pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Restless Multi-armed Bandit Game and Swarm Intelligence Effect
Authors: Shunsuke Yoshida, Masato Hisakado, Shintaro Mori
Date: 2015-03-13 00:00:00

We obtain the conditions for the emergence of the swarm intelligence effect
in an interactive game of restless multi-armed bandit (rMAB). A player competes
with multiple agents. Each bandit has a payoff that changes with a probability
$p_{c}$ per round. The agents and player choose one of three options: (1)
Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among
$n_{I}$ randomly chosen bandits), and (3) Observe (social learning for a good
bandit). Each agent has two parameters $(c,p_{obs})$ to specify the decision:
(i) $c$, the threshold value for Exploit, and (ii) $p_{obs}$, the probability
for Observe in learning. The parameters $(c,p_{obs})$ are uniformly
distributed. We determine the optimal strategies for the player using complete
knowledge about the rMAB. We show whether or not social or asocial learning is
more optimal in the $(p_{c},n_{I})$ space and define the swarm intelligence
effect. We conduct a laboratory experiment (67 subjects) and observe the swarm
intelligence effect only if $(p_{c},n_{I})$ are chosen so that social learning
is far more optimal than asocial learning.
An Adaptive Online HDP-HMM for Segmentation and Classification of Sequential Data
Authors: Ava Bargi, Richard Yi Da Xu, Massimo Piccardi
Date: 2015-03-10 00:00:00
Tasks: General Classification

In the recent years, the desire and need to understand sequential data has
been increasing, with particular interest in sequential contexts such as
patient monitoring, understanding daily activities, video surveillance, stock
market and the like. Along with the constant flow of data, it is critical to
classify and segment the observations on-the-fly, without being limited to a
rigid number of classes. In addition, the model needs to be capable of updating
its parameters to comply with possible evolutions. This interesting problem,
however, is not adequately addressed in the literature since many studies focus
on offline classification over a pre-defined class set. In this paper, we
propose a principled solution to this gap by introducing an adaptive online
system based on Markov switching models with hierarchical Dirichlet process
priors. This infinite adaptive online approach is capable of segmenting and
classifying the sequential data over unlimited number of classes, while meeting
the memory and delay constraints of streaming contexts. The model is further
enhanced by introducing a learning rate, responsible for balancing the extent
to which the model sustains its previous learning (parameters) or adapts to the
new streaming observations. Experimental results on several variants of
stationary and evolving synthetic data and two video datasets, TUM Assistive
Kitchen and collatedWeizmann, show remarkable performance in segmentation and
classification, particularly for evolutionary sequences with changing
distributions and/or containing new, unseen classes.
Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA domain
Authors: Sabyasachi Mukhopadhyay, Soham Mandal, Nandan K. Das, Subhadip Dey, Asish Mitra, Nirmalya Ghosh, Prasanta K. Panigrahi
Date: 2015-03-12 00:00:00

CT scan images of human brain of a particular patient in different cross
sections are taken, on which wavelet transform and multi-fractal analysis are
applied. The vertical and horizontal unfolding of images are done before
analyzing these images. A systematic investigation of de-noised CT scan images
of human brain in different cross-sections are carried out through wavelet
normalized energy and wavelet semi-log plots, which clearly points out the
mismatch between results of vertical and horizontal unfolding. The mismatch of
results confirms the heterogeneity in spatial domain. Using the multi-fractal
de-trended fluctuation analysis (MFDFA), the mismatch between the values of
Hurst exponent and width of singularity spectrum by vertical and horizontal
unfolding confirms the same.
Detecting Overlapping Communities in Networks Using Spectral Methods
Authors: Yuan Zhang, Elizaveta Levina, Ji Zhu
Date: 2014-12-10 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

Community detection is a fundamental problem in network analysis which is
made more challenging by overlaps between communities which often occur in
practice. Here we propose a general, flexible, and interpretable generative
model for overlapping communities, which can be thought of as a generalization
of the degree-corrected stochastic block model. We develop an efficient
spectral algorithm for estimating the community memberships, which deals with
the overlaps by employing the K-medians algorithm rather than the usual K-means
for clustering in the spectral domain. We show that the algorithm is
asymptotically consistent when networks are not too sparse and the overlaps
between communities not too large. Numerical experiments on both simulated
networks and many real social networks demonstrate that our method performs
very well compared to a number of benchmark methods for overlapping community
detection.
Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation
Authors: Eneko Agirre, Ander Barrena, Aitor Soroa
Date: 2015-03-05 00:00:00
Tasks: Entity Disambiguation

