NIPS '89 preliminary program

Dave.Touretzky@B.GP.CS.CMU.EDU Dave.Touretzky at B.GP.CS.CMU.EDU
Mon Oct 2 17:00:06 EDT 1989


Below is the preliminary program for the upcoming IEEE Conference on Neural
Information Processing Systems - Natural and Synthetic, which will be held
November 27 through 30, 1989.  A postconference workshop series will take
place November 30 through December 2.

For registration information, please contact the Local Arrangements Chair,
Kathie Hibbard, by sending email to hibbard at boulder.colorado.edu, or by
writing to:
	Kathie Hibbard
	NIPS '89
	University of Colorado
	Campus  Box 425
	Boulder, Colorado  80309-0425

================================================================


		____________________________________________
		!                                           !
		!     PRELIMINARY  PROGRAM,  NIPS  '89      !
		!           DENVER,  COLORADO               !
		!      NOVEMBER  27  _  NOVEMBER  30, 1989  !
		!___________________________________________!

                               OUTLINE

   Monday,  November  27,  1989
       4:00 PM:  Registration
       6:30 PM:  Reception and Conference Dinner
       8:30 PM:  After-Dinner Plenary Talk by Jack Cowan

   Tuesday,  November  28,  1989
       8:00 AM:  Continental  Breakfast
       8:30 AM - 12:30 PM:     Oral Session1 - Neuroscience
       12:30 - 2:30 PM:   Poster Preview Session 1A, 1B, 1C -
                   Neuroscience, Implementation and Simulation, Applications
       2:30 - 6:30 PM:   Oral Session 2 -
                   Algorithms, Architectures, and Theory I
       7:30 - 10:30 PM:   Refreshments and Poster Session 1A,1B, 1C -
                   Neuroscience, Implementation and Simulation, Applications

   Wednesday,  November  29,  1989
       8:00 AM:  Continental  Breakfast
       8:30 AM - 12:30 PM:     Oral Session3 - Applications
       12:30 - 2:30 PM:   Poster Preview Session 2 -
                   Algorithms, Architectures, and Theory
       2:30 - 6:30 PM:   Oral Session 4 - Implementationand Simulation
       7:30 - 10:30 PM:   Refreshments and Poster Session 2 -
                   Algorithms, Architectures, and Theory

   Thursday,  November  30,  1989
       8:00 AM:  Continental  Breakfast
       8:30 AM - 1:00 PM:     OralSession 5 -
                   Algorithms, Architectures, and Theory II

   Friday,  December  1  -  Saturday,  December  2,  1989
       Post Conference Workshops at Keystone

                   ________________________________
                   ! MONDAY,  NOVEMBER  27,  1989 !
                   !______________________________!

4:00 PM:  Registration
6:30 PM:  Reception and Conference Dinner
8:30 PM:  After-Dinner Plenary Talk "Some NeuroHistory:  Neural Networks from
          1952-1967," by Jack Cowan - University of Chicago.

                   ________________________________
                   ! TUESDAY,  NOVEMBER  28,  1989 !
                   !_______________________________!

                              ORAL  SESSION 1
                               NEUROSCIENCE
 SESSION  CHAIR:  James  Bower,  California  Institute  of  Technology
                      Tuesday,  8:30  AM  -  12:30  PM

 8:30  "Acoustic-Imaging Computations by Echolocating Bats:  Unification of
          Diversely-Represented Stimulus Features into Whole Images," by Jim
          Simmons - Brown University (Invited Talk).

 9:10  "Rules for Neuromodulation of Small Neural Circuits," by Ronald M.
          Harris-Warrick - Section of Neurobiology and Behavior, Cornell
          University.

 9:40  "Neural Network Analysis of Distributed Representations Of Sensory
          Information In The Leech," by S.R. Lockery, G. Wittenberg, W. B.
          Kristan Jr., N. Qian and T. J. Sejnowski -Department of Biology,
          University of California, San Diego and Computational Neurobiology
          Laboratory, The Salk Institute.

 10:10  BREAK

 11:00  "Reading a Neural Code,"by William Bialek, Fred Rieke, R. R. de Ruyter
          van Steveninck, and David Warland - Departments of Physics and
          Biophysics, University of Californiaat Berkeley.

