pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition
Authors: Zhenzhong Lan, Alexander G. Hauptmann
Date: 2015-10-15 00:00:00
Tasks: Action Recognition, Diversity, Temporal Action Localization

We address the problem of generating video features for action recognition.
The spatial pyramid and its variants have been very popular feature models due
to their success in balancing spatial location encoding and spatial invariance.
Although it seems straightforward to extend spatial pyramid to the temporal
domain (spatio-temporal pyramid), the large spatio-temporal diversity of
unconstrained videos and the resulting significantly higher dimensional
representations make it less appealing. This paper introduces the space-time
extended descriptor, a simple but efficient alternative way to include the
spatio-temporal location into the video features. Instead of only coding motion
information and leaving the spatio-temporal location to be represented at the
pooling stage, location information is used as part of the encoding step. This
method is a much more effective and efficient location encoding method as
compared to the fixed grid model because it avoids the danger of over
committing to artificial boundaries and its dimension is relatively low.
Experimental results on several benchmark datasets show that, despite its
simplicity, this method achieves comparable or better results than
spatio-temporal pyramid.
Elasticity-based Matching by Minimizing the Symmetric Difference of Shapes
Authors: Konrad Simon, Ronen Basri
Date: 2015-10-15 00:00:00

We consider the problem of matching two shapes assuming these shapes are
related by an elastic deformation. Using linearized elasticity theory and the
finite element method we seek an elastic deformation that is caused by simple
external boundary forces and accounts for the difference between the two
shapes. Our main contribution is in proposing a cost function and an
optimization procedure to minimize the symmetric difference between the
deformed and the target shapes as an alternative to point matches that guide
the matching in other techniques. We show how to approximate the nonlinear
optimization problem by a sequence of convex problems. We demonstrate the
utility of our method in experiments and compare it to an ICP-like matching
algorithm.
Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level
Authors: Krzysztof Wołk
Date: 2015-10-15 00:00:00
Tasks: Machine Translation, Position, Sentence, Translation

Text alignment and text quality are critical to the accuracy of Machine
Translation (MT) systems, some NLP tools, and any other text processing tasks
requiring bilingual data. This research proposes a language independent
bi-sentence filtering approach based on Polish (not a position-sensitive
language) to English experiments. This cleaning approach was developed on the
TED Talks corpus and also initially tested on the Wikipedia comparable corpus,
but it can be used for any text domain or language pair. The proposed approach
implements various heuristics for sentence comparison. Some of them leverage
synonyms and semantic and structural analysis of text as additional
information. Minimization of data loss was ensured. An improvement in MT system
score with text processed using the tool is discussed.
Sparsity-aware Possibilistic Clustering Algorithms
Authors: Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
Date: 2015-10-15 00:00:00
Tasks: Clustering

In this paper two novel possibilistic clustering algorithms are presented,
which utilize the concept of sparsity. The first one, called sparse
possibilistic c-means, exploits sparsity and can deal well with closely located
clusters that may also be of significantly different densities. The second one,
called sparse adaptive possibilistic c-means, is an extension of the first,
where now the involved parameters are dynamically adapted. The latter can deal
well with even more challenging cases, where, in addition to the above,
clusters may be of significantly different variances. More specifically, it
provides improved estimates of the cluster representatives, while, in addition,
it has the ability to estimate the actual number of clusters, given an
overestimate of it. Extensive experimental results on both synthetic and real
data sets support the previous statements.
Some Theorems for Feed Forward Neural Networks
Authors: K. Eswaran, Vishwajeet Singh
Date: 2015-09-17 00:00:00
Tasks: Cloud Computing, General Classification

In this paper we introduce a new method which employs the concept of
"Orientation Vectors" to train a feed forward neural network and suitable for
problems where large dimensions are involved and the clusters are
characteristically sparse. The new method is not NP hard as the problem size
increases. We `derive' the method by starting from Kolmogrov's method and then
relax some of the stringent conditions. We show for most classification
problems three layers are sufficient and the network size depends on the number
of clusters. We prove as the number of clusters increase from N to N+dN the
number of processing elements in the first layer only increases by d(logN), and
are proportional to the number of classes, and the method is not NP hard.
  Many examples are solved to demonstrate that the method of Orientation
Vectors requires much less computational effort than Radial Basis Function
methods and other techniques wherein distance computations are required, in
fact the present method increases logarithmically with problem size compared to
the Radial Basis Function method and the other methods which depend on distance
computations e.g statistical methods where probabilistic distances are
calculated. A practical method of applying the concept of Occum's razor to
choose between two architectures which solve the same classification problem
has been described. The ramifications of the above findings on the field of
Deep Learning have also been briefly investigated and we have found that it
directly leads to the existence of certain types of NN architectures which can
be used as a "mapping engine", which has the property of "invertibility", thus
improving the prospect of their deployment for solving problems involving Deep
Learning and hierarchical classification. The latter possibility has a lot of
future scope in the areas of machine learning and cloud computing.
A Novel Adaptive Possibilistic Clustering Algorithm
Authors: Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
Date: 2014-12-11 00:00:00
Tasks: Clustering

