pwc-archive/papers-with-abstracts huggingface dataset:
Freehand Sketch Recognition Using Deep Features
Authors: Ravi Kiran Sarvadevabhatla, R. Venkatesh Babu
Date: 2015-02-01 00:00:00
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval, Sketch Recognition

Freehand sketches often contain sparse visual detail. In spite of the
sparsity, they are easily and consistently recognized by humans across
cultures, languages and age groups. Therefore, analyzing such sparse sketches
can aid our understanding of the neuro-cognitive processes involved in visual
representation and recognition. In the recent past, Convolutional Neural
Networks (CNNs) have emerged as a powerful framework for feature representation
and recognition for a variety of image domains. However, the domain of sketch
images has not been explored. This paper introduces a freehand sketch
recognition framework based on "deep" features extracted from CNNs. We use two
popular CNNs for our experiments -- Imagenet CNN and a modified version of
LeNet CNN. We evaluate our recognition framework on a publicly available
benchmark database containing thousands of freehand sketches depicting everyday
objects. Our results are an improvement over the existing state-of-the-art
accuracies by 3% - 11%. The effectiveness and relative compactness of our deep
features also make them an ideal candidate for related problems such as
sketch-based image retrieval. In addition, we provide a preliminary glimpse of
how such features can help identify crucial attributes (e.g. object-parts) of
the sketched objects.
Learning Local Invariant Mahalanobis Distances
Authors: Ethan Fetaya, Shimon Ullman
Date: 2015-02-04 00:00:00
Tasks: BIG-bench Machine Learning, Translation

For many tasks and data types, there are natural transformations to which the
data should be invariant or insensitive. For instance, in visual recognition,
natural images should be insensitive to rotation and translation. This
requirement and its implications have been important in many machine learning
applications, and tolerance for image transformations was primarily achieved by
using robust feature vectors. In this paper we propose a novel and
computationally efficient way to learn a local Mahalanobis metric per datum,
and show how we can learn a local invariant metric to any transformation in
order to improve performance.
Constrained Extreme Learning Machines: A Study on Classification Cases
Authors: Wentao Zhu, Jun Miao, Laiyun Qing
Date: 2015-01-25 00:00:00
Tasks: Classification, General Classification

Extreme learning machine (ELM) is an extremely fast learning method and has a
powerful performance for pattern recognition tasks proven by enormous
researches and engineers. However, its good generalization ability is built on
large numbers of hidden neurons, which is not beneficial to real time response
in the test process. In this paper, we proposed new ways, named "constrained
extreme learning machines" (CELMs), to randomly select hidden neurons based on
sample distribution. Compared to completely random selection of hidden nodes in
ELM, the CELMs randomly select hidden nodes from the constrained vector space
containing some basic combinations of original sample vectors. The experimental
results show that the CELMs have better generalization ability than traditional
ELM, SVM and some other related methods. Additionally, the CELMs have a similar
fast learning speed as ELM.
The Noisy Power Method: A Meta Algorithm with Applications
Authors: Moritz Hardt, Eric Price
Date: 2013-11-11 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Matrix Completion, Privacy Preserving

We provide a new robust convergence analysis of the well-known power method
for computing the dominant singular vectors of a matrix that we call the noisy
power method. Our result characterizes the convergence behavior of the
algorithm when a significant amount noise is introduced after each
matrix-vector multiplication. The noisy power method can be seen as a
meta-algorithm that has recently found a number of important applications in a
broad range of machine learning problems including alternating minimization for
matrix completion, streaming principal component analysis (PCA), and
privacy-preserving spectral analysis. Our general analysis subsumes several
existing ad-hoc convergence bounds and resolves a number of open problems in
multiple applications including streaming PCA and privacy-preserving singular
vector computation.
Personalized Web Search
Authors: Li Zhou
Date: 2015-02-03 00:00:00
Tasks: Feature Engineering

Personalization is important for search engines to improve user experience.
Most of the existing work do pure feature engineering and extract a lot of
session-style features and then train a ranking model. Here we proposed a novel
way to model both long term and short term user behavior using Multi-armed
bandit algorithm. Our algorithm can generalize session information across users
well, and as an Explore-Exploit style algorithm, it can generalize to new urls
and new users well. Experiments show that our algorithm can improve performance
over the default ranking and outperforms several popular Multi-armed bandit
algorithms.
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
Authors: Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga, UK Arvind Rao, Ganesh Rao
Date: 2015-02-03 00:00:00
Tasks: Classification, Dictionary Learning, Diversity, General Classification, Histopathological Image Classification, image-classification, Image Classification

