pwc-archive/papers-with-abstracts huggingface dataset:
Active Distance-Based Clustering using K-medoids
Authors: Mehrdad Ghadiri, Amin Aghaee, Mahdieh Soleymani Baghshah
Date: 2015-12-12 00:00:00
Tasks: Clustering

k-medoids algorithm is a partitional, centroid-based clustering algorithm
which uses pairwise distances of data points and tries to directly decompose
the dataset with $n$ points into a set of $k$ disjoint clusters. However,
k-medoids itself requires all distances between data points that are not so
easy to get in many applications. In this paper, we introduce a new method
which requires only a small proportion of the whole set of distances and makes
an effort to estimate an upper-bound for unknown distances using the inquired
ones. This algorithm makes use of the triangle inequality to calculate an
upper-bound estimation of the unknown distances. Our method is built upon a
recursive approach to cluster objects and to choose some points actively from
each bunch of data and acquire the distances between these prominent points
from oracle. Experimental results show that the proposed method using only a
small subset of the distances can find proper clustering on many real-world and
synthetic datasets.
A mathematical motivation for complex-valued convolutional networks
Authors: Joan Bruna, Soumith Chintala, Yann Lecun, Serkan Piantino, Arthur Szlam, Mark Tygert
Date: 2015-03-11 00:00:00

A complex-valued convolutional network (convnet) implements the repeated
application of the following composition of three operations, recursively
applying the composition to an input vector of nonnegative real numbers: (1)
convolution with complex-valued vectors followed by (2) taking the absolute
value of every entry of the resulting vectors followed by (3) local averaging.
For processing real-valued random vectors, complex-valued convnets can be
viewed as "data-driven multiscale windowed power spectra," "data-driven
multiscale windowed absolute spectra," "data-driven multiwavelet absolute
values," or (in their most general configuration) "data-driven nonlinear
multiwavelet packets." Indeed, complex-valued convnets can calculate multiscale
windowed spectra when the convnet filters are windowed complex-valued
exponentials. Standard real-valued convnets, using rectified linear units
(ReLUs), sigmoidal (for example, logistic or tanh) nonlinearities, max.
pooling, etc., do not obviously exhibit the same exact correspondence with
data-driven wavelets (whereas for complex-valued convnets, the correspondence
is much more than just a vague analogy). Courtesy of the exact correspondence,
the remarkably rich and rigorous body of mathematical analysis for wavelets
applies directly to (complex-valued) convnets.
A Hidden Markov Model Based System for Entity Extraction from Social Media English Text at FIRE 2015
Authors: Kamal Sarkar
Date: 2015-12-12 00:00:00
Tasks: Entity Extraction using GAN, POS, TAG

This paper presents the experiments carried out by us at Jadavpur University
as part of the participation in FIRE 2015 task: Entity Extraction from Social
Media Text - Indian Languages (ESM-IL). The tool that we have developed for the
task is based on Trigram Hidden Markov Model that utilizes information like
gazetteer list, POS tag and some other word level features to enhance the
observation probabilities of the known tokens as well as unknown tokens. We
submitted runs for English only. A statistical HMM (Hidden Markov Models) based
model has been used to implement our system. The system has been trained and
tested on the datasets released for FIRE 2015 task: Entity Extraction from
Social Media Text - Indian Languages (ESM-IL). Our system is the best performer
for English language and it obtains precision, recall and F-measures of 61.96,
39.46 and 48.21 respectively.
A Novel Approach to Document Classification using WordNet
Authors: Koushiki Sarkar, Ritwika Law
Date: 2015-10-04 00:00:00
Tasks: Classification, Document Classification, General Classification

Content based Document Classification is one of the biggest challenges in the
context of free text mining. Current algorithms on document classifications
mostly rely on cluster analysis based on bag-of-words approach. However that
method is still being applied to many modern scientific dilemmas. It has
established a strong presence in fields like economics and social science to
merit serious attention from the researchers. In this paper we would like to
propose and explore an alternative grounded more securely on the dictionary
classification and correlatedness of words and phrases. It is expected that
application of our existing knowledge about the underlying classification
structure may lead to improvement of the classifier's performance.
Quantum assisted Gaussian process regression
Authors: Zhikuan Zhao, Jack K. Fitzsimons, Joseph F. Fitzsimons
Date: 2015-12-12 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, GPR, regression