In this paper a novel possibilistic c-means clustering algorithm, called
Adaptive Possibilistic c-means, is presented. Its main feature is that {\it
all} its parameters, after their initialization, are properly adapted during
its execution. Provided that the algorithm starts with a reasonable
overestimate of the number of physical clusters formed by the data, it is
capable, in principle, to unravel them (a long-standing issue in the clustering
literature). This is due to the fully adaptive nature of the proposed algorithm
that enables the removal of the clusters that gradually become obsolete. In
addition, the adaptation of all its parameters increases the flexibility of the
algorithm in following the variations in the formation of the clusters that
occur from iteration to iteration. Theoretical results that are indicative of
the convergence behavior of the algorithm are also provided. Finally, extensive
simulation results on both synthetic and real data highlight the effectiveness
of the proposed algorithm.
Online Markov decision processes with policy iteration
Authors: Yao Ma, Hao Zhang, Masashi Sugiyama
Date: 2015-10-15 00:00:00

The online Markov decision process (MDP) is a generalization of the classical
Markov decision process that incorporates changing reward functions. In this
paper, we propose practical online MDP algorithms with policy iteration and
theoretically establish a sublinear regret bound. A notable advantage of the
proposed algorithm is that it can be easily combined with function
approximation, and thus large and possibly continuous state spaces can be
efficiently handled. Through experiments, we demonstrate the usefulness of the
proposed algorithm.
Distributed Deep Q-Learning
Authors: Hao Yi Ong, Kevin Chavez, Augustus Hong
Date: 2015-08-18 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a distributed deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is based on the deep Q-network, a convolutional neural
network trained with a variant of Q-learning. Its input is raw pixels and its
output is a value function estimating future rewards from taking an action
given a system state. To distribute the deep Q-network training, we adapt the
DistBelief software framework to the context of efficiently training
reinforcement learning agents. As a result, the method is completely
asynchronous and scales well with the number of machines. We demonstrate that
the deep Q-network agent, receiving only the pixels and the game score as
inputs, was able to achieve reasonable success on a simple game with minimal
parameter tuning.
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
Authors: Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, Luc van Gool
Date: 2015-10-15 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object

In this paper we evaluate the quality of the activation layers of a
convolutional neural network (CNN) for the gen- eration of object proposals. We
generate hypotheses in a sliding-window fashion over different activation
layers and show that the final convolutional layers can find the object of
interest with high recall but poor localization due to the coarseness of the
feature maps. Instead, the first layers of the network can better localize the
object of interest but with a reduced recall. Based on this observation we
design a method for proposing object locations that is based on CNN features
and that combines the best of both worlds. We build an inverse cascade that,
going from the final to the initial convolutional layers of the CNN, selects
the most promising object locations and refines their boxes in a coarse-to-fine
manner. The method is efficient, because i) it uses the same features extracted
for detection, ii) it aggregates features using integral images, and iii) it
avoids a dense evaluation of the proposals due to the inverse coarse-to-fine
cascade. The method is also accurate; it outperforms most of the previously
proposed object proposals approaches and when plugged into a CNN-based detector
produces state-of-the- art detection performance.
A Novel Approach for Human Action Recognition from Silhouette Images
Authors: Satyabrata Maity, Debotosh Bhattacharjee, Amlan Chakrabarti
Date: 2015-10-15 00:00:00
Tasks: Action Detection, Action Recognition, Temporal Action Localization

In this paper, a novel human action recognition technique from video is
presented. Any action of human is a combination of several micro action
sequences performed by one or more body parts of the human. The proposed
approach uses spatio-temporal body parts movement (STBPM) features extracted
from foreground silhouette of the human objects. The newly proposed STBPM
feature estimates the movements of different body parts for any given time
segment to classify actions. We also proposed a rule based logic named rule
action classifier (RAC), which uses a series of condition action rules based on
prior knowledge and hence does not required training to classify any action.
Since we don't require training to classify actions, the proposed approach is
view independent. The experimental results on publicly available Wizeman and
MuHVAi datasets are compared with that of the related research work in terms of
accuracy in the human action detection, and proposed technique outperforms the
others.
Narrative Science Systems: A Review
Authors: Paramjot Kaur Sarao, Puneet Mittal, Rupinder Kaur
Date: 2015-10-15 00:00:00