Hyperlinks and other relations in Wikipedia are a extraordinary resource
which is still not fully understood. In this paper we study the different types
of links in Wikipedia, and contrast the use of the full graph with respect to
just direct links. We apply a well-known random walk algorithm on two tasks,
word relatedness and named-entity disambiguation. We show that using the full
graph is more effective than just direct links by a large margin, that
non-reciprocal links harm performance, and that there is no benefit from
categories and infoboxes, with coherent results on both tasks. We set new
state-of-the-art figures for systems based on Wikipedia links, comparable to
systems exploiting several information sources and/or supervised machine
learning. Our approach is open source, with instruction to reproduce results,
and amenable to be integrated with complementary text-based methods.
Approximating Sparse PCA from Incomplete Data
Authors: Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail
Date: 2015-03-12 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Math

We study how well one can recover sparse principal components of a data
matrix using a sketch formed from a few of its elements. We show that for a
wide class of optimization problems, if the sketch is close (in the spectral
norm) to the original data matrix, then one can recover a near optimal solution
to the optimization problem by using the sketch. In particular, we use this
approach to obtain sparse principal components and show that for \math{m} data
points in \math{n} dimensions, \math{O(\epsilon^{-2}\tilde k\max\{m,n\})}
elements gives an \math{\epsilon}-additive approximation to the sparse PCA
problem (\math{\tilde k} is the stable rank of the data matrix). We demonstrate
our algorithms extensively on image, text, biological and financial data. The
results show that not only are we able to recover the sparse PCAs from the
incomplete data, but by using our sparse sketch, the running time drops by a
factor of five or more.
Compact Nonlinear Maps and Circulant Extensions
Authors: Felix X. Yu, Sanjiv Kumar, Henry Rowley, Shih-Fu Chang
Date: 2015-03-12 00:00:00

Kernel approximation via nonlinear random feature maps is widely used in
speeding up kernel machines. There are two main challenges for the conventional
kernel approximation methods. First, before performing kernel approximation, a
good kernel has to be chosen. Picking a good kernel is a very challenging
problem in itself. Second, high-dimensional maps are often required in order to
achieve good performance. This leads to high computational cost in both
generating the nonlinear maps, and in the subsequent learning and prediction
process. In this work, we propose to optimize the nonlinear maps directly with
respect to the classification objective in a data-dependent fashion. The
proposed approach achieves kernel approximation and kernel learning in a joint
framework. This leads to much more compact maps without hurting the
performance. As a by-product, the same framework can also be used to achieve
more compact kernel maps to approximate a known kernel. We also introduce
Circulant Nonlinear Maps, which uses a circulant-structured projection matrix
to speed up the nonlinear maps for high-dimensional data.
Qualitative inequalities for squared partial correlations of a Gaussian random vector
Authors: Sanjay Chaudhuri
Date: 2015-03-12 00:00:00
Tasks: Model Selection

We describe various sets of conditional independence relationships,
sufficient for qualitatively comparing non-vanishing squared partial
correlations of a Gaussian random vector. These sufficient conditions are
satisfied by several graphical Markov models. Rules for comparing degree of
association among the vertices of such Gaussian graphical models are also
developed. We apply these rules to compare conditional dependencies on Gaussian
trees. In particular for trees, we show that such dependence can be completely
characterized by the length of the paths joining the dependent vertices to each
other and to the vertices conditioned on. We also apply our results to
postulate rules for model selection for polytree models. Our rules apply to
mutual information of Gaussian random vectors as well.
TILDE: A Temporally Invariant Learned DEtector
Authors: Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit
Date: 2014-11-17 00:00:00
Proceeding: CVPR 2015 6

We introduce a learning-based approach to detect repeatable keypoints under
drastic imaging changes of weather and lighting conditions to which
state-of-the-art keypoint detectors are surprisingly sensitive. We first
identify good keypoint candidates in multiple training images taken from the
same viewpoint. We then train a regressor to predict a score map whose maxima
are those points so that they can be found by simple non-maximum suppression.
As there are no standard datasets to test the influence of these kinds of
changes, we created our own, which we will make publicly available. We will
show that our method significantly outperforms the state-of-the-art methods in
such challenging conditions, while still achieving state-of-the-art performance
on the untrained standard Oxford dataset.
Spectral Clustering for Divide-and-Conquer Graph Matching
Authors: Vince Lyzinski, Daniel L. Sussman, Donniell E. Fishkind, Henry Pao, Li Chen, Joshua T. Vogelstein, Youngser Park, Carey E. Priebe
Date: 2013-10-04 00:00:00
Tasks: Clustering, Graph Embedding, Graph Matching, Stochastic Block Model