Gaussian processes (GP) are a widely used model for regression problems in
supervised machine learning. Implementation of GP regression typically requires
$O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow
et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process
regression (GPR), leading to an exponential reduction in computation time in
some instances. We show that even in some cases not ideally suited to the
quantum linear systems algorithm, a polynomial increase in efficiency still
occurs.
Query Answering over Contextualized RDF/OWL Knowledge with Forall-Existential Bridge Rules: Decidable Finite Extension Classes (Post Print)
Authors: Mathew Joseph, Gabriel Kuper, Till Mossakowski, Luciano Serafini
Date: 2015-12-12 00:00:00

The proliferation of contextualized knowledge in the Semantic Web (SW) has
led to the popularity of knowledge formats such as \emph{quads} in the SW
community. A quad is an extension of an RDF triple with contextual information
of the triple. In this paper, we study the problem of query answering over
quads augmented with forall-existential bridge rules that enable
interoperability of reasoning between triples in various contexts. We call a
set of quads together with such expressive bridge rules, a quad-system. Query
answering over quad-systems is undecidable, in general. We derive decidable
classes of quad-systems, for which query answering can be done using forward
chaining. Sound, complete and terminating procedures, which are adaptations of
the well known chase algorithm, are provided for these classes for deciding
query entailment. Safe, msafe, and csafe class of quad-systems restrict the
structure of blank nodes generated during the chase computation process to be
directed acyclic graphs (DAGs) of bounded depth. RR and restricted RR classes
do not allow the generation of blank nodes during the chase computation
process. Both data and combined complexity of query entailment has been
established for the classes derived. We further show that quad-systems are
equivalent to forall-existential rules whose predicates are restricted to
ternary arity, modulo polynomial time translations. We subsequently show that
the technique of safety, strictly subsumes in expressivity, some of the well
known and expressive techniques, such as joint acyclicity and model faithful
acyclicity, used for decidability guarantees in the realm of forall-existential
rules.
Active Sampler: Light-weight Accelerator for Complex Data Analytics at Scale
Authors: Jinyang Gao, H. V. Jagadish, Beng Chin Ooi
Date: 2015-12-12 00:00:00
Tasks: feature selection

Recent years have witnessed amazing outcomes from "Big Models" trained by
"Big Data". Most popular algorithms for model training are iterative. Due to
the surging volumes of data, we can usually afford to process only a fraction
of the training data in each iteration. Typically, the data are either
uniformly sampled or sequentially accessed.
  In this paper, we study how the data access pattern can affect model
training. We propose an Active Sampler algorithm, where training data with more
"learning value" to the model are sampled more frequently. The goal is to focus
training effort on valuable instances near the classification boundaries,
rather than evident cases, noisy data or outliers. We show the correctness and
optimality of Active Sampler in theory, and then develop a light-weight
vectorized implementation. Active Sampler is orthogonal to most approaches
optimizing the efficiency of large-scale data analytics, and can be applied to
most analytics models trained by stochastic gradient descent (SGD) algorithm.
Extensive experimental evaluations demonstrate that Active Sampler can speed up
the training procedure of SVM, feature selection and deep learning, for
comparable training quality by 1.6-2.2x.
Minimal Perceptrons for Memorizing Complex Patterns
Authors: Marissa Pastor, Juyong Song, Danh-Tai Hoang, Junghyo Jo
Date: 2015-12-12 00:00:00
Tasks: General Classification

Feedforward neural networks have been investigated to understand learning and
memory, as well as applied to numerous practical problems in pattern
classification. It is a rule of thumb that more complex tasks require larger
networks. However, the design of optimal network architectures for specific
tasks is still an unsolved fundamental problem. In this study, we consider
three-layered neural networks for memorizing binary patterns. We developed a
new complexity measure of binary patterns, and estimated the minimal network
size for memorizing them as a function of their complexity. We formulated the
minimal network size for regular, random, and complex patterns. In particular,
the minimal size for complex patterns, which are neither ordered nor
disordered, was predicted by measuring their Hamming distances from known
ordered patterns. Our predictions agreed with simulations based on the
back-propagation algorithm.
Multispectral Palmprint Recognition Using a Hybrid Feature
Authors: Sina Akbari Mistani, Shervin Minaee, Emad Fatemizadeh
Date: 2011-12-27 00:00:00