Automatic narration of events and entities is the need of the hour,
especially when live reporting is critical and volume of information to be
narrated is huge. This paper discusses the challenges in this context, along
with the algorithms used to build such systems. From a systematic study, we can
infer that most of the work done in this area is related to statistical data.
It was also found that subjective evaluation or contribution of experts is also
limited for narration context.
On Semiparametric Exponential Family Graphical Models
Authors: Zhuoran Yang, Yang Ning, Han Liu
Date: 2014-12-30 00:00:00
Tasks: parameter estimation, Two-sample testing

We propose a new class of semiparametric exponential family graphical models
for the analysis of high dimensional mixed data. Different from the existing
mixed graphical models, we allow the nodewise conditional distributions to be
semiparametric generalized linear models with unspecified base measure
functions. Thus, one advantage of our method is that it is unnecessary to
specify the type of each node and the method is more convenient to apply in
practice. Under the proposed model, we consider both problems of parameter
estimation and hypothesis testing in high dimensions. In particular, we propose
a symmetric pairwise score test for the presence of a single edge in the graph.
Compared to the existing methods for hypothesis tests, our approach takes into
account of the symmetry of the parameters, such that the inferential results
are invariant with respect to the different parametrizations of the same edge.
Thorough numerical simulations and a real data example are provided to back up
our results.
Mining Combined Causes in Large Data Sets
Authors: Saisai Ma, Jiuyong Li, Lin Liu, Thuc Duy Le
Date: 2015-08-28 00:00:00
Tasks: Causal Discovery, Computational Efficiency

In recent years, many methods have been developed for detecting causal
relationships in observational data. Some of them have the potential to tackle
large data sets. However, these methods fail to discover a combined cause, i.e.
a multi-factor cause consisting of two or more component variables which
individually are not causes. A straightforward approach to uncovering a
combined cause is to include both individual and combined variables in the
causal discovery using existing methods, but this scheme is computationally
infeasible due to the huge number of combined variables. In this paper, we
propose a novel approach to address this practical causal discovery problem,
i.e. mining combined causes in large data sets. The experiments with both
synthetic and real world data sets show that the proposed method can obtain
high-quality causal discoveries with a high computational efficiency.
Filtrated Spectral Algebraic Subspace Clustering
Authors: Manolis C. Tsakiris, Rene Vidal
Date: 2015-10-15 00:00:00
Tasks: Clustering

Algebraic Subspace Clustering (ASC) is a simple and elegant method based on
polynomial fitting and differentiation for clustering noiseless data drawn from
an arbitrary union of subspaces. In practice, however, ASC is limited to
equi-dimensional subspaces because the estimation of the subspace dimension via
algebraic methods is sensitive to noise. This paper proposes a new ASC
algorithm that can handle noisy data drawn from subspaces of arbitrary
dimensions. The key ideas are (1) to construct, at each point, a decreasing
sequence of subspaces containing the subspace passing through that point; (2)
to use the distances from any other point to each subspace in the sequence to
construct a subspace clustering affinity, which is superior to alternative
affinities both in theory and in practice. Experiments on the Hopkins 155
dataset demonstrate the superiority of the proposed method with respect to
sparse and low rank subspace clustering methods.
Sketch-based Manga Retrieval using Manga109 Dataset
Authors: Yusuke Matsui, Kota Ito, Yuji Aramaki, Toshihiko Yamasaki, Kiyoharu Aizawa
Date: 2015-10-15 00:00:00
Tasks: Quantization, Retrieval, TAG

Manga (Japanese comics) are popular worldwide. However, current e-manga
archives offer very limited search support, including keyword-based search by
title or author, or tag-based categorization. To make the manga search
experience more intuitive, efficient, and enjoyable, we propose a content-based
manga retrieval system. First, we propose a manga-specific image-describing
framework. It consists of efficient margin labeling, edge orientation histogram
feature description, and approximate nearest-neighbor search using product
quantization. Second, we propose a sketch-based interface as a natural way to
interact with manga content. The interface provides sketch-based querying,
relevance feedback, and query retouch. For evaluation, we built a novel dataset
of manga images, Manga109, which consists of 109 comic books of 21,142 pages
drawn by professional manga artists. To the best of our knowledge, Manga109 is
currently the biggest dataset of manga images available for research. We
conducted a comparative study, a localization evaluation, and a large-scale
qualitative study. From the experiments, we verified that: (1) the retrieval
accuracy of the proposed method is higher than those of previous methods; (2)
the proposed method can localize an object instance with reasonable runtime and
accuracy; and (3) sketch querying is useful for manga search.
Robust Learning for Optimal Treatment Decision with NP-Dimensionality
Authors: Chengchun Shi, Rui Song, Wenbin Lu
Date: 2015-10-15 00:00:00

