NIPS*95 program info available

[David Cohn]

                       Final Program NIPS*95

                Neural Information Processing Systems:
                       Natural and Synthetic

                     November 27 - December 2
                         Denver, Colorado

The final program for NIPS*95, along with other conference and
registration information, is now available on the NIPS home page

  URL:  http://www.cs.cmu.edu/Web/Groups/CNBC/nips/NIPS.html

or by sending email to nips95 at mines.colorado.edu.  Please note that
the deadline for early registration is October 28th; registration
costs rise by $50 after that date.

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NIPS*95 Conference Highlights
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    Tutorials: 
    November 27, 1995 
    Denver Marriott City Center, Denver, Colorado 
    
    Formal Conference Sessions: 
    November 28 - 30, 1995 
    Denver Marriott City Center, Denver, Colorado 
    
    Post-Meeting Workshops: 
    December 1-2, 1995 
    Marriott Hotel, Vail, Colorado 

NIPS is a single-track conference -- there will be no parallel
sessions.  Out of approximately 460 submitted papers, 30 will be
presented as talks; another 110 will be presented as posters. All
accepted papers will appear in the proceedings. A number of invited
talks will survey active areas of research and lead off the sessions.
These include:
  
  John H. McMasters, (Banquet Speaker) - Boeing Commercial Aircraft  
  Company  
  "Origins and Future of Flight: A Paleoecological Perspective" 
  
  Bruce Rosen, Massachusetts General Hospital
  "Mapping Brain Function with Functional Magnetic Resonance Imaging" 
  
  David Heckerman, Microsoft
  "Learning Bayesian Networks" 
  
  Brian Ripley, Statistics, Oxford
  "Statistical Ideas for Selecting Network Architectures" 
  
  Thomas McAvoy, University of Maryland
  "Application of Neural Networks in the Chemical Process Industries" 
  
  Elizabeth Bates, UCSD, Cognitive Science Department
  "Brain Organization for Language in Children and Adults" 


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TUTORIAL PROGRAM
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November 27, 1995 

Session I: 9:30-11:30 a.m. 

  "Functional Anatomy of Primate Vision" 
  Gary Blasdel, Harvard Medical School
  
  "Neural Networks for Identification and Control"
  Kumpati Narendra, Yale University
  
Session II: 1:00-3:00 p.m. 
  
  "Cortical Circuits in a Multichip Communication Framework"
  Misha Mahowald, Institute for Neuroinformatics
  
  "Computational Learning and Statistical Prediction"
  Jerome Friedman, Stanford University
  
Session III: 3:30-5:30 p.m. 
  
  "Unsupervised Learning Procedures"
  Geoffrey Hinton, University of Toronto
  
  "Option Pricing in Modern Finance Theory and the Relevance of 
  Artificial Neural Networks"
  Halbert White, University of California at San Diego

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POST-MEETING WORKSHOPS
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November 30 - December 2, 1995 

The formal conference will be followed by post-meeting workshop
sessions in Vail, Colorado.  Registration for the workshops is
optional. It includes the welcome reception, two continental
breakfasts and one banquet dinner. The workshops will have morning
(7:30-9:30 a.m.) and afternoon (4:30-6:30 p.m.) sessions each day and
will be followed by a summary session at 7:00 p.m. on the final day.
Early registration is strongly encouraged, as we may have to limit
attendance. Early room reservations at Vail are also strongly
encouraged. Below is a partial list of this year's workshops.

   Noisy Time Series 

   Object Features for Visual Shape Representation 

   Neural Hardware Engineering 

   Benchmarking of NN Learning Algorithms 

   Symbolic Dynamics in Neural Processing 

   Prospects for Neural Human-Machine Interfaces 

   Neural Information and Coding 

   Modeling the Mind: Large Scale Research Projects 

   Vertebrate Neurophysiology and Neural Networks: can the teacher learn from
   the student? 

   Hybrid HMM/ANN Systems for Sequence Recognition 

   Robot Learning - Learning in the "Real World" 

   Transfer of Knowledge in Inductive Systems 

   The Dynamics of On-Line Learning 

   Optimization Problem Solving with Neural Nets 

   Neural Networks for Signal Processing 

   Statistical and Structural Models in Network Vision 

   Learning in Bayesian Networks and Other Graphical Models 

   Knowledge Acquisition, Consolidation, and Transfer within Neural Networks 

   Dealing with Incomplete Data in Classification and Regression 

   Topological Maps for Density Estimation, Regression and Classification