Personal identification problem has been a major field of research in recent
years. Biometrics-based technologies that exploit fingerprints, iris, face,
voice and palmprints, have been in the center of attention to solve this
problem. Palmprints can be used instead of fingerprints that have been of the
earliest of these biometrics technologies. A palm is covered with the same skin
as the fingertips but has a larger surface, giving us more information than the
fingertips. The major features of the palm are palm-lines, including principal
lines, wrinkles and ridges. Using these lines is one of the most popular
approaches towards solving the palmprint recognition problem. Another robust
feature is the wavelet energy of palms. In this paper we used a hybrid feature
which combines both of these features. %Moreover, multispectral analysis is
applied to improve the performance of the system. At the end, minimum distance
classifier is used to match test images with one of the training samples. The
proposed algorithm has been tested on a well-known multispectral palmprint
dataset and achieved an average accuracy of 98.8\%.
Efficient Deep Feature Learning and Extraction via StochasticNets
Authors: Mohammad Javad Shafiee, Parthipan Siva, Paul Fieguth, Alexander Wong
Date: 2015-12-11 00:00:00
Tasks: Classification, General Classification

Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting
Authors: Abhimanu Kumar, Pengtao Xie, Junming Yin, Eric P. Xing
Date: 2015-12-09 00:00:00
Tasks: General Classification, image-classification, Image Classification

We propose a distributed approach to train deep neural networks (DNNs), which
has guaranteed convergence theoretically and great scalability empirically:
close to 6 times faster on instance of ImageNet data set when run with 6
machines. The proposed scheme is close to optimally scalable in terms of number
of machines, and guaranteed to converge to the same optima as the undistributed
setting. The convergence and scalability of the distributed setting is shown
empirically across different datasets (TIMIT and ImageNet) and machine learning
tasks (image classification and phoneme extraction). The convergence analysis
provides novel insights into this complex learning scheme, including: 1)
layerwise convergence, and 2) convergence of the weights in probability.
Color Space Transformation Network
Authors: Alexandros Karargyris
Date: 2015-10-31 00:00:00

Deep networks have become very popular over the past few years. The main
reason for this widespread use is their excellent ability to learn and predict
knowledge in a very easy and efficient way. Convolutional neural networks and
auto-encoders have become the normal in the area of imaging and computer vision
achieving unprecedented accuracy levels in many applications. The most common
strategy is to build and train networks with many layers by tuning their
hyper-parameters. While this approach has proven to be a successful way to
build robust deep learning schemes it suffers from high complexity. In this
paper we introduce a module that learns color space transformations within a
network. Given a large dataset of colored images the color space transformation
module tries to learn color space transformations that increase overall
classification accuracy. This module has shown to increase overall accuracy for
the same network design and to achieve faster convergence. It is part of a
broader family of image transformations (e.g. spatial transformer network).
Rethinking the Inception Architecture for Computer Vision
Authors: Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
Date: 2015-12-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Computational Efficiency, Image Classification, Retinal OCT Disease Classification, Robotic Grasping

Convolutional networks are at the core of most state-of-the-art computer
vision solutions for a wide variety of tasks. Since 2014 very deep
convolutional networks started to become mainstream, yielding substantial gains
in various benchmarks. Although increased model size and computational cost
tend to translate to immediate quality gains for most tasks (as long as enough
labeled data is provided for training), computational efficiency and low
parameter count are still enabling factors for various use cases such as mobile
vision and big-data scenarios. Here we explore ways to scale up networks in
ways that aim at utilizing the added computation as efficiently as possible by
suitably factorized convolutions and aggressive regularization. We benchmark
our methods on the ILSVRC 2012 classification challenge validation set
demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6%
top-5 error for single frame evaluation using a network with a computational
cost of 5 billion multiply-adds per inference and with using less than 25
million parameters. With an ensemble of 4 models and multi-crop evaluation, we
report 3.5% top-5 error on the validation set (3.6% error on the test set) and
17.3% top-1 error on the validation set.
Improving Human Activity Recognition Through Ranking and Re-ranking
Authors: Zhenzhong Lan, Shoou-I Yu, Alexander G. Hauptmann
Date: 2015-12-11 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Re-Ranking

We propose two well-motivated ranking-based methods to enhance the
performance of current state-of-the-art human activity recognition systems.
First, as an improvement over the classic power normalization method, we
propose a parameter-free ranking technique called rank normalization (RaN). RaN
normalizes each dimension of the video features to address the sparse and
bursty distribution problems of Fisher Vectors and VLAD. Second, inspired by
curriculum learning, we introduce a training-free re-ranking technique called
multi-class iterative re-ranking (MIR). MIR captures relationships among action
classes by separating easy and typical videos from difficult ones and
re-ranking the prediction scores of classifiers accordingly. We demonstrate
that our methods significantly improve the performance of state-of-the-art
motion features on six real-world datasets.
A New Approach of Gray Images Binarization with Threshold Methods
Authors: Andrei Hossu, Daniela Andone
Date: 2015-12-11 00:00:00
Tasks: Binarization