In order to identify important variables that are involved in making optimal
treatment decision, Lu et al. (2013) proposed a penalized least squared
regression framework for a fixed number of predictors, which is robust against
the misspecification of the conditional mean model. Two problems arise: (i) in
a world of explosively big data, effective methods are needed to handle
ultra-high dimensional data set, for example, with the dimension of predictors
is of the non-polynomial (NP) order of the sample size; (ii) both the
propensity score and conditional mean models need to be estimated from data
under NP dimensionality.
  In this paper, we propose a two-step estimation procedure for deriving the
optimal treatment regime under NP dimensionality. In both steps, penalized
regressions are employed with the non-concave penalty function, where the
conditional mean model of the response given predictors may be misspecified.
The asymptotic properties, such as weak oracle properties, selection
consistency and oracle distributions, of the proposed estimators are
investigated. In addition, we study the limiting distribution of the estimated
value function for the obtained optimal treatment regime. The empirical
performance of the proposed estimation method is evaluated by simulations and
an application to a depression dataset from the STAR*D study.
Batch Bayesian Optimization via Local Penalization
Authors: Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence
Date: 2015-05-29 00:00:00
Tasks: Bayesian Optimisation, Bayesian Optimization, Efficient Exploration, Gaussian Processes

The popularity of Bayesian optimization methods for efficient exploration of
parameter spaces has lead to a series of papers applying Gaussian processes as
surrogates in the optimization of functions. However, most proposed approaches
only allow the exploration of the parameter space to occur sequentially. Often,
it is desirable to simultaneously propose batches of parameter values to
explore. This is particularly the case when large parallel processing
facilities are available. These facilities could be computational or physical
facets of the process being optimized. E.g. in biological experiments many
experimental set ups allow several samples to be simultaneously processed.
Batch methods, however, require modeling of the interaction between the
evaluations in the batch, which can be expensive in complex scenarios. We
investigate a simple heuristic based on an estimate of the Lipschitz constant
that captures the most important aspect of this interaction (i.e. local
repulsion) at negligible computational overhead. The resulting algorithm
compares well, in running time, with much more elaborate alternatives. The
approach assumes that the function of interest, $f$, is a Lipschitz continuous
function. A wrap-loop around the acquisition function is used to collect
batches of points of certain size minimizing the non-parallelizable
computational effort. The speed-up of our method with respect to previous
approaches is significant in a set of computationally expensive experiments.
Group-Invariant Subspace Clustering
Authors: Shuchin Aeron, Eric Kernfeld
Date: 2015-10-15 00:00:00
Tasks: Clustering

In this paper we consider the problem of group invariant subspace clustering
where the data is assumed to come from a union of group-invariant subspaces of
a vector space, i.e. subspaces which are invariant with respect to action of a
given group. Algebraically, such group-invariant subspaces are also referred to
as submodules. Similar to the well known Sparse Subspace Clustering approach
where the data is assumed to come from a union of subspaces, we analyze an
algorithm which, following a recent work [1], we refer to as Sparse Sub-module
Clustering (SSmC). The method is based on finding group-sparse
self-representation of data points. In this paper we primarily derive general
conditions under which such a group-invariant subspace identification is
possible. In particular we extend the geometric analysis in [2] and in the
process we identify a related problem in geometric functional analysis.
Tumor Motion Tracking in Liver Ultrasound Images Using Mean Shift and Active Contour
Authors: Jalil Rasekhi
Date: 2015-09-01 00:00:00

In this paper we present a new method for motion tracking of tumors in liver
ultrasound image sequences. Our algorithm has two main steps. In the first
step, we apply mean shift algorithm with multiple features to estimate the
center of the target in each frame. Target in the first frame is defined using
an ellipse. Edge, texture, and intensity features are extracted from the first
frame, and then mean shift algorithm is applied to each feature separately to
find the center of ellipse related to that feature in the next frame. The
center of ellipse will be the weighted average of these centers. By using mean
shift actually we estimate the target movement between two consecutive frames.
Once the correct ellipsoid in each frame is known, in the second step we apply
the Dynamic Directional Gradient Vector Flow (DDGVF) version of active contour
models, in order to find the correct boundary of tumors. We sample a few points
on the boundary of active contour then translate those points based on the
translation of the center of ellipsoid in two consecutive frames to determine
the target movement. We use these translated sample points as an initial guess
for active contour in the next frame. Our experimental results show that, the
suggested method provides a reliable performance for liver tumor tracking in
ultrasound image sequences.