 11:30  "Neural Network Simulation of Somatosensory Representational
          Plasticity," by KamilA. Grajski and Michael M. Merzenich - Coleman
          Memorial Laboratories, University of California, San Francisco.

 12:00  "Brain Maps and Parallel Computer Maps," by Mark E. Nelson and James
          Bower - Division of Biology, California Institute of Technology.

                    POSTER PREVIEW  SESSION  1A
                              NEUROSCIENCE
                        Tuesday,  12:30  -  2:30  PM

A1.  "Category Learning and Object Recognition in a Simple Oscillating Model
         of Cortex " by Bill Baird - Department of Physiology, University of
         California Berkeley.

A2.  "From Information Theory to Structure and Function in a Simplified Model
         of a Biological Perceptual System," by Ralph Linsker   - IBM Research,
         T. J. Watson Research Center.

A3.  "Development and Regeneration of Brain Connections:  A Computational
         Theory," by J.D. Cowan and A.E. Friedman - Mathematics Department,
         University of Chicago.

A4.  "Collective Oscillations in Neuronal Networks:  Functional Architecture
         Drives Dynamics," by Daniel M. Kammen, Philip J. Holmes, and Christof
         Koch - Computation and Neural Systems Program, California Institute
         of Technology.

A5.  "Computer Simulation of Oscillatory Behavior in Cerebral Cortical
         Networks," by M.A. Wilson and J.M. Bower - Computation and Neural
         Systems Program, Division of Biology, California Institute of
         Technology.

A6.  "A Neural Network Model of Catecholamine Effects:  Enhancement of
         Signal Detection Performance is an Emergent Property of Changes in
         Individual Unit Behavior," by David Servan-Schreiber, Harry Printz and
         Jonathan Cohen - Departments of Computer Science and Psychology,
         Carnegie Mellon University.

A7.  "Non-Boltzmann Dynamics in Networks of Spiking Neurons," by Michael
         Crair and William Bialek - Departments ofPhysics and Biophysics,
         University of California at Berkeley.

A8.  "A Computer Modeling Approach toUnderstanding the Inferior Olive and
         Its Relationship to the Cerebellar Cortexin Rats," by Maurice Lee and
         James M. Bower - Computation and Neural Systems Program,
         California Institute of Technology.

A9.  "An Analog VLSI Model of Adaptationin the Vestibulo-Ocular Reflex," by
         Stephen P. DeWeerth and Carver A. Mead - California Institute of
         Technology.

A10.  "Can Simple Cells Learn Curves? A Hebbian Model in a Structured
         Environment," by William R. Softky and Daniel M. Kammen - Divisions
         of Physics and Biology and Computation and Neural Systems Program,
         California Institute of Technology.

A11.  "Formation of Neuronal Groupsin Simple Cortical Models," by Alex
         Chernjavsky and John Moody - Section of Molecular Neurobiology,
         Howard Hughes Medical Institute,Yale University.

A12.  "Signal Propagation in Layered Networks," by Garrett T. Kenyon, Eberhard
         E. Fetz and Robert D. Puff - University of Washington, Department of
         Physics.

A13.  "A Systematic Study of the Input/OutputProperties of a Model Neuron
         With Active Membranes," by Paul Rhodes - University of California,
         San Diego.

A14.  "Analytic Solutions to the Formation of Feature-Analyzing Cells of a
         Three-Layer Feedforward Information Processing Neural Net," by D.S.
         Tang - Microelectronics and Computer Technology Corporation.

A15.  "The Computation of Sound Source Elevation in the Barn Owl" by C.D.
	 Spence and J.C. Pearson, David Sarnoff Research Center.

                    POSTER PREVIEW  SESSION  1B
                IMPLEMENTATION  AND  SIMULATION
                        Tuesday,  12:30  -  2:30  PM

B1.  "Real-Time Computer Vision and Robotics Using Analog VLSI Circuits," by
         Christof Koch, John G. Harris, Tim Horiuchi, Andrew Hsu, and Jin Luo -
         Computation and Neural Systems Program, California Institute of
         Technology.

B2.  "The Effects of Circuit Integration on a Feature Map Vector Quantizer,"
         by Jim Mann - MIT Lincoln Laboratory.

B3.  "Pulse-Firing Neural Chips Implementing Hundreds of Neurons," by Alan F.
         Murray, Michael Brownlow, AlisterHamilton, Il Song Han, 
		 H. Martin Reekie, and Lionel Tarassenko - Department of 
		 Electrical Engineering, University of Edinburgh, Scotland.

