pwc-archive/papers-with-abstracts huggingface dataset:
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
Authors: Ken Chatfield, Karen Simonyan, Andrew Zisserman
Date: 2014-07-17 00:00:00
Tasks: Binarization, GPU, Quantization, Retrieval

We investigate the gains in precision and speed, that can be obtained by
using Convolutional Networks (ConvNets) for on-the-fly retrieval - where
classifiers are learnt at run time for a textual query from downloaded images,
and used to rank large image or video datasets.
  We make three contributions: (i) we present an evaluation of state-of-the-art
image representations for object category retrieval over standard benchmark
datasets containing 1M+ images; (ii) we show that ConvNets can be used to
obtain features which are incredibly performant, and yet much lower dimensional
than previous state-of-the-art image representations, and that their
dimensionality can be reduced further without loss in performance by
compression using product quantization or binarization. Consequently, features
with the state-of-the-art performance on large-scale datasets of millions of
images can fit in the memory of even a commodity GPU card; (iii) we show that
an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel
with downloading the new training images, allowing for a continuous refinement
of the model as more images become available, and simultaneous training and
ranking. The outcome is an on-the-fly system that significantly outperforms its
predecessors in terms of: precision of retrieval, memory requirements, and
speed, facilitating accurate on-the-fly learning and ranking in under a second
on a single GPU.
FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions
Authors: Md. Lisul Islam, Novia Nurain, Swakkhar Shatabda, M. Sohel Rahman
Date: 2014-11-17 00:00:00
Tasks: Cloud Computing, Distributed Computing, graph partitioning

Graph partitioning, a well studied problem of parallel computing has many
applications in diversified fields such as distributed computing, social
network analysis, data mining and many other domains. In this paper, we
introduce FGPGA, an efficient genetic approach for producing feasible graph
partitions. Our method takes into account the heterogeneity and capacity
constraints of the partitions to ensure balanced partitioning. Such approach
has various applications in mobile cloud computing that include feasible
deployment of software applications on the more resourceful infrastructure in
the cloud instead of mobile hand set. Our proposed approach is light weight and
hence suitable for use in cloud architecture. We ensure feasibility of the
partitions generated by not allowing over-sized partitions to be generated
during the initialization and search. Our proposed method tested on standard
benchmark datasets significantly outperforms the state-of-the-art methods in
terms of quality of partitions and feasibility of the solutions.
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
Authors: Robert A. Vandermeulen, Clayton D. Scott
Date: 2014-11-17 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Density Estimation, parameter estimation

While robust parameter estimation has been well studied in parametric density
estimation, there has been little investigation into robust density estimation
in the nonparametric setting. We present a robust version of the popular kernel
density estimator (KDE). As with other estimators, a robust version of the KDE
is useful since sample contamination is a common issue with datasets. What
"robustness" means for a nonparametric density estimate is not straightforward
and is a topic we explore in this paper. To construct a robust KDE we scale the
traditional KDE and project it to its nearest weighted KDE in the $L^2$ norm.
This yields a scaled and projected KDE (SPKDE). Because the squared $L^2$ norm
penalizes point-wise errors superlinearly this causes the weighted KDE to
allocate more weight to high density regions. We demonstrate the robustness of
the SPKDE with numerical experiments and a consistency result which shows that
asymptotically the SPKDE recovers the uncontaminated density under sufficient
conditions on the contamination.
Geodesic Exponential Kernels: When Curvature and Linearity Conflict
Authors: Aasa Feragen, Francois Lauze, Søren Hauberg
Date: 2014-11-02 00:00:00
Proceeding: CVPR 2015 6

