pwc-archive/papers-with-abstracts huggingface dataset:
Universal Approximation Using Shuffled Linear Models
Authors: Laurens Bliek
Date: 2013-08-29 00:00:00

This paper proposes a specific type of Local Linear Model, the Shuffled
Linear Model (SLM), that can be used as a universal approximator. Local
operating points are chosen randomly and linear models are used to approximate
a function or system around these points. The model can also be interpreted as
an extension to Extreme Learning Machines with Radial Basis Function nodes, or
as a specific way of using Takagi-Sugeno fuzzy models. Using the available
theory of Extreme Learning Machines, universal approximation of the SLM and an
upper bound on the number of models are proved mathematically, and an efficient
algorithm is proposed.
Image enhancement using fusion by wavelet transform and laplacian pyramid
Authors: S. M. Mukane, Y. S. Ghodake, P. S. Khandagle
Date: 2013-11-19 00:00:00
Tasks: Image Enhancement

The idea of combining multiple image modalities to provide a single, enhanced
image is well established different fusion methods have been proposed in
literature. This paper is based on image fusion using laplacian pyramid and
wavelet transform method. Images of same size are used for experimentation.
Images used for the experimentation are standard images and averaging filter is
used of equal weights in original images to burl. Performance of image fusion
technique is measured by mean square error, normalized absolute error and peak
signal to noise ratio. From the performance analysis it has been observed that
MSE is decreased in case of both the methods where as PSNR increased, NAE
decreased in case of laplacian pyramid where as constant for wavelet transform
method.
Stochastic gradient descent on Riemannian manifolds
Authors: Silvere Bonnabel
Date: 2011-11-22 00:00:00
Tasks: Riemannian optimization

Stochastic gradient descent is a simple approach to find the local minima of
a cost function whose evaluations are corrupted by noise. In this paper, we
develop a procedure extending stochastic gradient descent algorithms to the
case where the function is defined on a Riemannian manifold. We prove that, as
in the Euclidian case, the gradient descent algorithm converges to a critical
point of the cost function. The algorithm has numerous potential applications,
and is illustrated here by four examples. In particular a novel gossip
algorithm on the set of covariance matrices is derived and tested numerically.
Face Verification Using Kernel Principle Component Analysis
Authors: V. Karthikeyan, Manjupriya, C. K. Chithra, M. Divya
Date: 2013-11-19 00:00:00
Tasks: Face Detection, Face Recognition, Face Verification

In the beginning stage, face verification is done using easy method of geometric algorithm models, but the verification route has now developed into a scientific progress of complicated geometric representation and matching process. In modern time the skill have enhanced face detection system into the vigorous focal point. Researchers currently undergoing strong research on finding face recognition system for wider area information taken under hysterical elucidation dissimilarity. The proposed face recognition system consists of a narrative exposition indiscreet preprocessing method, a hybrid Fourier-based facial feature extraction and a score fusion scheme. We take in conventional the face detection in unlike cheer up circumstances and at unusual setting. Image processing, Image detection, Feature removal and Face detection are the methods used for Face Verification System . This paper focuses mainly on the issue of toughness to lighting variations. The proposed system has obtained an average of verification rate on Two-Dimensional images under different lightening conditions.
Face Verification System based on Integral Normalized Gradient Image(INGI)
Authors: V. Karthikeyan, M. Divya, C. K. Chithra, K. Manju Priya
Date: 2013-11-19 00:00:00
Tasks: Face Verification

Character identification plays a vital role in the contemporary world of Image processing. It can solve many composite problems and makes humans work easier. An instance is Handwritten Character detection. Handwritten recognition is not a novel expertise, but it has not gained community notice until Now. The eventual aim of designing Handwritten Character recognition structure with an accurateness rate of 100% is pretty illusionary. Tamil Handwritten Character recognition system uses the Neural Networks to distinguish them. Neural Network and structural characteristics are used to instruct and recognize written characters. After training and testing the exactness rate reached 99%. This correctness rate is extremely high. In this paper we are exploring image processing through the Hilditch algorithm foundation and structural characteristics of a character in the image. And we recognized some character of the Tamil language, and we are trying to identify all the character of Tamil In our future works.
Nonparametric Bayes dynamic modeling of relational data
Authors: Daniele Durante, David B. Dunson
Date: 2013-11-19 00:00:00
Tasks: Data Augmentation, Gaussian Processes

