pwc-archive/papers-with-abstracts huggingface dataset:
Face Recognition: Perspectives from the Real-World
Authors: Bappaditya Mandal
Date: 2016-02-09 00:00:00
Tasks: Face Recognition

In this paper, we analyze some of our real-world deployment of face
recognition (FR) systems for various applications and discuss the gaps between
expectations of the user and what the system can deliver. We evaluate some of
our proposed algorithms with ad-hoc modifications for applications such as FR
on wearable devices (like Google Glass), monitoring of elderly people in senior
citizens centers, FR of children in child care centers and face matching
between a scanned IC/passport face image and a few live webcam images for
automatic hotel/resort checkouts. We describe each of these applications, the
challenges involved and proposed solutions. Since FR is intuitive in nature and
we human beings use it for interactions with the outside world, people have
high expectations of its performance in real-world scenarios. However, we
analyze and discuss here that it is not the case, machine recognition of faces
for each of these applications poses unique challenges and demands specific
research components so as to adapt in the actual sites.
Porting HTM Models to the Heidelberg Neuromorphic Computing Platform
Authors: Sebastian Billaudelle, Subutai Ahmad
Date: 2015-05-08 00:00:00

Hierarchical Temporal Memory (HTM) is a computational theory of machine
intelligence based on a detailed study of the neocortex. The Heidelberg
Neuromorphic Computing Platform, developed as part of the Human Brain Project
(HBP), is a mixed-signal (analog and digital) large-scale platform for modeling
networks of spiking neurons. In this paper we present the first effort in
porting HTM networks to this platform. We describe a framework for simulating
key HTM operations using spiking network models. We then describe specific
spatial pooling and temporal memory implementations, as well as simulations
demonstrating that the fundamental properties are maintained. We discuss issues
in implementing the full set of plasticity rules using Spike-Timing Dependent
Plasticity (STDP), and rough place and route calculations. Although further
work is required, our initial studies indicate that it should be possible to
run large-scale HTM networks (including plasticity rules) efficiently on the
Heidelberg platform. More generally the exercise of porting high level HTM
algorithms to biophysical neuron models promises to be a fruitful area of
investigation for future studies.
Spoofing detection under noisy conditions: a preliminary investigation and an initial database
Authors: Xiaohai Tian, Zhizheng Wu, Xiong Xiao, Eng Siong Chng, Haizhou Li
Date: 2016-02-09 00:00:00
Tasks: Speaker Verification

Spoofing detection for automatic speaker verification (ASV), which is to
discriminate between live speech and attacks, has received increasing
attentions recently. However, all the previous studies have been done on the
clean data without significant additive noise. To simulate the real-life
scenarios, we perform a preliminary investigation of spoofing detection under
additive noisy conditions, and also describe an initial database for this task.
The noisy database is based on the ASVspoof challenge 2015 database and
generated by artificially adding background noises at different signal-to-noise
ratios (SNRs). Five different additive noises are included. Our preliminary
results show that using the model trained from clean data, the system
performance degrades significantly in noisy conditions. Phase-based feature is
more noise robust than magnitude-based features. And the systems perform
significantly differ under different noise scenarios.
Challenges of Integrating A Priori Information Efficiently in the Discovery of Spatio-Temporal Objects in Large Databases
Authors: Benjamin Schott, Johannes Stegmaier, Masanari Takamiya, Ralf Mikut
Date: 2016-02-09 00:00:00
Tasks: Object

Using the knowledge discovery framework, it is possible to explore object
databases and extract groups of objects with highly heterogeneous movement
behavior by efficiently integrating a priori knowledge through interacting with
the framework. The whole process is modular expandable and is therefore
adaptive to any problem formulation. Further, the flexible use of different
information allocation processes reveal a great potential to efficiently
incorporate the a priori knowledge of different users in different ways.
Therefore, the stepwise knowledge discovery process embedded in the knowledge
discovery framework is described in detail to point out the flexibility of such
a system incorporating object databases from different applications. The
described framework can be used to gain knowledge out of object databases in
many different fields. This knowledge can be used to gain further insights and
improve the understanding of underlying phenomena. The functionality of the
proposed framework is exemplarily demonstrated using a benchmark database based
on real biological object data.
Empirical Bayes Estimation for the Stochastic Blockmodel
Authors: Shakira Suwan, Dominic S. Lee, Runze Tang, Daniel L. Sussman, Minh Tang, Carey E. Priebe
Date: 2014-05-23 00:00:00
Tasks: Position