We consider kernel methods on general geodesic metric spaces and provide both
negative and positive results. First we show that the common Gaussian kernel
can only be generalized to a positive definite kernel on a geodesic metric
space if the space is flat. As a result, for data on a Riemannian manifold, the
geodesic Gaussian kernel is only positive definite if the Riemannian manifold
is Euclidean. This implies that any attempt to design geodesic Gaussian kernels
on curved Riemannian manifolds is futile. However, we show that for spaces with
conditionally negative definite distances the geodesic Laplacian kernel can be
generalized while retaining positive definiteness. This implies that geodesic
Laplacian kernels can be generalized to some curved spaces, including spheres
and hyperbolic spaces. Our theoretical results are verified empirically.
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
Authors: Mehrtash T. Harandi, Mathieu Salzmann, Richard Hartley
Date: 2014-07-04 00:00:00
Tasks: Dimensionality Reduction

Representing images and videos with Symmetric Positive Definite (SPD)
matrices and considering the Riemannian geometry of the resulting space has
proven beneficial for many recognition tasks. Unfortunately, computation on the
Riemannian manifold of SPD matrices --especially of high-dimensional ones--
comes at a high cost that limits the applicability of existing techniques. In
this paper we introduce an approach that lets us handle high-dimensional SPD
matrices by constructing a lower-dimensional, more discriminative SPD manifold.
To this end, we model the mapping from the high-dimensional SPD manifold to the
low-dimensional one with an orthonormal projection. In particular, we search
for a projection that yields a low-dimensional manifold with maximum
discriminative power encoded via an affinity-weighted similarity measure based
on metrics on the manifold. Learning can then be expressed as an optimization
problem on a Grassmann manifold. Our evaluation on several classification tasks
shows that our approach leads to a significant accuracy gain over
state-of-the-art methods.
A Latent Clothing Attribute Approach for Human Pose Estimation
Authors: Weipeng Zhang, Jie Shen, Guangcan Liu, Yong Yu
Date: 2014-11-16 00:00:00
Tasks: Action Recognition, Attribute, Pose Estimation, Retrieval, Temporal Action Localization

As a fundamental technique that concerns several vision tasks such as image
parsing, action recognition and clothing retrieval, human pose estimation (HPE)
has been extensively investigated in recent years. To achieve accurate and
reliable estimation of the human pose, it is well-recognized that the clothing
attributes are useful and should be utilized properly. Most previous
approaches, however, require to manually annotate the clothing attributes and
are therefore very costly. In this paper, we shall propose and explore a
\emph{latent} clothing attribute approach for HPE. Unlike previous approaches,
our approach models the clothing attributes as latent variables and thus
requires no explicit labeling for the clothing attributes. The inference of the
latent variables are accomplished by utilizing the framework of latent
structured support vector machines (LSSVM). We employ the strategy of
\emph{alternating direction} to train the LSSVM model: In each iteration, one
kind of variables (e.g., human pose or clothing attribute) are fixed and the
others are optimized. Our extensive experiments on two real-world benchmarks
show the state-of-the-art performance of our proposed approach.
Empirical non-parametric estimation of the Fisher Information
Authors: Visar Berisha, Alfred O. Hero
Date: 2014-08-06 00:00:00
Tasks: Density Estimation

The Fisher information matrix (FIM) is a foundational concept in statistical
signal processing. The FIM depends on the probability distribution, assumed to
belong to a smooth parametric family. Traditional approaches to estimating the
FIM require estimating the probability distribution function (PDF), or its
parameters, along with its gradient or Hessian. However, in many practical
situations the PDF of the data is not known but the statistician has access to
an observation sample for any parameter value. Here we propose a method of
estimating the FIM directly from sampled data that does not require knowledge
of the underlying PDF. The method is based on non-parametric estimation of an
$f$-divergence over a local neighborhood of the parameter space and a relation
between curvature of the $f$-divergence and the FIM. Thus we obtain an
empirical estimator of the FIM that does not require density estimation and is
asymptotically consistent. We empirically evaluate the validity of our approach
using two experiments.
Non-Standard Words as Features for Text Categorization
Authors: Slobodan Beliga, Sanda Martinčić-Ipšić
Date: 2014-08-28 00:00:00
Tasks: Lemmatization, Text Categorization