We present a parallelized bijective graph matching algorithm that leverages
seeds and is designed to match very large graphs. Our algorithm combines
spectral graph embedding with existing state-of-the-art seeded graph matching
procedures. We justify our approach by proving that modestly correlated, large
stochastic block model random graphs are correctly matched utilizing very few
seeds through our divide-and-conquer procedure. We also demonstrate the
effectiveness of our approach in matching very large graphs in simulated and
real data examples, showing up to a factor of 8 improvement in runtime with
minimal sacrifice in accuracy.
Are there intelligent Turing machines?
Authors: Norbert Bátfai
Date: 2015-03-12 00:00:00
Tasks: Emotional Intelligence

This paper introduces a new computing model based on the cooperation among
Turing machines called orchestrated machines. Like universal Turing machines,
orchestrated machines are also designed to simulate Turing machines but they
can also modify the original operation of the included Turing machines to
create a new layer of some kind of collective behavior. Using this new model we
can define some interested notions related to cooperation ability of Turing
machines such as the intelligence quotient or the emotional intelligence
quotient for Turing machines.
Learning to Detect Vehicles by Clustering Appearance Patterns
Authors: Eshed Ohn-Bar, Mohan M. Trivedi
Date: 2015-03-12 00:00:00
Tasks: Clustering, Diversity, Object, object-detection, Object Detection, vehicle detection

This paper studies efficient means for dealing with intra-category diversity
in object detection. Strategies for occlusion and orientation handling are
explored by learning an ensemble of detection models from visual and
geometrical clusters of object instances. An AdaBoost detection scheme is
employed with pixel lookup features for fast detection. The analysis provides
insight into the design of a robust vehicle detection system, showing promise
in terms of detection performance and orientation estimation accuracy.
Geometry and Expressive Power of Conditional Restricted Boltzmann Machines
Authors: Guido Montufar, Nihat Ay, Keyan Ghazi-Zahedi
Date: 2014-02-14 00:00:00

Conditional restricted Boltzmann machines are undirected stochastic neural
networks with a layer of input and output units connected bipartitely to a
layer of hidden units. These networks define models of conditional probability
distributions on the states of the output units given the states of the input
units, parametrized by interaction weights and biases. We address the
representational power of these models, proving results their ability to
represent conditional Markov random fields and conditional distributions with
restricted supports, the minimal size of universal approximators, the maximal
model approximation errors, and on the dimension of the set of representable
conditional distributions. We contribute new tools for investigating
conditional probability models, which allow us to improve the results that can
be derived from existing work on restricted Boltzmann machine probability
models.
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
Authors: Samir F. Hafez, Mazen M. Selim, Hala H. Zayed
Date: 2015-03-12 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %.
Starting engagement detection towards a companion robot using multimodal features
Authors: Dominique Vaufreydaz, Wafa Johal, Claudine Combe
Date: 2015-03-12 00:00:00

Recognition of intentions is a subconscious cognitive process vital to human
communication. This skill enables anticipation and increases the quality of
interactions between humans. Within the context of engagement, non-verbal
signals are used to communicate the intention of starting the interaction with
a partner. In this paper, we investigated methods to detect these signals in
order to allow a robot to know when it is about to be addressed. Originality of
our approach resides in taking inspiration from social and cognitive sciences
to perform our perception task. We investigate meaningful features, i.e. human
readable features, and elicit which of these are important for recognizing
someone's intention of starting an interaction. Classically, spatial
information like the human position and speed, the human-robot distance are
used to detect the engagement. Our approach integrates multimodal features
gathered using a companion robot equipped with a Kinect. The evaluation on our
corpus collected in spontaneous conditions highlights its robustness and
validates the use of such a technique in a real environment. Experimental
validation shows that multimodal features set gives better precision and recall
than using only spatial and speed features. We also demonstrate that 7 selected
features are sufficient to provide a good starting engagement detection score.
In our last investigation, we show that among our full 99 features set, the
space reduction is not a solved task. This result opens new researches
perspectives on multimodal engagement detection.
Boosting of Image Denoising Algorithms
Authors: Yaniv Romano, Michael Elad
Date: 2015-02-22 00:00:00
Tasks: Denoising, Image Denoising

In this paper we propose a generic recursive algorithm for improving image
denoising methods. Given the initial denoised image, we suggest repeating the
following "SOS" procedure: (i) (S)trengthen the signal by adding the previous
denoised image to the degraded input image, (ii) (O)perate the denoising method
on the strengthened image, and (iii) (S)ubtract the previous denoised image
from the restored signal-strengthened outcome. The convergence of this process
is studied for the K-SVD image denoising and related algorithms. Still in the
context of K-SVD image denoising, we introduce an interesting interpretation of
the SOS algorithm as a technique for closing the gap between the local
patch-modeling and the global restoration task, thereby leading to improved
performance. In a quest for the theoretical origin of the SOS algorithm, we
provide a graph-based interpretation of our method, where the SOS recursive
update effectively minimizes a penalty function that aims to denoise the image,
while being regularized by the graph Laplacian. We demonstrate the SOS boosting
algorithm for several leading denoising methods (K-SVD, NLM, BM3D, and EPLL),
showing tendency to further improve denoising performance.
RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis
Authors: Chinh Dang, Abdolreza Moghadam, Hayder Radha
Date: 2014-05-07 00:00:00