B4.  "An Efficient Implementation ofthe Backpropagation Algorithm on the
         Connection Machine CM-2," by Xiru Zhang, Michael Mckenna, Jill P.
         Mesirov, and David Waltz - Thinking Machines Corporation.

B5.  "Performance of Connectionist Learning Algorithms on 2-D SIMD Processor
         Arrays," by Fernando J. Nunez and Jose A.B. Fortes - School of
         Electrical Engineering, Purdue University.

B6.  "Dataflow Architectures: Flexible Platforms for Neural Network
         Simulation," by I.G. Smotroff - The MITRE Corporation.

B7.  "Neural Network Visualization," by Jakub Wejchert and Gerald Tesauro -
         IBM Research, T.J. Watson Research Center.

                    POSTER  PREVIEW  SESSION  1C
                              APPLICATIONS
                        Tuesday,  12:30  -  2:30  PM

C1.  "Computation and Learning in Artificial Dendritic-Type
         Structures:  Application to Speech Recognition," by Tony Bell - Free
         University of Brussels, Belgium.

C2.  "Speaker Independent Speech Recognition with Neural Networks and
         Speech Knowledge," by Yoshua Bengio, Regis Cardin, and Renato De
         Mori - McGill University, School of Computer Science.

C3.  "HMM Speech Recognition with Neural Net Discrimination," by William Y.
         Huang and Richard P. Lippmann- MIT Lincoln Laboratory.

C4.  "Connectionist Architectures for Multi-Speaker Phoneme Recognition," by
         John B. Hampshire II and Alex H. Waibel - School of Computer
         Science, Carnegie Mellon University.

C5.  "Performance Comparisons Between Backpropagation Networks and
         Classification Trees on Three Real-World Applications," by Les Atlas,
         Ronald Cole, Yeshwant Muthusamy, James Taylor, and Etienne Barnard  -
         Department of Electrical Engineering, University of 
		 Washington, Seattle.

C6.  "Combining Visual and Acoustic Speech Signals with a Neural Network
         Improves Intelligibility," by Ben P. Yuhas, M.H. Goldstein, Jr., and
         Terrence J. Sejnowski - Speech Processing Laboratory, Department of
         Electrical and Computer Engineering, Johns Hopkins University.

C7.  "A Neural Network for Real-Time Signal Processing," by Donald B. Malkoff
         - General Electric / Advanced Technology Laboratories.

C8.  "A Neural Network to Detect Homologies in Proteins," by Yoshua Bengio,
         Yannick Pouliot, Samy Bengio,and Patrick Agin - McGill University,
         School of Computer Science.

C9.  "Recognizing Hand-Drawn and Handwritten Symbols with Neural Nets," by
         Gale L. Martin and James A. Pittman - MCC,Austin.

C10.  "Model Based Image Compression and Adaptive Data Representation by
         Interacting Filter Banks," by Toshiaki Okamoto, Mitsuo Kawato, Toshio
         Inui, and Sei Miyake - ATR Auditory and Visual Perception Research
         Laboratories, Japan.

C11.  "A Large-Scale Network Which Recognizes Handwritten Kanji Characters,"
         by Yoshihiro Mori and Kazuki Joe - ATR Auditory and Visual
         Perception Research Laboratories, Japan.

C12.  "Traffic: Object Recognition Using Hierarchical Reference Frame
         Transformations," by Richard S. Zemel, Michael C. Mozer, 
	 and Geoffrey Hinton - Department of Computer Science, 
	 University of Toronto.

C13.  "Comparing the Performance of Connectionist and Statistical Classifiers on
         an Image Segmentation Problem," by Sheri L. Gish and W.E. Blanz -
         IBM Knowledge Based Systems, Menlo Park.

C14.  "A Modular Architecture For Target Recognition Using Neural Networks,"
         by Murali M. Menon and Eric J. Van Allen - MIT Lincoln Laboratory.

C15.  "Neurally Inspired Plasticity in Oculomotor Processes," by Paul Viola -
         Artificial Intelligence Laboratory, Massachusetts 
	 Institute of Technology.

C16.  "Neuronal Group Selection Theory:  A Grounding in Robotics," by Jim
         Donnett and Tim Smithers - Department of Artificial Intelligence,
         University of Edinburgh, Scotland.