In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-specific features and present a low-complexity method for classification
and disease grading in histopathology. Essentially, our Discriminative
Feature-oriented Dictionary Learning (DFDL) method learns class-specific
features which are suitable for representing samples from the same class while
are poorly capable of representing samples from other classes. Experiments on
three challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian lung images provided by the Animal
Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor
images from The Cancer Genome Atlas (TCGA) database, show the significance of
DFDL model in a variety problems over state-of-the-art methods
Riemannian metrics for neural networks II: recurrent networks and learning symbolic data sequences
Authors: Yann Ollivier
Date: 2013-06-03 00:00:00

Recurrent neural networks are powerful models for sequential data, able to
represent complex dependencies in the sequence that simpler models such as
hidden Markov models cannot handle. Yet they are notoriously hard to train.
Here we introduce a training procedure using a gradient ascent in a Riemannian
metric: this produces an algorithm independent from design choices such as the
encoding of parameters and unit activities. This metric gradient ascent is
designed to have an algorithmic cost close to backpropagation through time for
sparsely connected networks. We use this procedure on gated leaky neural
networks (GLNNs), a variant of recurrent neural networks with an architecture
inspired by finite automata and an evolution equation inspired by
continuous-time networks. GLNNs trained with a Riemannian gradient are
demonstrated to effectively capture a variety of structures in synthetic
problems: basic block nesting as in context-free grammars (an important feature
of natural languages, but difficult to learn), intersections of multiple
independent Markov-type relations, or long-distance relationships such as the
distant-XOR problem. This method does not require adjusting the network
structure or initial parameters: the network used is a sparse random graph and
the initialization is identical for all problems considered.
Riemannian metrics for neural networks I: feedforward networks
Authors: Yann Ollivier
Date: 2013-03-04 00:00:00

We describe four algorithms for neural network training, each adapted to
different scalability constraints. These algorithms are mathematically
principled and invariant under a number of transformations in data and network
representation, from which performance is thus independent. These algorithms
are obtained from the setting of differential geometry, and are based on either
the natural gradient using the Fisher information matrix, or on Hessian
methods, scaled down in a specific way to allow for scalability while keeping
some of their key mathematical properties.
Classification of Hyperspectral Imagery on Embedded Grassmannians
Authors: Sofya Chepushtanova, Michael Kirby
Date: 2015-02-03 00:00:00
Tasks: Classification, General Classification

We propose an approach for capturing the signal variability in hyperspectral
imagery using the framework of the Grassmann manifold. Labeled points from each
class are sampled and used to form abstract points on the Grassmannian. The
resulting points on the Grassmannian have representations as orthonormal
matrices and as such do not reside in Euclidean space in the usual sense. There
are a variety of metrics which allow us to determine a distance matrices that
can be used to realize the Grassmannian as an embedding in Euclidean space. We
illustrate that we can achieve an approximately isometric embedding of the
Grassmann manifold using the chordal metric while this is not the case with
geodesic distances. However, non-isometric embeddings generated by using a
pseudometric on the Grassmannian lead to the best classification results. We
observe that as the dimension of the Grassmannian grows, the accuracy of the
classification grows to 100% on two illustrative examples. We also observe a
decrease in classification rates if the dimension of the points on the
Grassmannian is too large for the dimension of the Euclidean space. We use
sparse support vector machines to perform additional model reduction. The
resulting classifier selects a subset of dimensions of the embedding without
loss in classification performance.
Learning Planar Ising Models
Authors: Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli
Date: 2015-02-03 00:00:00

Inference and learning of graphical models are both well-studied problems in
statistics and machine learning that have found many applications in science
and engineering. However, exact inference is intractable in general graphical
models, which suggests the problem of seeking the best approximation to a
collection of random variables within some tractable family of graphical
models. In this paper, we focus on the class of planar Ising models, for which
exact inference is tractable using techniques of statistical physics. Based on
these techniques and recent methods for planarity testing and planar embedding,
we propose a simple greedy algorithm for learning the best planar Ising model
to approximate an arbitrary collection of binary random variables (possibly
from sample data). Given the set of all pairwise correlations among variables,
we select a planar graph and optimal planar Ising model defined on this graph
to best approximate that set of correlations. We demonstrate our method in
simulations and for the application of modeling senate voting records.
DeepID3: Face Recognition with Very Deep Neural Networks
Authors: Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang
Date: 2015-02-03 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, Object Recognition

