Statistical Relational Learning (SRL) models that combine logic with
probabilities. Structure learning of these systems is an intersection area of
Inductive Logic Programming (ILP) and statistical learning (SL). However, ILP
cannot deal with probabilities, SL cannot model relational hypothesis. The
biggest challenge of integrating these two machine learning frameworks is how
to estimate the probability of a logic clause only from the observation of
grounded logic atoms. Many current methods models a joint probability by
representing clause as graphical model and literals as vertices in it. This
model is still too complicate and only can be approximate by pseudo-likelihood.
We propose Inductive Logic Boosting framework to transform the relational
dataset into a feature-based dataset, induces logic rules by boosting Problog
Rule Trees and relaxes the independence constraint of pseudo-likelihood.
Experimental evaluation on benchmark datasets demonstrates that the AUC-PR and
AUC-ROC value of ILP learned rules are higher than current state-of-the-art SRL
methods.
Machine Learning at Scale
Authors: Sergei Izrailev, Jeremy M. Stanley
Date: 2014-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Management

It takes skill to build a meaningful predictive model even with the abundance
of implementations of modern machine learning algorithms and readily available
computing resources. Building a model becomes challenging if hundreds of
terabytes of data need to be processed to produce the training data set. In a
digital advertising technology setting, we are faced with the need to build
thousands of such models that predict user behavior and power advertising
campaigns in a 24/7 chaotic real-time production environment. As data
scientists, we also have to convince other internal departments critical to
implementation success, our management, and our customers that our machine
learning system works. In this paper, we present the details of the design and
implementation of an automated, robust machine learning platform that impacts
billions of advertising impressions monthly. This platform enables us to
continuously optimize thousands of campaigns over hundreds of millions of
users, on multiple continents, against varying performance objectives.
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit
Authors: Chong Zhang, Yu Zhao, Jochen Triesch, Bertram E. Shi
Date: 2014-02-14 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

We extend the framework of efficient coding, which has been used to model the
development of sensory processing in isolation, to model the development of the
perception/action cycle. Our extension combines sparse coding and reinforcement
learning so that sensory processing and behavior co-develop to optimize a
shared intrinsic motivational signal: the fidelity of the neural encoding of
the sensory input under resource constraints. Applying this framework to a
model system consisting of an active eye behaving in a time varying
environment, we find that this generic principle leads to the simultaneous
development of both smooth pursuit behavior and model neurons whose properties
are similar to those of primary visual cortical neurons selective for different
directions of visual motion. We suggest that this general principle may form
the basis for a unified and integrated explanation of many perception/action
loops.
A DCT Approximation for Image Compression
Authors: R. J. Cintra, F. M. Bayer
Date: 2014-02-25 00:00:00
Tasks: Image Compression

An orthogonal approximation for the 8-point discrete cosine transform (DCT)
is introduced. The proposed transformation matrix contains only zeros and ones;
multiplications and bit-shift operations are absent. Close spectral behavior
relative to the DCT was adopted as design criterion. The proposed algorithm is
superior to the signed discrete cosine transform. It could also outperform
state-of-the-art algorithms in low and high image compression scenarios,
exhibiting at the same time a comparable computational complexity.
Algorithms for multi-armed bandit problems
Authors: Volodymyr Kuleshov, Doina Precup
Date: 2014-02-25 00:00:00
Tasks: Multi-Armed Bandits

Although many algorithms for the multi-armed bandit problem are
well-understood theoretically, empirical confirmation of their effectiveness is
generally scarce. This paper presents a thorough empirical study of the most
popular multi-armed bandit algorithms. Three important observations can be made
from our results. Firstly, simple heuristics such as epsilon-greedy and
Boltzmann exploration outperform theoretically sound algorithms on most
settings by a significant margin. Secondly, the performance of most algorithms
varies dramatically with the parameters of the bandit problem. Our study
identifies for each algorithm the settings where it performs well, and the
settings where it performs poorly. Thirdly, the algorithms' performance
relative each to other is affected only by the number of bandit arms and the
variance of the rewards. This finding may guide the design of subsequent
empirical evaluations. In the second part of the paper, we turn our attention
to an important area of application of bandit algorithms: clinical trials.
Although the design of clinical trials has been one of the principal practical
problems motivating research on multi-armed bandits, bandit algorithms have
never been evaluated as potential treatment allocation strategies. Using data
from a real study, we simulate the outcome that a 2001-2002 clinical trial
would have had if bandit algorithms had been used to allocate patients to
treatments. We find that an adaptive trial would have successfully treated at
least 50% more patients, while significantly reducing the number of adverse
effects and increasing patient retention. At the end of the trial, the best
treatment could have still been identified with a high level of statistical
confidence. Our findings demonstrate that bandit algorithms are attractive
alternatives to current adaptive treatment allocation strategies.
Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps
Authors: Pengfei Wan, Gene Cheung, Philip A. Chou, Dinei Florencio, Cha Zhang, Oscar C. Au
Date: 2014-02-25 00:00:00
Tasks: Quantization