This paper presents categorization of Croatian texts using Non-Standard Words
(NSW) as features. Non-Standard Words are: numbers, dates, acronyms,
abbreviations, currency, etc. NSWs in Croatian language are determined
according to Croatian NSW taxonomy. For the purpose of this research, 390 text
documents were collected and formed the SKIPEZ collection with 6 classes:
official, literary, informative, popular, educational and scientific. Text
categorization experiment was conducted on three different representations of
the SKIPEZ collection: in the first representation, the frequencies of NSWs are
used as features; in the second representation, the statistic measures of NSWs
(variance, coefficient of variation, standard deviation, etc.) are used as
features; while the third representation combines the first two feature sets.
Naive Bayes, CN2, C4.5, kNN, Classification Trees and Random Forest algorithms
were used in text categorization experiments. The best categorization results
are achieved using the first feature set (NSW frequencies) with the
categorization accuracy of 87%. This suggests that the NSWs should be
considered as features in highly inflectional languages, such as Croatian. NSW
based features reduce the dimensionality of the feature space without standard
lemmatization procedures, and therefore the bag-of-NSWs should be considered
for further Croatian texts categorization experiments.
Ten Years of Pedestrian Detection, What Have We Learned?
Authors: Rodrigo Benenson, Mohamed Omran, Jan Hosang, Bernt Schiele
Date: 2014-11-16 00:00:00
Tasks: Pedestrian Detection

Paper-by-paper results make it easy to miss the forest for the trees.We
analyse the remarkable progress of the last decade by discussing the main ideas
explored in the 40+ detectors currently present in the Caltech pedestrian
detection benchmark. We observe that there exist three families of approaches,
all currently reaching similar detection quality. Based on our analysis, we
study the complementarity of the most promising ideas by combining multiple
published strategies. This new decision forest detector achieves the current
best known performance on the challenging Caltech-USA dataset.
Combining contextual and local edges for line segment extraction in cluttered images
Authors: Rui F. C. Guerreiro
Date: 2014-11-16 00:00:00
Tasks: valid

Automatic extraction methods typically assume that line segments are
pronounced, thin, few and far between, do not cross each other, and are noise
and clutter-free. Since these assumptions often fail in realistic scenarios,
many line segments are not detected or are fragmented. In more severe cases,
i.e., many who use the Hough Transform, extraction can fail entirely. In this
paper, we propose a method that tackles these issues. Its key aspect is the
combination of thresholded image derivatives obtained with filters of large and
small footprints, which we denote as contextual and local edges, respectively.
Contextual edges are robust to noise and we use them to select valid local
edges, i.e., local edges that are of the same type as contextual ones:
dark-to-bright transition of vice-versa. If the distance between valid local
edges does not exceed a maximum distance threshold, we enforce connectivity by
marking them and the pixels in between as edge points. This originates
connected edge maps that are robust and well localized. We use a powerful
two-sample statistical test to compute contextual edges, which we introduce
briefly, as they are unfamiliar to the image processing community. Finally, we
present experiments that illustrate, with synthetic and real images, how our
method is efficient in extracting complete segments of all lengths and widths
in several situations where current methods fail.
HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection
Authors: Zhiwei Qin, Xiaocheng Tang, Ioannis Akrotirianakis, Amit Chakraborty
Date: 2014-11-16 00:00:00
Tasks: feature selection, General Classification

We consider classification tasks in the regime of scarce labeled training
data in high dimensional feature space, where specific expert knowledge is also
available. We propose a new hybrid optimization algorithm that solves the
elastic-net support vector machine (SVM) through an alternating direction
method of multipliers in the first phase, followed by an interior-point method
for the classical SVM in the second phase. Both SVM formulations are adapted to
knowledge incorporation. Our proposed algorithm addresses the challenges of
automatic feature selection, high optimization accuracy, and algorithmic
flexibility for taking advantage of prior knowledge. We demonstrate the
effectiveness and efficiency of our algorithm and compare it with existing
methods on a collection of synthetic and real-world data.
GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem for Protein Structures
Authors: Md. Lisul Islam, Swakkhar Shatabda, M. Sohel Rahman
Date: 2014-11-16 00:00:00
Tasks: Protein Structure Prediction

Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to
predict the native structure of proteins. However, NMR machines are only able
to report approximate and partial distances between pair of atoms. To build the
protein structure one has to solve the Euclidean distance geometry problem
given the incomplete interval distance data produced by NMR machines. In this
paper, we propose a new genetic algorithm for solving the Euclidean distance
geometry problem for protein structure prediction given sparse NMR data. Our
genetic algorithm uses a greedy mutation operator to intensify the search, a
twin removal technique for diversification in the population and a random
restart method to recover stagnation. On a standard set of benchmark dataset,
our algorithm significantly outperforms standard genetic algorithms.
Efficient and Accurate Approximations of Nonlinear Convolutional Networks
Authors: Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, Jian Sun
Date: 2014-11-16 00:00:00
Proceeding: CVPR 2015 6

This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We minimize the reconstruction error of the nonlinear responses, subject to a low-rank constraint which helps to reduce the complexity of filters. We develop an effective solution to this constrained nonlinear optimization problem. An algorithm is also presented for reducing the accumulated error when multiple layers are approximated. A whole-model speedup ratio of 4x is demonstrated on a large network trained for ImageNet, while the top-5 error rate is only increased by 0.9%. Our accelerated model has a comparably fast speed as the "AlexNet", but is 4.7% more accurate.
Graph connection Laplacian and random matrices with random blocks
Authors: Noureddine El Karoui, Hau-Tieng Wu
Date: 2013-10-01 00:00:00

Graph connection Laplacian (GCL) is a modern data analysis technique that is
starting to be applied for the analysis of high dimensional and massive
datasets. Motivated by this technique, we study matrices that are akin to the
ones appearing in the null case of GCL, i.e the case where there is no
structure in the dataset under investigation. Developing this understanding is
important in making sense of the output of the algorithms based on GCL. We
hence develop a theory explaining the behavior of the spectral distribution of
a large class of random matrices, in particular random matrices with random
block entries of fixed size. Part of the theory covers the case where there is
significant dependence between the blocks. Numerical work shows that the
agreement between our theoretical predictions and numerical simulations is
generally very good.
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
Authors: Ke Jiang, Qichao Que, Brian Kulis
Date: 2014-11-16 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Retrieval

We present a simple but powerful reinterpretation of kernelized
locality-sensitive hashing (KLSH), a general and popular method developed in
the vision community for performing approximate nearest-neighbor searches in an
arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based
on viewing the steps of the KLSH algorithm in an appropriately projected space,
and has several key theoretical and practical benefits. First, it eliminates
the problematic conceptual difficulties that are present in the existing
motivation of KLSH. Second, it yields the first formal retrieval performance
bounds for KLSH. Third, our analysis reveals two techniques for boosting the
empirical performance of KLSH. We evaluate these extensions on several
large-scale benchmark image retrieval data sets, and show that our analysis
leads to improved recall performance of at least 12%, and sometimes much
higher, over the standard KLSH method.
ROSS User's Guide and Reference Manual (Version 1.0)
Authors: Glenn R. Hofford
Date: 2014-11-15 00:00:00
Tasks: Natural Language Understanding

The ROSS method is a new approach in the area of knowledge representation
that is useful for many artificial intelligence and natural language
understanding representation and reasoning tasks. (ROSS stands for
"Representation", "Ontology", "Structure", "Star" language). ROSS is a physical
symbol-based representational scheme. ROSS provides a complex model for the
declarative representation of physical structure and for the representation of
processes and causality. From the metaphysical perspective, the ROSS view of
external reality involves a 4D model, wherein discrete single-time-point
unit-sized locations with states are the basis for all objects, processes and
aspects that can be modeled. ROSS includes a language called "Star" for the
specification of ontology classes. The ROSS method also includes a formal
scheme called the "instance model". Instance models are used in the area of
natural language meaning representation to represent situations. This document
is an in-depth specification of the ROSS method.
Introduction to ROSS: A New Representational Scheme
Authors: Glenn R. Hofford
Date: 2014-11-15 00:00:00