Key frame extraction algorithms consider the problem of selecting a subset of
the most informative frames from a video to summarize its content.
Functional Inverse Regression in an Enlarged Dimension Reduction Space
Authors: Ting-Li Chen, Su-Yun Huang, Yanyuan Ma, I-Ping Tu
Date: 2015-03-12 00:00:00
Tasks: Dimensionality Reduction, regression

We consider an enlarged dimension reduction space in functional inverse
regression. Our operator and functional analysis based approach facilitates a
compact and rigorous formulation of the functional inverse regression problem.
It also enables us to expand the possible space where the dimension reduction
functions belong. Our formulation provides a unified framework so that the
classical notions, such as covariance standardization, Mahalanobis distance,
SIR and linear discriminant analysis, can be naturally and smoothly carried out
in our enlarged space. This enlarged dimension reduction space also links to
the linear discriminant space of Gaussian measures on a separable Hilbert
space.
Combining Language and Vision with a Multimodal Skip-gram Model
Authors: Angeliki Lazaridou, Nghia The Pham, Marco Baroni
Date: 2015-01-12 00:00:00
Proceeding: HLT 2015 5
Tasks: Retrieval

We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed to visual representations of the objects they denote
(extracted from natural images), and must predict linguistic and visual
features jointly. The MMSKIP-GRAM models achieve good performance on a variety
of semantic benchmarks. Moreover, since they propagate visual information to
all words, we use them to improve image labeling and retrieval in the zero-shot
setup, where the test concepts are never seen during model training. Finally,
the MMSKIP-GRAM models discover intriguing visual properties of abstract words,
paving the way to realistic implementations of embodied theories of meaning.
Single image super-resolution by approximated Heaviside functions
Authors: Liang-Jian Deng, Weihong Guo, Ting-Zhu Huang
Date: 2015-03-12 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Image super-resolution is a process to enhance image resolution. It is widely
used in medical imaging, satellite imaging, target recognition, etc. In this
paper, we conduct continuous modeling and assume that the unknown image
intensity function is defined on a continuous domain and belongs to a space
with a redundant basis. We propose a new iterative model for single image
super-resolution based on an observation: an image is consisted of smooth
components and non-smooth components, and we use two classes of approximated
Heaviside functions (AHFs) to represent them respectively. Due to sparsity of
the non-smooth components, a $L_{1}$ model is employed. In addition, we apply
the proposed iterative model to image patches to reduce computation and
storage. Comparisons with some existing competitive methods show the
effectiveness of the proposed method.
On the Impossibility of Learning the Missing Mass
Authors: Elchanan Mossel, Mesrob I. Ohannessian
Date: 2015-03-12 00:00:00

This paper shows that one cannot learn the probability of rare events without
imposing further structural assumptions. The event of interest is that of
obtaining an outcome outside the coverage of an i.i.d. sample from a discrete
distribution. The probability of this event is referred to as the "missing
mass". The impossibility result can then be stated as: the missing mass is not
distribution-free PAC-learnable in relative error. The proof is
semi-constructive and relies on a coupling argument using a dithered geometric
distribution. This result formalizes the folklore that in order to predict rare
events, one necessarily needs distributions with "heavy tails".
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram
Authors: Nagaraja S., Prabhakar C. J.
Date: 2015-03-12 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper, we present a novel approach for image retrieval based on
extraction of low level features using techniques such as Directional Binary
Code, Haar Wavelet transform and Histogram of Oriented Gradients. The DBC
texture descriptor captures the spatial relationship between any pair of
neighbourhood pixels in a local region along a given direction, while Local
Binary Patterns descriptor considers the relationship between a given pixel and
its surrounding neighbours. Therefore, DBC captures more spatial information
than LBP and its variants, also it can extract more edge information than LBP.
Hence, we employ DBC technique in order to extract grey level texture feature
from each RGB channels individually and computed texture maps are further
combined which represents colour texture features of an image. Then, we
decomposed the extracted colour texture map and original image using Haar
wavelet transform. Finally, we encode the shape and local features of wavelet
transformed images using Histogram of Oriented Gradients for content based
image retrieval. The performance of proposed method is compared with existing
methods on two databases such as Wang's corel image and Caltech 256. The
evaluation results show that our approach outperforms the existing methods for
image retrieval.