Inference for the stochastic blockmodel is currently of burgeoning interest
in the statistical community, as well as in various application domains as
diverse as social networks, citation networks, brain connectivity networks
(connectomics), etc. Recent theoretical developments have shown that spectral
embedding of graphs yields tractable distributional results; in particular, a
random dot product latent position graph formulation of the stochastic
blockmodel informs a mixture of normal distributions for the adjacency spectral
embedding. We employ this new theory to provide an empirical Bayes methodology
for estimation of block memberships of vertices in a random graph drawn from
the stochastic blockmodel, and demonstrate its practical utility. The posterior
inference is conducted using a Metropolis-within-Gibbs algorithm. The theory
and methods are illustrated through Monte Carlo simulation studies, both within
the stochastic blockmodel and beyond, and experimental results on a Wikipedia
data set are presented.
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Authors: Alexey Dosovitskiy, Thomas Brox
Date: 2016-02-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Image Generation

Image-generating machine learning models are typically trained with loss
functions based on distance in the image space. This often leads to
over-smoothed results. We propose a class of loss functions, which we call deep
perceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of
computing distances in the image space, we compute distances between image
features extracted by deep neural networks. This metric better reflects
perceptually similarity of images and thus leads to better results. We show
three applications: autoencoder training, a modification of a variational
autoencoder, and inversion of deep convolutional networks. In all cases, the
generated images look sharp and resemble natural images.
Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data
Authors: Ferhat Özgür Çatak
Date: 2016-02-09 00:00:00
Tasks: Classification, General Classification, Privacy Preserving

Especially in the Big Data era, the usage of different classification methods
is increasing day by day. The success of these classification methods depends
on the effectiveness of learning methods. Extreme learning machine (ELM)
classification algorithm is a relatively new learning method built on
feed-forward neural-network. ELM classification algorithm is a simple and fast
method that can create a model from high-dimensional data sets. Traditional ELM
learning algorithm implicitly assumes complete access to whole data set. This
is a major privacy concern in most of cases. Sharing of private data (i.e.
medical records) is prevented because of security concerns. In this research,
we propose an efficient and secure privacy-preserving learning algorithm for
ELM classification over data that is vertically partitioned among several
parties. The new learning method preserves the privacy on numerical attributes,
builds a classification model without sharing private data without disclosing
the data of each party to others.
Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM
Authors: Ferhat Özgür Çatak
Date: 2016-02-09 00:00:00
Tasks: General Classification

In machine learning area, as the number of labeled input samples becomes very
large, it is very difficult to build a classification model because of input
data set is not fit in a memory in training phase of the algorithm, therefore,
it is necessary to utilize data partitioning to handle overall data set.
Bagging and boosting based data partitioning methods have been broadly used in
data mining and pattern recognition area. Both of these methods have shown a
great possibility for improving classification model performance. This study is
concerned with the analysis of data set partitioning with noise removal and its
impact on the performance of multiple classifier models. In this study, we
propose noise filtering preprocessing at each data set partition to increment
classifier model performance. We applied Gini impurity approach to find the
best split percentage of noise filter ratio. The filtered sub data set is then
used to train individual ensemble models.
Classification with Boosting of Extreme Learning Machine Over Arbitrarily Partitioned Data
Authors: Ferhat Özgür Çatak
Date: 2016-02-09 00:00:00
Tasks: General Classification

Machine learning based computational intelligence methods are widely used to
analyze large scale data sets in this age of big data. Extracting useful
predictive modeling from these types of data sets is a challenging problem due
to their high complexity. Analyzing large amount of streaming data that can be
leveraged to derive business value is another complex problem to solve. With
high levels of data availability (\textit{i.e. Big Data}) automatic
classification of them has become an important and complex task. Hence, we
explore the power of applying MapReduce based Distributed AdaBoosting of
Extreme Learning Machine (ELM) to build a predictive bag of classification
models. Accordingly, (i) data set ensembles are created; (ii) ELM algorithm is
used to build weak learners (classifier functions); and (iii) builds a strong
learner from a set of weak learners. We applied this training model to the
benchmark knowledge discovery and data mining data sets.
Joint Defogging and Demosaicking
Authors: Y. J. Lee, K. Hirakawa, T. Q. Nguyen
Date: 2016-02-09 00:00:00
Tasks: Demosaicking

Image defogging is a technique used extensively for enhancing visual quality
of images in bad weather condition. Even though defogging algorithms have been
well studied, defogging performance is degraded by demosaicking artifacts and
sensor noise amplification in distant scenes. In order to improve visual
quality of restored images, we propose a novel approach to perform defogging
and demosaicking simultaneously. We conclude that better defogging performance
with fewer artifacts can be achieved when a defogging algorithm is combined
with a demosaicking algorithm simultaneously. We also demonstrate that the
proposed joint algorithm has the benefit of suppressing noise amplification in
distant scene. In addition, we validate our theoretical analysis and
observations for both synthesized datasets with ground truth fog-free images
and natural scene datasets captured in a raw format.
Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents
Authors: Jing Lu, Jan Egger, Andreas Wimmer, Stefan Großkopf, Bernd Freisleben
Date: 2016-02-09 00:00:00
Tasks: Anatomy, Segmentation