In texture-plus-depth representation of a 3D scene, depth maps from different
camera viewpoints are typically lossily compressed via the classical transform
coding / coefficient quantization paradigm. In this paper we propose to reduce
distortion of the decoded depth maps due to quantization. The key observation
is that depth maps from different viewpoints constitute multiple descriptions
(MD) of the same 3D scene. Considering the MD jointly, we perform a POCS-like
iterative procedure to project a reconstructed signal from one depth map to the
other and back, so that the converged depth maps have higher precision than the
original quantized versions.
Open science in machine learning
Authors: Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong
Date: 2014-02-24 00:00:00
Tasks: BIG-bench Machine Learning

We present OpenML and mldata, open science platforms that provides easy
access to machine learning data, software and results to encourage further
study and application. They go beyond the more traditional repositories for
data sets and software packages in that they allow researchers to also easily
share the results they obtained in experiments and to compare their solutions
with those of others.
A Testbed for Cross-Dataset Analysis
Authors: Tatiana Tommasi, Tinne Tuytelaars, Barbara Caputo
Date: 2014-02-24 00:00:00

Since its beginning visual recognition research has tried to capture the huge
variability of the visual world in several image collections. The number of
available datasets is still progressively growing together with the amount of
samples per object category. However, this trend does not correspond directly
to an increasing in the generalization capabilities of the developed
recognition systems. Each collection tends to have its specific characteristics
and to cover just some aspects of the visual world: these biases often narrow
the effect of the methods defined and tested separately over each image set.
Our work makes a first step towards the analysis of the dataset bias problem on
a large scale. We organize twelve existing databases in a unique corpus and we
present the visual community with a useful feature repository for future
research.
Near Optimal Bayesian Active Learning for Decision Making
Authors: Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, J. Andrew Bagnell, Siddhartha Srinivasa
Date: 2014-02-24 00:00:00
Tasks: Active Learning, Decision Making, Experimental Design

How should we gather information to make effective decisions? We address
Bayesian active learning and experimental design problems, where we
sequentially select tests to reduce uncertainty about a set of hypotheses.
Instead of minimizing uncertainty per se, we consider a set of overlapping
decision regions of these hypotheses. Our goal is to drive uncertainty into a
single decision region as quickly as possible.
  We identify necessary and sufficient conditions for correctly identifying a
decision region that contains all hypotheses consistent with observations. We
develop a novel Hyperedge Cutting (HEC) algorithm for this problem, and prove
that is competitive with the intractable optimal policy. Our efficient
implementation of the algorithm relies on computing subsets of the complete
homogeneous symmetric polynomials. Finally, we demonstrate its effectiveness on
two practical applications: approximate comparison-based learning and active
localization using a robot manipulator.
A Novel Face Recognition Method using Nearest Line Projection
Authors: Huanguo Zhang, Sha Lv, Wei Li, Xun Qu
Date: 2014-02-24 00:00:00
Tasks: Face Recognition

Face recognition is a popular application of pat- tern recognition methods, and it faces challenging problems including illumination, expression, and pose. The most popular way is to learn the subspaces of the face images so that it could be project to another discriminant space where images of different persons can be separated. In this paper, a nearest line projection algorithm is developed to represent the face images for face recognition. Instead of projecting an image to its nearest image, we try to project it to its nearest line spanned by two different face images. The subspaces are learned so that each face image to its nearest line is minimized. We evaluated the proposed algorithm on some benchmark face image database, and also compared it to some other image projection algorithms. The experiment results showed that the proposed algorithm outperforms other ones.
Learning directed acyclic graphs based on sparsest permutations
Authors: Garvesh Raskutti, Caroline Uhler
Date: 2013-07-01 00:00:00

