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Hello, Could you please distribute this announcement through
Comp-geom mailing list ? Thank you, Fred *********************************************************************** Please apologize for multiple posting. --- Call for participation --- NIPS Workshop on Topology Learning New challenges at the crossing of Machine Learning,
Computational Geometry and Topology December 7-8, 2007 Whistler, --- Topic There is a growing interest in Machine Learning, in
applying geometrical and topological tools to high-dimensional data analysis
and processing. Considering a finite set of points in a
high-dimensional space, the approaches developed in the field of Topology
Learning intend to learn, explore and exploit the topology of the shapes
(topological invariants such as the connectedness, the intrinsic dimension or
the Betti numbers), manifolds or not, from which these points are supposed to
be drawn. Applications likely to benefit from these topological
characteristics have been identified in the field of Exploratory Data Analysis,
Pattern Recognition, Process Control, Semi-Supervised Learning, Manifold
Learning and Clustering. However it appears that the integration in the Machine
Learning and Statistics frameworks of the problems we are faced with in
Topology Learning, is still in its infancy. So we wish this workshop to ignite
cross-fertilization between Machine Learning, Computational Geometry and
Topology, likely to benefit to all of them by leading to new approaches, deeper
understanding, and stronger theoretical results about the problems carried by
Topology Learning. --- Trends We wish this workshop to do the spadework on the
following open problems and discuss the proposed solutions: * Theory: How and under which conditions to ensure
provably correct topology with respect to the data? Especially facing noisy,
multi-scale, multidimensional or incomplete datasets? * Algorithms: How to cope with multidimensional or
massive datasets in reasonable memory and time? Can we provide objective
criteria to tune the hyper-parameters? * Applications: How can we insert the topological
knowledge into Machine Learning algorithms? When is it beneficial to do so? How
to visually represent the resulting topology to the analyst in case of
exploratory data analysis? Can we define some benchmark of real and artificial
data specific to this field? --- Submission Authors are invited to submit an abstract based on
original research or already published results, describing new methods they
developed, open problems they are faced with or applications they tackle,
fitting the topic and trends given above. Abstracts should not exceed 2
single-spaced pages with figures and references. If the authors believe that
more details are essential to substantiate the main claims of their abstract,
they may include a clearly marked appendix that will be read at the discretion
of the scientific committee. Abstracts shall be sent by e-mail to:
topolearn2007@gmail.com with subject "SUBMIT". --- Important dates * Submission of abstracts: before October 12, 2007 * Notification of acceptance: October 19, 2007 * Workshop on Topology Learning: December 7-8,
2007 --- Invited speakers * Pr. Herbert Edelsbrunner ( * Pr. Partha Niyogi ( * Pr. Jean-Daniel Boissonnat (INRIA Sophia
Antipolis, France) - Research on Geometric Computing. (
http://www.inria.fr/personnel/Jean-Daniel.Boissonnat.en.html) * Pr. Mathias Hein ( * Dr. Vin de Silva ( --- Organizing and scientific committee * Michaël Aupetit (chair), CEA-IDF, France * Frédéric Chazal, INRIA-Futurs, France * Gilles Gasso, INSA-Rouen, France * David Cohen-Steiner, INRIA-Sophia, France * Pierre Gaillard, CEA-IDF, France --- Registration http://nips.cc/Conferences/2007/ --- Contact information * e-mail: topolearn2007@gmail.com * web site:
http://topolearnnips2007.insa-rouen.fr/ |
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