In this paper we present an efficient algorithm for the segmentation of the
inner and outer boundary of thoratic and abdominal aortic aneurysms (TAA & AAA)
in computed tomography angiography (CTA) acquisitions. The aneurysm
segmentation includes two steps: first, the inner boundary is segmented based
on a grey level model with two thresholds; then, an adapted active contour
model approach is applied to the more complicated outer boundary segmentation,
with its initialization based on the available inner boundary segmentation. An
opacity image, which aims at enhancing important features while reducing
spurious structures, is calculated from the CTA images and employed to guide
the deformation of the model. In addition, the active contour model is extended
by a constraint force that prevents intersections of the inner and outer
boundary and keeps the outer boundary at a distance, given by the thrombus
thickness, to the inner boundary. Based upon the segmentation results, we can
measure the aneurysm size at each centerline point on the centerline orthogonal
multiplanar reformatting (MPR) plane. Furthermore, a 3D TAA or AAA model is
reconstructed from the set of segmented contours, and the presence of endoleaks
is detected and highlighted. The implemented method has been evaluated on nine
clinical CTA data sets with variations in anatomy and location of the pathology
and has shown promising results.
Approximate Probabilistic Inference via Word-Level Counting
Authors: Supratik Chakraborty, Kuldeep S. Meel, Rakesh Mistry, Moshe Y. Vardi
Date: 2015-11-24 00:00:00

Hashing-based model counting has emerged as a promising approach for
large-scale probabilistic inference on graphical models. A key component of
these techniques is the use of xor-based 2-universal hash functions that
operate over Boolean domains. Many counting problems arising in probabilistic
inference are, however, naturally encoded over finite discrete domains.
Techniques based on bit-level (or Boolean) hash functions require these
problems to be propositionalized, making it impossible to leverage the
remarkable progress made in SMT (Satisfiability Modulo Theory) solvers that can
reason directly over words (or bit-vectors). In this work, we present the first
approximate model counter that uses word-level hashing functions, and can
directly leverage the power of sophisticated SMT solvers. Empirical evaluation
over an extensive suite of benchmarks demonstrates the promise of the approach.
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks
Authors: Babak Saleh, Ahmed Elgammal, Jacob Feldman
Date: 2016-02-09 00:00:00
Tasks: General Classification, Object Recognition

Deep artificial neural networks have made remarkable progress in different
tasks in the field of computer vision. However, the empirical analysis of these
models and investigation of their failure cases has received attention
recently. In this work, we show that deep learning models cannot generalize to
atypical images that are substantially different from training images. This is
in contrast to the superior generalization ability of the visual system in the
human brain. We focus on Convolutional Neural Networks (CNN) as the
state-of-the-art models in object recognition and classification; investigate
this problem in more detail, and hypothesize that training CNN models suffer
from unstructured loss minimization. We propose computational models to improve
the generalization capacity of CNNs by considering how typical a training image
looks like. By conducting an extensive set of experiments we show that
involving a typicality measure can improve the classification results on a new
set of images by a large margin. More importantly, this significant improvement
is achieved without fine-tuning the CNN model on the target image set.
A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation
Authors: Shayan Poursoltan, Frank Neumann
Date: 2016-02-09 00:00:00

With this paper, we contribute to the growing research area of feature-based
analysis of bio-inspired computing. In this research area, problem instances
are classified according to different features of the underlying problem in
terms of their difficulty of being solved by a particular algorithm. We
investigate the impact of different sets of evolved instances for building
prediction models in the area of algorithm selection. Building on the work of
Poursoltan and Neumann [11,10], we consider how evolved instances can be used
to predict the best performing algorithm for constrained continuous
optimisation from a set of bio-inspired computing methods, namely high
performing variants of differential evolution, particle swarm optimization, and
evolution strategies. Our experimental results show that instances evolved with
a multi-objective approach in combination with random instances of the
underlying problem allow to build a model that accurately predicts the best
performing algorithm for a wide range of problem instances.
Compliance-Aware Bandits
Authors: Nicolás Della Penna, Mark D. Reid, David Balduzzi
Date: 2016-02-09 00:00:00