Symmetric binary matrices representing relations among entities are commonly
collected in many areas. Our focus is on dynamically evolving binary relational
matrices, with interest being in inference on the relationship structure and
prediction. We propose a nonparametric Bayesian dynamic model, which reduces
dimensionality in characterizing the binary matrix through a lower-dimensional
latent space representation, with the latent coordinates evolving in continuous
time via Gaussian processes. By using a logistic mapping function from the
probability matrix space to the latent relational space, we obtain a flexible
and computational tractable formulation. Employing P\`olya-Gamma data
augmentation, an efficient Gibbs sampler is developed for posterior
computation, with the dimension of the latent space automatically inferred. We
provide some theoretical results on flexibility of the model, and illustrate
performance via simulation experiments. We also consider an application to
co-movements in world financial markets.
Reasoning about the Impacts of Information Sharing
Authors: Chatschik Bisdikian, Federico Cerutti, Yuqing Tang, Nir Oren
Date: 2013-11-19 00:00:00

In this paper we describe a decision process framework allowing an agent to
decide what information it should reveal to its neighbours within a
communication graph in order to maximise its utility. We assume that these
neighbours can pass information onto others within the graph. The inferences
made by agents receiving the messages can have a positive or negative impact on
the information providing agent, and our decision process seeks to identify how
a message should be modified in order to be most beneficial to the information
producer. Our decision process is based on the provider's subjective beliefs
about others in the system, and therefore makes extensive use of the notion of
trust. Our core contributions are therefore the construction of a model of
information propagation; the description of the agent's decision procedure; and
an analysis of some of its properties.
Subjective Logic Operators in Trust Assessment: an Empirical Study
Authors: Federico Cerutti, Alice Toniolo, Nir Oren, Timothy J. Norman
Date: 2013-11-19 00:00:00

Computational trust mechanisms aim to produce trust ratings from both direct
and indirect information about agents' behaviour. Subjective Logic (SL) has
been widely adopted as the core of such systems via its fusion and discount
operators. In recent research we revisited the semantics of these operators to
explore an alternative, geometric interpretation. In this paper we present a
principled desiderata for discounting and fusion operators in SL. Building upon
this we present operators that satisfy these desirable properties, including a
family of discount operators. We then show, through a rigorous empirical study,
that specific, geometrically interpreted operators significantly outperform
standard SL operators in estimating ground truth. These novel operators offer
real advantages for computational models of trust and reputation, in which they
may be employed without modifying other aspects of an existing system.
Analysis of Farthest Point Sampling for Approximating Geodesics in a Graph
Authors: Pegah Kamousi, Sylvain Lazard, Anil Maheshwari, Stefanie Wuhrer
Date: 2013-11-19 00:00:00

A standard way to approximate the distance between any two vertices $p$ and
$q$ on a mesh is to compute, in the associated graph, a shortest path from $p$
to $q$ that goes through one of $k$ sources, which are well-chosen vertices.
Precomputing the distance between each of the $k$ sources to all vertices of
the graph yields an efficient computation of approximate distances between any
two vertices. One standard method for choosing $k$ sources, which has been used
extensively and successfully for isometry-invariant surface processing, is the
so-called Farthest Point Sampling (FPS), which starts with a random vertex as
the first source, and iteratively selects the farthest vertex from the already
selected sources.
  In this paper, we analyze the stretch factor $\mathcal{F}_{FPS}$ of
approximate geodesics computed using FPS, which is the maximum, over all pairs
of distinct vertices, of their approximated distance over their geodesic
distance in the graph. We show that $\mathcal{F}_{FPS}$ can be bounded in terms
of the minimal value $\mathcal{F}^*$ of the stretch factor obtained using an
optimal placement of $k$ sources as $\mathcal{F}_{FPS}\leq 2 r_e^2
\mathcal{F}^*+ 2 r_e^2 + 8 r_e + 1$, where $r_e$ is the ratio of the lengths of
the longest and the shortest edges of the graph. This provides some evidence
explaining why farthest point sampling has been used successfully for
isometry-invariant shape processing. Furthermore, we show that it is
NP-complete to find $k$ sources that minimize the stretch factor.
Hilditchs Algorithm Based Tamil Character Recognition
Authors: V. Karthikeyan
Date: 2013-11-19 00:00:00