The state-of-the-art of face recognition has been significantly advanced by
the emergence of deep learning. Very deep neural networks recently achieved
great success on general object recognition because of their superb learning
capacity. This motivates us to investigate their effectiveness on face
recognition. This paper proposes two very deep neural network architectures,
referred to as DeepID3, for face recognition. These two architectures are
rebuilt from stacked convolution and inception layers proposed in VGG net and
GoogLeNet to make them suitable to face recognition. Joint face
identification-verification supervisory signals are added to both intermediate
and final feature extraction layers during training. An ensemble of the
proposed two architectures achieves 99.53% LFW face verification accuracy and
96.0% LFW rank-1 face identification accuracy, respectively. A further
discussion of LFW face verification result is given in the end.
Optimistic Risk Perception in the Temporal Difference error Explains the Relation between Risk-taking, Gambling, Sensation-seeking and Low Fear
Authors: Joost Broekens, Tim Baarslag
Date: 2014-04-08 00:00:00
Tasks: Reinforcement Learning

Understanding the affective, cognitive and behavioural processes involved in
risk taking is essential for treatment and for setting environmental conditions
to limit damage. Using Temporal Difference Reinforcement Learning (TDRL) we
computationally investigated the effect of optimism in risk perception in a
variety of goal-oriented tasks. Optimism in risk perception was studied by
varying the calculation of the Temporal Difference error, i.e., delta, in three
ways: realistic (stochastically correct), optimistic (assuming action control),
and overly optimistic (assuming outcome control). We show that for the gambling
task individuals with 'healthy' perception of control, i.e., action optimism,
do not develop gambling behaviour while individuals with 'unhealthy' perception
of control, i.e., outcome optimism, do. We show that high intensity of
sensations and low levels of fear co-occur due to optimistic risk perception.
We found that overly optimistic risk perception (outcome optimism) results in
risk taking and in persistent gambling behaviour in addition to high intensity
of sensations. We discuss how our results replicate risk-taking related
phenomena.
Face frontalization for Alignment and Recognition
Authors: Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic
Date: 2015-02-03 00:00:00
Tasks: Face Recognition, Face Reconstruction, Robust Face Recognition

Recently, it was shown that excellent results can be achieved in both face
landmark localization and pose-invariant face recognition. These breakthroughs
are attributed to the efforts of the community to manually annotate facial
images in many different poses and to collect 3D faces data. In this paper, we
propose a novel method for joint face landmark localization and frontal face
reconstruction (pose correction) using a small set of frontal images only. By
observing that the frontal facial image is the one with the minimum rank from
all different poses we formulate an appropriate model which is able to jointly
recover the facial landmarks as well as the frontalized version of the face. To
this end, a suitable optimization problem, involving the minimization of the
nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is
assessed in frontal face reconstruction (pose correction), face landmark
localization, and pose-invariant face recognition and verification by
conducting experiments on $6$ facial images databases. The experimental results
demonstrate the effectiveness of the proposed method.
A multiset model of multi-species evolution to solve big deceptive problems
Authors: Luis Correia, Antonio Manso
Date: 2015-02-03 00:00:00

This chapter presents SMuGA, an integration of symbiogenesis with the
Multiset Genetic Algorithm (MuGA). The symbiogenetic approach used here is
based on the host-parasite model with the novelty of varying the length of
parasites along the evolutionary process. Additionally, it models
collaborations between multiple parasites and a single host. To improve
efficiency, we introduced proxy evaluation of parasites, which saves fitness
function calls and exponentially reduces the symbiotic collaborations produced.
Another novel feature consists of breaking the evolutionary cycle into two
phases: a symbiotic phase and a phase of independent evolution of both hosts
and parasites. SMuGA was tested in optimization of a variety of deceptive
functions, with results one order of magnitude better than state of the art
symbiotic algorithms. This allowed to optimize deceptive problems with large
sizes, and showed a linear scaling in the number of iterations to attain the
optimum.
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints
Authors: Xiaoxia Sun, Nasser M. Nasrabadi, Trac. D. Tran
Date: 2015-02-03 00:00:00
Tasks: Dictionary Learning, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Sparse representation models a signal as a linear combination of a small
number of dictionary atoms. As a generative model, it requires the dictionary
to be highly redundant in order to ensure both a stable high sparsity level and
a low reconstruction error for the signal. However, in practice, this
requirement is usually impaired by the lack of labelled training samples.
Fortunately, previous research has shown that the requirement for a redundant
dictionary can be less rigorous if simultaneous sparse approximation is
employed, which can be carried out by enforcing various structured sparsity
constraints on the sparse codes of the neighboring pixels. In addition,
numerous works have shown that applying a variety of dictionary learning
methods for the sparse representation model can also improve the classification
performance. In this paper, we highlight the task-driven dictionary learning
algorithm, which is a general framework for the supervised dictionary learning
method. We propose to enforce structured sparsity priors on the task-driven
dictionary learning method in order to improve the performance of the
hyperspectral classification. Our approach is able to benefit from both the
advantages of the simultaneous sparse representation and those of the
supervised dictionary learning. We enforce two different structured sparsity
priors, the joint and Laplacian sparsity, on the task-driven dictionary
learning method and provide the details of the corresponding optimization
algorithms. Experiments on numerous popular hyperspectral images demonstrate
that the classification performance of our approach is superior to sparse
representation classifier with structured priors or the task-driven dictionary
learning method.
$Q$- and $A$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Authors: Phillip J. Schulte, Anastasios A. Tsiatis, Eric B. Laber, Marie Davidian
Date: 2012-02-19 00:00:00