Motivated by clinical trials, we study bandits with observable
non-compliance. At each step, the learner chooses an arm, after, instead of
observing only the reward, it also observes the action that took place. We show
that such noncompliance can be helpful or hurtful to the learner in general.
Unfortunately, naively incorporating compliance information into bandit
algorithms loses guarantees on sublinear regret. We present hybrid algorithms
that maintain regret bounds up to a multiplicative factor and can incorporate
compliance information. Simulations based on real data from the International
Stoke Trial show the practical potential of these algorithms.
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Authors: Bo Tang, Steven Kay, Haibo He
Date: 2016-02-09 00:00:00
Tasks: feature selection, General Classification, Multi-class Classification, Text Classification, Two-sample testing

Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information
measures: Kullback-Leibler divergence and Jeffreys divergence for binary
hypothesis testing, and analyze their asymptotic properties relating to type I
and type II errors of a Bayesian classifier. We then introduce a new divergence
measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure
multi-distribution divergence for multi-class classification. Based on the
JMH-divergence, we develop two efficient feature selection methods, termed
maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization.
The promising results of extensive experiments demonstrate the effectiveness of
the proposed approaches.
Collaborative filtering via sparse Markov random fields
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2016-02-09 00:00:00
Tasks: Collaborative Filtering, Movie Recommendation, Recommendation Systems

Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov random fields (MRF). We address the open problem of structure learning
and introduce a sparsity-inducing algorithm to automatically estimate the
interaction structures between users and between items. Item-item and user-user
correlation networks are obtained as a by-product. Large-scale experiments on
movie recommendation and date matching datasets demonstrate the power of the
proposed method.
Machine Learning Model of the Swift/BAT Trigger Algorithm for Long GRB Population Studies
Authors: Philip B Graff, Amy Y Lien, John G Baker, Takanori Sakamoto
Date: 2015-09-03 00:00:00
Tasks: BIG-bench Machine Learning, parameter estimation

To draw inferences about gamma-ray burst (GRB) source populations based on
Swift observations, it is essential to understand the detection efficiency of
the Swift burst alert telescope (BAT). This study considers the problem of
modeling the Swift/BAT triggering algorithm for long GRBs, a computationally
expensive procedure, and models it using machine learning algorithms. A large
sample of simulated GRBs from Lien 2014 is used to train various models: random
forests, boosted decision trees (with AdaBoost), support vector machines, and
artificial neural networks. The best models have accuracies of $\gtrsim97\%$
($\lesssim 3\%$ error), which is a significant improvement on a cut in GRB flux
which has an accuracy of $89.6\%$ ($10.4\%$ error). These models are then used
to measure the detection efficiency of Swift as a function of redshift $z$,
which is used to perform Bayesian parameter estimation on the GRB rate
distribution. We find a local GRB rate density of $n_0 \sim
0.48^{+0.41}_{-0.23} \ {\rm Gpc}^{-3} {\rm yr}^{-1}$ with power-law indices of
$n_1 \sim 1.7^{+0.6}_{-0.5}$ and $n_2 \sim -5.9^{+5.7}_{-0.1}$ for GRBs above
and below a break point of $z_1 \sim 6.8^{+2.8}_{-3.2}$. This methodology is
able to improve upon earlier studies by more accurately modeling Swift
detection and using this for fully Bayesian model fitting. The code used in
this is analysis is publicly available online
(https://github.com/PBGraff/SwiftGRB_PEanalysis).
Poor starting points in machine learning
Authors: Mark Tygert
Date: 2016-02-09 00:00:00
Tasks: BIG-bench Machine Learning

Poor (even random) starting points for learning/training/optimization are
common in machine learning. In many settings, the method of Robbins and Monro
(online stochastic gradient descent) is known to be optimal for good starting
points, but may not be optimal for poor starting points -- indeed, for poor
starting points Nesterov acceleration can help during the initial iterations,
even though Nesterov methods not designed for stochastic approximation could
hurt during later iterations. The common practice of training with nontrivial
minibatches enhances the advantage of Nesterov acceleration.
Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
Authors: Xiyang Dai, Sameh Khamis, Yangmuzi Zhang, Larry S. Davis
Date: 2016-02-09 00:00:00
Tasks: BIG-bench Machine Learning

Sparse representations have been successfully applied to signal processing,
computer vision and machine learning. Currently there is a trend to learn
sparse models directly on structure data, such as region covariance. However,
such methods when combined with region covariance often require complex
computation. We present an approach to transform a structured sparse model
learning problem to a traditional vectorized sparse modeling problem by
constructing a Euclidean space representation for region covariance matrices.
Our new representation has multiple advantages. Experiments on several vision
tasks demonstrate competitive performance with the state-of-the-art methods.