NIPS*93 workshops

[Michael C. Mozer]

For the curious, a list of topics for the NIPS*93 post-conference workshops
is attached.  The workshops will be held in Vail, Colorado, on December 3 and
4, 1993.  

For further info concerning the individual workshops, please contact the
workshop organizers, whose names and e-mail are listed below.  Abstracts are 
not available at present, but will be distributed prior to the workshops.  

For NIPS conference and workshop registration info, please write to:  NIPS*93
Registration / NIPS Foundation / PO Box 60035 / Pasadena, CA  91116-6035 USA


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December 3, 1993
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Complexity Issues in Neural Computation and Learning
   Vwani Roychowdhury & Kai-Yeung Siu
   vwani at ecn.purdue.edu

Connectionism for Music and Audition
   Andreas Weigend & Dick Duda
   weigend at cs.colorado.edu

Memory-based Methods for Regression and Classification
   Thomas Dietterich 
   tgd at cs.orst.edu

Neural Networks and Formal Grammars
   Simon Lucas 
   sml at essex.ac.uk

Neurobiology, Psychophysics, and Computational Models of Visual Attention
   Ernst Niebur & Bruno Olshausen
   ernst at acquine.cns.caltech.edu

Robot Learning: Exploration and Continuous Domains
   David Cohn 
   cohn at psyche.mit.edu

Stability and Observability
   Max Garzon & F. Botelho
   garzonm at maxpc.msci.memst.edu

VLSI Implementations
   William O. Camp, Jr. 
   camp at owgvm6.vnet.ibm.com

What Does the Hippocampus Compute?
   Mark Gluck & Bruce McNaughton
   gluck at pavlov.rutgers.edu

----------------
December 4, 1993
----------------

Catastrophic Interference in Connectionist Networks:  Can it be Predicted, 
Can it be Prevented?
   Bob French 
   french at willamette.edu

Connectionist Modeling and Parallel Architectures
   Joachim Diederich & Ah Chung Tsoi
   joachim at fitmail.fit.qut.edu.au

Dynamic Representation Issues in Connectionist Cognitive Modeling
   Jordan Pollack 
   pollack at cis.ohio-state.edu

Functional Models of Selective Attention and Context Dependency
   Thomas Hildebrandt 
   thildebr at aragorn.csee.lehigh.edu

Learning in Computer Vision and Image Understanding -- An Advantage over 
Classical Techniques?
   Hayit Greenspan 
   hayit at micro.caltech.edu

Memory-based Methods for Regression and Classification
   Thomas Dietterich 
   tgd at cs.orst.edu

Neural Network Methods for Optimization Problems
   Arun Jagota 
   jagota at cs.buffalo.edu

Processing of Visual and Auditory Space and its Modification by Experience
   Josef Rauschecker 
   josef at helix.nih.gov

Putting it all Together:  Methods for Combining Neural Networks
   Michael Perrone 
   mpp at cns.brown.edu


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NOTE:  The assignment of workshops to dates is tentative.
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