In clinical practice, physicians make a series of treatment decisions over
the course of a patient's disease based on his/her baseline and evolving
characteristics. A dynamic treatment regime is a set of sequential decision
rules that operationalizes this process. Each rule corresponds to a decision
point and dictates the next treatment action based on the accrued information.
Using existing data, a key goal is estimating the optimal regime, that, if
followed by the patient population, would yield the most favorable outcome on
average. Q- and A-learning are two main approaches for this purpose. We provide
a detailed account of these methods, study their performance, and illustrate
them using data from a depression study.
Landmark-Guided Elastic Shape Analysis of Human Character Motions
Authors: Martin Bauer, Markus Eslitzbichler, Markus Grasmair
Date: 2015-02-03 00:00:00
Tasks: General Classification

Motions of virtual characters in movies or video games are typically
generated by recording actors using motion capturing methods. Animations
generated this way often need postprocessing, such as improving the periodicity
of cyclic animations or generating entirely new motions by interpolation of
existing ones. Furthermore, search and classification of recorded motions
becomes more and more important as the amount of recorded motion data grows.
  In this paper, we will apply methods from shape analysis to the processing of
animations. More precisely, we will use the by now classical elastic metric
model used in shape matching, and extend it by incorporating additional inexact
feature point information, which leads to an improved temporal alignment of
different animations.
An Ontology for Comprehensive Tutoring of Euphonic Conjunctions of Sanskrit Grammar
Authors: S. V. Kasmir Raja, V. Rajitha, Meenakshi Lakshmanan
Date: 2014-10-10 00:00:00

Euphonic conjunctions (sandhis) form a very important aspect of Sanskrit
morphology and phonology. The traditional and modern methods of studying about
euphonic conjunctions in Sanskrit follow different methodologies. The former
involves a rigorous study of the Paninian system embodied in Panini's
Ashtadhyayi, while the latter usually involves the study of a few important
sandhi rules with the use of examples. The former is not suitable for
beginners, and the latter, not sufficient to gain a comprehensive understanding
of the operation of sandhi rules. This is so since there are not only numerous
sandhi rules and exceptions, but also complex precedence rules involved. The
need for a new ontology for sandhi-tutoring was hence felt. This work presents
a comprehensive ontology designed to enable a student-user to learn in stages
all about euphonic conjunctions and the relevant aphorisms of Sanskrit grammar
and to test and evaluate the progress of the student-user. The ontology forms
the basis of a multimedia sandhi tutor that was given to different categories
of users including Sanskrit scholars for extensive and rigorous testing.
Parameter Selection In Particle Swarm Optimization For Transportation Network Design Problem
Authors: Mehran Fasihozaman Langerudi
Date: 2014-12-22 00:00:00

In transportation planning and development, transport network design problem
seeks to optimize specific objectives (e.g. total travel time) through choosing
among a given set of projects while keeping consumption of resources (e.g.
budget) within their limits. Due to the numerous cases of choosing projects,
solving such a problem is very difficult and time-consuming. Based on particle
swarm optimization (PSO) technique, a heuristic solution algorithm for the
bi-level problem is designed. This paper evaluates the algorithm performance in
the response of changing certain basic PSO parameters.
Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
Authors: Xiaodan Liang, Qingxing Cao, Rui Huang, Liang Lin
Date: 2015-02-03 00:00:00

The aim of this study is to provide an automatic computational framework to
assist clinicians in diagnosing Focal Liver Lesions (FLLs) in
Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip
as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as
latent variables in a discriminative model. Different types of FLLs are
characterized by both spatial and temporal enhancement patterns of the ROIs.
The model is learned by iteratively inferring the optimal ROI locations and
optimizing the model parameters. To efficiently search the optimal spatial and
temporal locations of the ROIs, we propose a data-driven inference algorithm by
combining effective spatial and temporal pruning. The experiments show that our
method achieves promising results on the largest dataset in the literature (to
the best of our knowledge), which we have made publicly available.