Character identification plays a vital role in the contemporary world of
Image processing. It can solve many composite problems and makes humans work
easier. An instance is Handwritten Character detection. Handwritten recognition
is not a novel expertise, but it has not gained community notice until Now. The
eventual aim of designing Handwritten Character recognition structure with an
accurateness rate of 100% is pretty illusionary. Tamil Handwritten Character
recognition system uses the Neural Networks to distinguish them. Neural Network
and structural characteristics are used to instruct and recognize written
characters. After training and testing the exactness rate reached 99%. This
correctness rate is extremely high. In this paper we are exploring image
processing through the Hilditch algorithm foundation and structural
characteristics of a character in the image. And we recognized some character
of the Tamil language, and we are trying to identify all the character of Tamil
In our future works.
Near-Optimal Entrywise Sampling for Data Matrices
Authors: Dimitris Achlioptas, Zohar Karnin, Edo Liberty
Date: 2013-11-19 00:00:00
Proceeding: NeurIPS 2013 12

We consider the problem of selecting non-zero entries of a matrix $A$ in
order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$. For
large $m \times n$ matrices, such that $n \gg m$ (for example, representing $n$
observations over $m$ attributes) we give sampling distributions that exhibit
four important properties. First, they have closed forms computable from
minimal information regarding $A$. Second, they allow sketching of matrices
whose non-zeros are presented to the algorithm in arbitrary order as a stream,
with $O(1)$ computation per non-zero. Third, the resulting sketch matrices are
not only sparse, but their non-zero entries are highly compressible. Lastly,
and most importantly, under mild assumptions, our distributions are provably
competitive with the optimal offline distribution. Note that the probabilities
in the optimal offline distribution may be complex functions of all the entries
in the matrix. Therefore, regardless of computational complexity, the optimal
distribution might be impossible to compute in the streaming model.
Post-Proceedings of the First International Workshop on Learning and Nonmonotonic Reasoning
Authors: Katsumi Inoue, Chiaki Sakama
Date: 2013-11-19 00:00:00
Tasks: BIG-bench Machine Learning, Inductive Learning, Inductive logic programming, Negation, Relational Reasoning

Knowledge Representation and Reasoning and Machine Learning are two important
fields in AI. Nonmonotonic logic programming (NMLP) and Answer Set Programming
(ASP) provide formal languages for representing and reasoning with commonsense
knowledge and realize declarative problem solving in AI. On the other side,
Inductive Logic Programming (ILP) realizes Machine Learning in logic
programming, which provides a formal background to inductive learning and the
techniques have been applied to the fields of relational learning and data
mining. Generally speaking, NMLP and ASP realize nonmonotonic reasoning while
lack the ability of learning. By contrast, ILP realizes inductive learning
while most techniques have been developed under the classical monotonic logic.
With this background, some researchers attempt to combine techniques in the
context of nonmonotonic ILP. Such combination will introduce a learning
mechanism to programs and would exploit new applications on the NMLP side,
while on the ILP side it will extend the representation language and enable us
to use existing solvers. Cross-fertilization between learning and nonmonotonic
reasoning can also occur in such as the use of answer set solvers for ILP,
speed-up learning while running answer set solvers, learning action theories,
learning transition rules in dynamical systems, abductive learning, learning
biological networks with inhibition, and applications involving default and
negation. This workshop is the first attempt to provide an open forum for the
identification of problems and discussion of possible collaborations among
researchers with complementary expertise. The workshop was held on September
15th of 2013 in Corunna, Spain. This post-proceedings contains five technical
papers (out of six accepted papers) and the abstract of the invited talk by Luc
De Raedt.
Bandits with Switching Costs: T^{2/3} Regret
Authors: Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres
Date: 2013-10-11 00:00:00