ROSS ("Representation, Ontology, Structure, Star") is introduced as a new
method for knowledge representation that emphasizes representational constructs
for physical structure. The ROSS representational scheme includes a language
called "Star" for the specification of ontology classes. The ROSS method also
includes a formal scheme called the "instance model". Instance models are used
in the area of natural language meaning representation to represent situations.
This paper provides both the rationale and the philosophical background for the
ROSS method.
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
Authors: Saeed Afshar, Libin George, Jonathan Tapson, Andre van Schaik, Tara Julia Hamilton
Date: 2014-08-06 00:00:00

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a
simple spiking neuron model that performs statistical inference and
unsupervised learning of spatiotemporal spike patterns. SKAN is the first
proposed neuron model to investigate the effects of dynamic synapto-dendritic
kernels and demonstrate their computational power even at the single neuron
scale. The rule-set defining the neuron is simple there are no complex
mathematical operations such as normalization, exponentiation or even
multiplication. The functionalities of SKAN emerge from the real-time
interaction of simple additive and binary processes. Like a biological neuron,
SKAN is robust to signal and parameter noise, and can utilize both in its
operations. At the network scale neurons are locked in a race with each other
with the fastest neuron to spike effectively hiding its learnt pattern from its
neighbors. The robustness to noise, high speed and simple building blocks not
only make SKAN an interesting neuron model in computational neuroscience, but
also make it ideal for implementation in digital and analog neuromorphic
systems which is demonstrated through an implementation in a Field Programmable
Gate Array (FPGA).
Diversity Handling In Evolutionary Landscape
Authors: Maumita Bhattacharya
Date: 2014-11-15 00:00:00
Tasks: Diversity

The search ability of an Evolutionary Algorithm (EA) depends on the variation
among the individuals in the population. Maintaining an optimal level of
diversity in the EA population is imperative to ensure that progress of the EA
search is unhindered by premature convergence to suboptimal solutions. Clearer
understanding of the concept of population diversity, in the context of
evolutionary search and premature convergence in particular, is the key to
designing efficient EAs. To this end, this paper first presents a comprehensive
analysis of the EA population diversity issues. Next we present an
investigation on a counter-niching EA technique that introduces and maintains
constructive diversity in the population. The proposed approach uses informed
genetic operations to reach promising, but un-explored or under-explored areas
of the search space, while discouraging premature local convergence. Simulation
runs on a number of standard benchmark test functions with Genetic Algorithm
(GA) implementation shows promising results.
Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning
Authors: Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette
Date: 2014-11-15 00:00:00

This paper aims to explore the effect of prior disambiguation on neural
network- based compositional models, with the hope that better semantic
representations for text compounds can be produced. We disambiguate the input
word vectors before they are fed into a compositional deep net. A series of
evaluations shows the positive effect of prior disambiguation for such deep
models.
Definition of Visual Speech Element and Research on a Method of Extracting Feature Vector for Korean Lip-Reading
Authors: Ha Jong Won, Li Gwang Chol, Kim Hyok Chol, Li Kum Song
Date: 2014-11-15 00:00:00
Tasks: Lip Reading

In this paper, we defined the viseme (visual speech element) and described
about the method of extracting visual feature vector. We defined the 10 visemes
based on vowel by analyzing of Korean utterance and proposed the method of
extracting the 20-dimensional visual feature vector, combination of static
features and dynamic features. Lastly, we took an experiment in recognizing
words based on 3-viseme HMM and evaluated the efficiency.