Connectionists: NIPS 2014 Workshop -- Riemannian Geometry in Machine Learning, Statistics, and Computer Vision

[Vittorio Murino]

Apologize for multiple posting
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RIEMANNIAN GEOMETRY @ NIPS2014 -- Call for Workshop Participation

RIEMANNIAN GEOMETRY IN MACHINE LEARNING,  STATISTICS, AND COMPUTER VISION
Saturday December 13, 2014
Palais des Congrès, Montreal, Canada

This workshop will be held in conjunction with Neural Information 
Processing Systems NIPS 2014

http://www.riemanniangeometry2014.eu/

*** Look at the workshop program !!!
http://riemanniangeometry2014.eu/index.php/schedule


OVERVIEW
Traditional machine learning and data analysis methods often assume that 
the input data can be represented by vectors in Euclidean space.
While this assumption has worked well for many applications, researchers 
have increasingly realized that if the data is intrinsically non-Euclidean,
ignoring this geometrical structure can lead to suboptimal results.

In the existing literature, there are two common approaches for 
exploiting data geometry when the data is assumed to lie on a Riemannian 
manifold.
In the first direction, often referred to as manifold learning, the data 
is assumed to lie on an unknown Riemannian manifold and the structure of 
this manifold is exploited through the training data, either labeled or 
unlabeled. Examples of manifold learning techniques include Manifold 
Regularization via the graph Laplacian,
Locally Linear Embedding, and Isometric Mapping.

In the second direction, which is gaining increasing importance and 
success, the Riemannian manifold representing the input data is assumed 
to be known explicitly. Some manifolds that have been widely used for 
data representation are: the manifold of symmetric, positive definite 
matrices, the Grassmannian manifold of subspaces of a vector space, and 
the Kendall manifold of shapes. When the manifold is known, the full 
power of the mathematical theory of Riemannian geometry can be exploited 
in both the formulation of algorithms as well as their theoretical analysis.
Successful applications of these approaches are numerous and range from 
brain imaging and low rank matrix completion to computer vision tasks 
such as object detection and tracking.

This workshop focuses on the latter direction. We aim to bring together 
researchers in statistics, machine learning, computer vision, and other 
areas, to discuss and exchange current state of the art results, both 
theoretically and computationally, and identify potential future 
research directions.


INVITER SPEAKERS
* Thomas Fletcher, University of Utah
* Mark Girolami/Michael Betancourt, University of Warwick
* Richard Hartley, Australian National University/NICTA
* Anuj Srivastava, Florida State University
* Bart Vandereycken, Princeton University


FORMAT
The workshop will consist of invited talks only, followed by a panel 
discussion together with the speakers.
Specifically, we plan to have three invited oral presentations in the 
morning and two in the afternoon, each lasting 45 minutes plus questions,
about one hour in total. At the end of the talks in the afternoon, a 
panel and open discussion forum will be held, lasting about one hour.
All invited speakers will participate in the panel discussion, which 
will be mediated by the workshop organizers.
Two half-hour periods, one in the morning and one in the afternoon, will 
be allocated for breaks and informal discussions.


ORGANIZING COMMITTEE
Minh Ha Quang - Pattern Analysis & Computer Vision (PAVIS), Istituto 
Italiano di Tecnologia, Genova, Italy
Vikas Sindhwani - IBM T.J. Watson Research Center, New York
Vittorio Murino - Pattern Analysis & Computer Vision (PAVIS), Istituto 
Italiano di Tecnologia, Genova, and University of Verona, Italy

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-- 
Vittorio Murino

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Prof. Vittorio Murino, Ph.D.
PAVIS - Pattern Analysis & Computer Vision

IIT Istituto Italiano di Tecnologia
Via Morego 30
16163 Genova, Italy

Phone:	+39 010 71781 504
Mobile: +39 329 6508554
Fax: +39 010 71781 236
E-mail:vittorio.murino at iit.it

Secretary: Sara Curreli
email:	sara.curreli at iit.it
Phone: 	+39 010 71781 917

http://www.iit.it/pavis
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