We study the adversarial multi-armed bandit problem in a setting where the
player incurs a unit cost each time he switches actions. We prove that the
player's $T$-round minimax regret in this setting is
$\widetilde{\Theta}(T^{2/3})$, thereby closing a fundamental gap in our
understanding of learning with bandit feedback. In the corresponding
full-information version of the problem, the minimax regret is known to grow at
a much slower rate of $\Theta(\sqrt{T})$. The difference between these two
rates provides the \emph{first} indication that learning with bandit feedback
can be significantly harder than learning with full-information feedback
(previous results only showed a different dependence on the number of actions,
but not on $T$.)
  In addition to characterizing the inherent difficulty of the multi-armed
bandit problem with switching costs, our results also resolve several other
open problems in online learning. One direct implication is that learning with
bandit feedback against bounded-memory adaptive adversaries has a minimax
regret of $\widetilde{\Theta}(T^{2/3})$. Another implication is that the
minimax regret of online learning in adversarial Markov decision processes
(MDPs) is $\widetilde{\Theta}(T^{2/3})$. The key to all of our results is a new
randomized construction of a multi-scale random walk, which is of independent
interest and likely to prove useful in additional settings.
Guaranteed clustering and biclustering via semidefinite programming
Authors: Brendan P. W. Ames
Date: 2012-02-16 00:00:00
Tasks: Clustering

Identifying clusters of similar objects in data plays a significant role in a
wide range of applications. As a model problem for clustering, we consider the
densest k-disjoint-clique problem, whose goal is to identify the collection of
k disjoint cliques of a given weighted complete graph maximizing the sum of the
densities of the complete subgraphs induced by these cliques. In this paper, we
establish conditions ensuring exact recovery of the densest k cliques of a
given graph from the optimal solution of a particular semidefinite program. In
particular, the semidefinite relaxation is exact for input graphs corresponding
to data consisting of k large, distinct clusters and a smaller number of
outliers. This approach also yields a semidefinite relaxation for the
biclustering problem with similar recovery guarantees. Given a set of objects
and a set of features exhibited by these objects, biclustering seeks to
simultaneously group the objects and features according to their expression
levels. This problem may be posed as partitioning the nodes of a weighted
bipartite complete graph such that the sum of the densities of the resulting
bipartite complete subgraphs is maximized. As in our analysis of the densest
k-disjoint-clique problem, we show that the correct partition of the objects
and features can be recovered from the optimal solution of a semidefinite
program in the case that the given data consists of several disjoint sets of
objects exhibiting similar features. Empirical evidence from numerical
experiments supporting these theoretical guarantees is also provided.
On Nonrigid Shape Similarity and Correspondence
Authors: Alon Shtern, Ron Kimmel
Date: 2013-11-18 00:00:00

An important operation in geometry processing is finding the correspondences
between pairs of shapes. The Gromov-Hausdorff distance, a measure of
dissimilarity between metric spaces, has been found to be highly useful for
nonrigid shape comparison. Here, we explore the applicability of related shape
similarity measures to the problem of shape correspondence, adopting spectral
type distances. We propose to evaluate the spectral kernel distance, the
spectral embedding distance and the novel spectral quasi-conformal distance,
comparing the manifolds from different viewpoints. By matching the shapes in
the spectral domain, important attributes of surface structure are being
aligned. For the purpose of testing our ideas, we introduce a fully automatic
framework for finding intrinsic correspondence between two shapes. The proposed
method achieves state-of-the-art results on the Princeton isometric shape
matching protocol applied, as usual, to the TOSCA and SCAPE benchmarks.
Planning by case-based reasoning based on fuzzy logic
Authors: Baghdad Atmani, Sofia Benbelkacem, Mohamed Benamina
Date: 2013-11-18 00:00:00
Tasks: Graph Classification, Retrieval