We consider the problem of learning a Bayesian network or directed acyclic graph (DAG) model from observational data. A number of constraint-based, score-based and hybrid algorithms have been developed for this purpose. For constraint-based methods, statistical consistency guarantees typically rely on the faithfulness assumption, which has been show to be restrictive especially for graphs with cycles in the skeleton. However, there is only limited work on consistency guarantees for score-based and hybrid algorithms and it has been unclear whether consistency guarantees can be proven under weaker conditions than the faithfulness assumption. In this paper, we propose the sparsest permutation (SP) algorithm. This algorithm is based on finding the causal ordering of the variables that yields the sparsest DAG. We prove that this new score-based method is consistent under strictly weaker conditions than the faithfulness assumption. We also demonstrate through simulations on small DAGs that the SP algorithm compares favorably to the constraint-based PC and SGS algorithms as well as the score-based Greedy Equivalence Search and hybrid Max-Min Hill-Climbing method. In the Gaussian setting, we prove that our algorithm boils down to finding the permutation of the variables with sparsest Cholesky decomposition for the inverse covariance matrix. Using this connection, we show that in the oracle setting, where the true covariance matrix is known, the SP algorithm is in fact equivalent to $\ell_0$-penalized maximum likelihood estimation.
Automatic Estimation of Live Coffee Leaf Infection based on Image Processing Techniques
Authors: Eric Hitimana, Oubong Gwun
Date: 2014-02-24 00:00:00
Tasks: Image Segmentation, Management, Segmentation, Semantic Segmentation

Image segmentation is the most challenging issue in computer vision
applications. And most difficulties for crops management in agriculture are the
lack of appropriate methods for detecting the leaf damage for pests treatment.
In this paper we proposed an automatic method for leaf damage detection and
severity estimation of coffee leaf by avoiding defoliation. After enhancing the
contrast of the original image using LUT based gamma correction, the image is
processed to remove the background, and the output leaf is clustered using
Fuzzy c-means segmentation in V channel of YUV color space to maximize all leaf
damage detection, and finally, the severity of leaf is estimated in terms of
ratio for leaf pixel distribution between the normal and the detected leaf
damage. The results in each proposed method was compared to the current
researches and the accuracy is obvious either in the background removal or
damage detection.
Sparse phase retrieval via group-sparse optimization
Authors: Fabien Lauer, Henrik Ohlsson
Date: 2014-02-24 00:00:00
Tasks: Retrieval

This paper deals with sparse phase retrieval, i.e., the problem of estimating
a vector from quadratic measurements under the assumption that few components
are nonzero. In particular, we consider the problem of finding the sparsest
vector consistent with the measurements and reformulate it as a group-sparse
optimization problem with linear constraints. Then, we analyze the convex
relaxation of the latter based on the minimization of a block l1-norm and show
various exact recovery and stability results in the real and complex cases.
Invariance to circular shifts and reflections are also discussed for real
vectors measured via complex matrices.
No more meta-parameter tuning in unsupervised sparse feature learning
Authors: Adriana Romero, Petia Radeva, Carlo Gatta
Date: 2014-02-24 00:00:00
Tasks: Image Classification

We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised
feature learning algorithm, which exploits a new way of optimizing for
sparsity. Experiments on STL-10 show that the method presents state-of-the-art
performance and provides discriminative features that generalize well.
Bandits with concave rewards and convex knapsacks
Authors: Shipra Agrawal, Nikhil R. Devanur
Date: 2014-02-24 00:00:00

