Connectionists: call for papers: NIPS*2007 Workshop on Efficient Machine Learning

[Samy Bengio]

Hello,

Please advertise the following CALL FOR PAPERS:

           NIPS*2007 Workshop on Efficient Machine Learning
        Overcoming Computational Bottlenecks in Machine Learning
               Whistler, Canada, December 7-8, 2007
                  http://bigml.wikispaces.com/cfp


Overview
--------

The ever increasing size of available data to be processed by machine learning
algorithms has yielded several approaches, from online algorithms to parallel
and distributed computing on multi-node clusters. Nevertheless, it is not clear
how modern machine learning approaches can either cope with such parallel
machineries or take into account strong constraints regarding the available time
to handle training and/or test examples. This workshop will explore two
alternatives:

     * modern machine learning approaches that can handle real time processing
       at train and/or at test time, under strict computational constraints
       (when the flow of incoming data is continuous and needs to be handled)
     * modern machine learning approaches that can take advantage of new
       commodity hardware such as multicore, GPUs, and fast networks.

This two-day workshop aims to set the agenda for future advancements by
fostering a discussion of new ideas and methods and by demonstrating the
potential uses of readily-available solutions. It will bring together both
researchers and practitioners to offer their views and experience in applying
machine learning to large scale learning.

Topics of Interest
------------------

     * efficient parallelization of machine learning algorithms and algorithms
       that make use of new hardware architectures
     * sub-linear training algorithms for virtually infinite datasets
     * new online boosting, online kernel, and other efficient non-linear
       online training algorithms
     * efficient feature extraction for classification and detection
     * adapted structures for very large number of features per example
     * evolving under strict time/space constraints
     * coarse-to-fine and "focusing" algorithms for detection

Submission Procedure
--------------------

We encourage the submissions of extended abstract. The suggested abstract length
is about 2 pages. The invited speakers will be allocated between 40 and 60
minutes, while the authors of the accepted abstracts will be allocated between
30 and 40 minutes to present their work (to be determined according to
submissions). In addition, the abstracts will be available to a broader audience
on the dedicated web site. The authors should submit their extended abstract to
bigml.nips at gmail.com in pdf. An email confirming the reception of the submission
will be sent by the organizers.

Important Dates
---------------

     * Aug 28: Workshop announcement / call for abstracts
     * Oct 12: Abstract submission deadline
     * Nov 1: Notification of acceptance
     * Dec 7 and 8: Workshop

Invited Speakers
----------------

     * Yali Amit, University of Chicago
     * Yoshua Bengio, University of Montreal
     * Michael Burl, NASA JPL
     * Corinna Cortes, Google
     * Dennis DeCoste, Microsoft
     * Don Geman, John Hopkins University
     * Dan Pelleg and Elad Yom-Tov, IBM Research
     * Yann LeCun, New York University
     * Srinivasan Parthasarathy, Ohio State University
     * Nicol N. Schraudolph, National ICT Australia

Organizers
----------

     * Samy Bengio, Google
     * Corinna Cortes, Google
     * Dennis DeCoste, Microsoft Live Labs
     * Francois Fleuret, IDIAP Research Institute
     * Ramesh Natarajan, IBM T.J. Watson Research Lab
     * Edwin Pednault, IBM T.J. Watson Research Lab
     * Dan Pelleg, IBM Haifa Research Lab
     * Elad Yom-Tov, IBM Haifa Research Lab

Other Members of the Programme Committee
----------------------------------------

     * Yali Amit, University of Chicago
     * Gilles Blanchard, Fraunhofer Institut FIRST, Berlin
     * Ronan Collobert, NEC
     * Yves Grandvalet
     * IDIAP Research Institute
     * Jiri Matas, Czech Technical University, Prague
     * Sam Roweis, Google


----
Samy Bengio
Research Scientist in Machine Learning.
Google, 1600 Amphitheatre Pkwy, Building 47-171D, Mountain View, CA 94043, USA
tel:+1 (650) 253-2563, mailto:bengio at google.com, http://bengio.abracadoudou.com