The treatment of complex systems often requires the manipulation of vague,
imprecise and uncertain information. Indeed, the human being is competent in
handling of such systems in a natural way. Instead of thinking in mathematical
terms, humans describes the behavior of the system by language proposals. In
order to represent this type of information, Zadeh proposed to model the
mechanism of human thought by approximate reasoning based on linguistic
variables. He introduced the theory of fuzzy sets in 1965, which provides an
interface between language and digital worlds. In this paper, we propose a
Boolean modeling of the fuzzy reasoning that we baptized Fuzzy-BML and uses the
characteristics of induction graph classification. Fuzzy-BML is the process by
which the retrieval phase of a CBR is modelled not in the conventional form of
mathematical equations, but in the form of a database with membership functions
of fuzzy rules.
Early Stage Influenza Detection from Twitter
Authors: Jiwei Li, Claire Cardie
Date: 2013-09-27 00:00:00

Influenza is an acute respiratory illness that occurs virtually every year
and results in substantial disease, death and expense. Detection of Influenza
in its earliest stage would facilitate timely action that could reduce the
spread of the illness. Existing systems such as CDC and EISS which try to
collect diagnosis data, are almost entirely manual, resulting in about two-week
delays for clinical data acquisition. Twitter, a popular microblogging service,
provides us with a perfect source for early-stage flu detection due to its
real- time nature. For example, when a flu breaks out, people that get the flu
may post related tweets which enables the detection of the flu breakout
promptly. In this paper, we investigate the real-time flu detection problem on
Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal
unsupervised Bayesian algorithm based on a 4 phase Markov Network, trying to
identify the flu breakout at the earliest stage. We test our model on real
Twitter datasets from the United States along with baselines in multiple
applications, such as real-time flu breakout detection, future epidemic phase
prediction, or Influenza-like illness (ILI) physician visits. Experimental
results show the robustness and effectiveness of our approach. We build up a
real time flu reporting system based on the proposed approach, and we are
hopeful that it would help government or health organizations in identifying
flu outbreaks and facilitating timely actions to decrease unnecessary
mortality.
A message-passing algorithm for multi-agent trajectory planning
Authors: Jose Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan Yedidia
Date: 2013-11-18 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Motion Planning, Trajectory Planning

We describe a novel approach for computing collision-free \emph{global}
trajectories for $p$ agents with specified initial and final configurations,
based on an improved version of the alternating direction method of multipliers
(ADMM). Compared with existing methods, our approach is naturally
parallelizable and allows for incorporating different cost functionals with
only minor adjustments. We apply our method to classical challenging instances
and observe that its computational requirements scale well with $p$ for several
cost functionals. We also show that a specialization of our algorithm can be
used for {\em local} motion planning by solving the problem of joint
optimization in velocity space.
From Maxout to Channel-Out: Encoding Information on Sparse Pathways
Authors: Qi. Wang, Joseph JaJa
Date: 2013-11-18 00:00:00
Tasks: General Classification, image-classification, Image Classification

Motivated by an important insight from neural science, we propose a new
framework for understanding the success of the recently proposed "maxout"
networks. The framework is based on encoding information on sparse pathways and
recognizing the correct pathway at inference time. Elaborating further on this
insight, we propose a novel deep network architecture, called "channel-out"
network, which takes a much better advantage of sparse pathway encoding. In
channel-out networks, pathways are not only formed a posteriori, but they are
also actively selected according to the inference outputs from the lower
layers. From a mathematical perspective, channel-out networks can represent a
wider class of piece-wise continuous functions, thereby endowing the network
with more expressive power than that of maxout networks. We test our
channel-out networks on several well-known image classification benchmarks,
setting new state-of-the-art performance on CIFAR-100 and STL-10, which
represent some of the "harder" image classification benchmarks.