In this paper, we consider a very general model for exploration-exploitation
tradeoff which allows arbitrary concave rewards and convex constraints on the
decisions across time, in addition to the customary limitation on the time
horizon. This model subsumes the classic multi-armed bandit (MAB) model, and
the Bandits with Knapsacks (BwK) model of Badanidiyuru et al.[2013]. We also
consider an extension of this model to allow linear contexts, similar to the
linear contextual extension of the MAB model. We demonstrate that a natural and
simple extension of the UCB family of algorithms for MAB provides a polynomial
time algorithm that has near-optimal regret guarantees for this substantially
more general model, and matches the bounds provided by Badanidiyuru et
al.[2013] for the special case of BwK, which is quite surprising. We also
provide computationally more efficient algorithms by establishing interesting
connections between this problem and other well studied problems/algorithms
such as the Blackwell approachability problem, online convex optimization, and
the Frank-Wolfe technique for convex optimization. We give examples of several
concrete applications, where this more general model of bandits allows for
richer and/or more efficient formulations of the problem.
Machine Learning Methods in the Computational Biology of Cancer
Authors: Mathukumalli Vidyasagar
Date: 2014-02-24 00:00:00
Tasks: BIG-bench Machine Learning, Classification, compressed sensing, feature selection, General Classification, regression

The objectives of this "perspective" paper are to review some recent advances
in sparse feature selection for regression and classification, as well as
compressed sensing, and to discuss how these might be used to develop tools to
advance personalized cancer therapy. As an illustration of the possibilities, a
new algorithm for sparse regression is presented, and is applied to predict the
time to tumor recurrence in ovarian cancer. A new algorithm for sparse feature
selection in classification problems is presented, and its validation in
endometrial cancer is briefly discussed. Some open problems are also presented.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Authors: Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann Lecun
Date: 2013-12-21 00:00:00
Tasks: General Classification, Image Classification, Object Detection, Object Recognition

We present an integrated framework for using Convolutional Networks for
classification, localization and detection. We show how a multiscale and
sliding window approach can be efficiently implemented within a ConvNet. We
also introduce a novel deep learning approach to localization by learning to
predict object boundaries. Bounding boxes are then accumulated rather than
suppressed in order to increase detection confidence. We show that different
tasks can be learned simultaneously using a single shared network. This
integrated framework is the winner of the localization task of the ImageNet
Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very
competitive results for the detection and classifications tasks. In
post-competition work, we establish a new state of the art for the detection
task. Finally, we release a feature extractor from our best model called
OverFeat.
The Cerebellum: New Computational Model that Reveals its Primary Function to Calculate Multibody Dynamics Conform to Lagrange-Euler Formulation
Authors: Lavdim Kurtaj, Ilir Limani, Vjosa Shatri, Avni Skeja
Date: 2014-02-24 00:00:00
Tasks: Position

Cerebellum is part of the brain that occupies only 10% of the brain volume,
but it contains about 80% of total number of brain neurons. New cerebellar
function model is developed that sets cerebellar circuits in context of
multibody dynamics model computations, as important step in controlling balance
and movement coordination, functions performed by two oldest parts of the
cerebellum. Model gives new functional interpretation for granule cells-Golgi
cell circuit, including distinct function for upper and lower Golgi cell
dendritc trees, and resolves issue of sharing Granule cells between Purkinje
cells. Sets new function for basket cells, and for stellate cells according to
position in molecular layer. New model enables easily and direct integration of
sensory information from vestibular system and cutaneous mechanoreceptors, for
balance, movement and interaction with environments. Model gives explanation of
Purkinje cells convergence on deep-cerebellar nuclei.
Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
Authors: Andrew Gordon Wilson, Yuting Wu, Daniel J. Holland, Sebastian Nowozin, Mick D. Mantle, Lynn F. Gladden, Andrew Blake
Date: 2014-02-14 00:00:00
Tasks: Bayesian Inference, Sensitivity

Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic
properties of atomic nuclei to discover the structure, reaction state and
chemical environment of molecules. We propose a probabilistic generative model
and inference procedures for NMR spectroscopy. Specifically, we use a weighted
sum of trigonometric functions undergoing exponential decay to model free
induction decay (FID) signals. We discuss the challenges in estimating the
components of this general model -- amplitudes, phase shifts, frequencies,
decay rates, and noise variances -- and offer practical solutions. We compare
with conventional Fourier transform spectroscopy for estimating the relative
concentrations of chemicals in a mixture, using synthetic and experimentally
acquired FID signals. We find the proposed model is particularly robust to low
signal to noise ratios (SNR), and overlapping peaks in the Fourier transform of
the FID, enabling accurate predictions (e.g., 1% sensitivity at low SNR) which
are not possible with conventional spectroscopy (5% sensitivity).