The paper presents some aspects of the (gray level) image binarization
methods used in artificial vision systems. It is introduced a new approach of
gray level image binarization for artificial vision systems dedicated to
industrial automation temporal thresholding. In the first part of the paper are
extracted some limitations of using the global optimum thresholding in gray
level image binarization. In the second part of this paper are presented some
aspects of the dynamic optimum thresholding method for gray level image
binarization. Starting from classic methods of global and dynamic optimal
thresholding of the gray level images in the next section are introduced the
concepts of temporal histogram and temporal thresholding. In the final section
are presented some practical aspects of the temporal thresholding method in
artificial vision applications form the moving scene in robotic automation
class; pointing out the influence of the acquisition frequency on the methods
results.
Single and Multiple Illuminant Estimation Using Convolutional Neural Networks
Authors: Simone Bianco, Claudio Cusano, Raimondo Schettini
Date: 2015-08-05 00:00:00

In this paper we present a method for the estimation of the color of the
illuminant in RAW images. The method includes a Convolutional Neural Network
that has been specially designed to produce multiple local estimates. A
multiple illuminant detector determines whether or not the local outputs of the
network must be aggregated into a single estimate. We evaluated our method on
standard datasets with single and multiple illuminants, obtaining lower
estimation errors with respect to those obtained by other general purpose
methods in the state of the art.
Deep Feature Learning with Relative Distance Comparison for Person Re-identification
Authors: Shengyong Ding, Liang Lin, Guangrun Wang, Hongyang Chao
Date: 2015-12-11 00:00:00
Tasks: Person Re-Identification, Triplet

Identifying the same individual across different scenes is an important yet
difficult task in intelligent video surveillance. Its main difficulty lies in
how to preserve similarity of the same person against large appearance and
structure variation while discriminating different individuals. In this paper,
we present a scalable distance driven feature learning framework based on the
deep neural network for person re-identification, and demonstrate its
effectiveness to handle the existing challenges. Specifically, given the
training images with the class labels (person IDs), we first produce a large
number of triplet units, each of which contains three images, i.e. one person
with a matched reference and a mismatched reference. Treating the units as the
input, we build the convolutional neural network to generate the layered
representations, and follow with the $L2$ distance metric. By means of
parameter optimization, our framework tends to maximize the relative distance
between the matched pair and the mismatched pair for each triplet unit.
Moreover, a nontrivial issue arising with the framework is that the triplet
organization cubically enlarges the number of training triplets, as one image
can be involved into several triplet units. To overcome this problem, we
develop an effective triplet generation scheme and an optimized gradient
descent algorithm, making the computational load mainly depends on the number
of original images instead of the number of triplets. On several challenging
databases, our approach achieves very promising results and outperforms other
state-of-the-art approaches.
Robust Dictionary based Data Representation
Authors: Wei-Ya Ren
Date: 2015-12-11 00:00:00

The robustness to noise and outliers is an important issue in linear
representation in real applications. We focus on the problem that samples are
grossly corrupted, which is also the 'sample specific' corruptions problem. A
reasonable assumption is that corrupted samples cannot be represented by the
dictionary while clean samples can be well represented. This assumption is
enforced in this paper by investigating the coefficients of corrupted samples.
Concretely, we require the coefficients of corrupted samples be zero. In this
way, the representation quality of clean data can be assured without the effect
of corrupted data. At last, a robust dictionary based data representation
approach and its sparse representation version are proposed, which have
directive significance for future applications.
Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication
Authors: Massimo Cairo, Gabriele Farina, Romeo Rizzi
Date: 2015-11-30 00:00:00

In this paper, we present a novel algorithm for the maximum a posteriori
decoding (MAPD) of time-homogeneous Hidden Markov Models (HMM), improving the
worst-case running time of the classical Viterbi algorithm by a logarithmic
factor. In our approach, we interpret the Viterbi algorithm as a repeated
computation of matrix-vector $(\max, +)$-multiplications. On time-homogeneous
HMMs, this computation is online: a matrix, known in advance, has to be
multiplied with several vectors revealed one at a time. Our main contribution
is an algorithm solving this version of matrix-vector $(\max,+)$-multiplication
in subquadratic time, by performing a polynomial preprocessing of the matrix.
Employing this fast multiplication algorithm, we solve the MAPD problem in
$O(mn^2/ \log n)$ time for any time-homogeneous HMM of size $n$ and observation
sequence of length $m$, with an extra polynomial preprocessing cost negligible
for $m > n$. To the best of our knowledge, this is the first algorithm for the
MAPD problem requiring subquadratic time per observation, under the only
assumption -- usually verified in practice -- that the transition probability
matrix does not change with time.