C17.  "Composite Holographic Associative Recall Model (CHARM) and
         Recognition Failure of Recallable Words," by Janet Metcalfe -
         Department of Psychology, University of California, San Diego.

C18.  "Using a Translation-Invariant Neural Network to Diagnose Heart
         Arrhythmia," by Susan Lee - Johns Hopkins Institute.

C19.  "Exploring Bifurcation Diagrams With Adaptive Networks," by Alan S.
         Lapedes and Robert M. Farber - Theoretical Division, Los Alamos
         National Laboratory.

C20.  "Generalized Hopfield Networks and Nonlinear Optimization," by Athanasios
         G. Tsirukis, Gintaras V. Reklaitis, and Manoel F. Tenorio - School of
         Chemical Engineering, Purdue University.


                             ORAL  SESSION 2
        ARCHITECTURES,  ALGORITHMS,  AND  THEORY  I
            SESSION  CHAIR:  John  Moody,  Yale  University
                        Tuesday,  2:30  -  6:30  PM

2:30  "Statistical Properties of Polynomial Networks and Other Artificial Neural
         Networks:  A Unifying View," by Andrew Barron - University of Illinois
         at Champaign-Urbana (Invited Talk).

3:10  "Supervised Learning: A Theoretical Framework," by Sara Solla, 
	 Naftali Tishby, and Esther Levin - AT&T Bell Laboratories.

3:40  "Practical Characteristics of Neural Network and Conventional Pattern
         Classifiers on Artificial and Speech Problems," by Yuchun Lee and
         Richard P. Lippmann - Digital Equipment Corporation and MIT Lincoln
         Laboratory.

4:10  BREAK

5:00  "The Cocktail Party Problem:  Speech/Data Signal Separation Comparison
         Between Backprop and SONN," by Manoel F. Tenorio, John Kassebaum,
         and Christoph Schaefers - School of Electrical Engineering, Purdue
         University.

5:30  "Optimal Brain Damage," by Yann LeCun, John Denker, Sara Solla, Richard
         E. Howard, and Lawrence D. Jackel - AT&T Bell Laboratories.

6:00  "Sequential Decision Problems and Neural Networks," by Andrew G. Barto,
         Richard S. Sutton and Chris Watkins -Department of Computer and
         Information Science, University of Massachusetts, Amherst.

                     POSTER  SESSION  1A,  1B,  1C
    NEUROSCIENCE,  IMPLEMENTATION  AND  SIMULATION,
                              APPLICATIONS
                        Tuesday,  7:30  -  10:30  PM
          (Papers  are  Listed  Under  Poster  Preview  Session)

                ___________________________________
                ! WEDNESDAY,  NOVEMBER  29,  1989  !
                !__________________________________!

                             ORAL  SESSION 3
                              APPLICATIONS
   SESSION  CHAIR:  Richard  Lippmann,  MIT  Lincoln  Laboratory
                   Wednesday,  8:30  AM  -  12:30 PM

8:30  "Visual Preprocessing" by George Sperling - New York University (Invited
         Talk).

9:10  "Handwritten Digit Recognition with a Back-Propagation Network," by Y.
         LeCun, B. Boser, J.S. Denker, D. Henderson,R.E. Howard, W. Hubbard,
         and L.D. Jackel - AT&T BellLab oratories.

9:40  "A Self-Organizing Associative Memory System for Control Applications,"
         by Michael Hormel - Department ofControl Theory and Robotics,
         Technical University of Darmstadt, Germany.

10:10  BREAK

11:00  "Variable Resolution Learning Techniques for Speech Recognition," by
         Kevin Lang and Geoffrey Hinton - Carnegie-Mellon University.

11:30  "Word Recognition in a Continuous Speech Recognition System
         Embedding MLP into HMM," by H. Bourlard andN. Morgan -
         International Computer Science Institute, Berkeley.

12:00  "A Computational Basis for Phonology," by David S. Touretzky and
         Deirdre W. Wheeler - Carnegie-Mellon University.

                    POSTER  PREVIEW  SESSION  2
         ARCHITECTURES,  ALGORITHMS,  AND  THEORY
                      Wednesday,  12:30  -  2:30  PM

1. "Using Local Networks to Control Movement," by ChristopherG. Atkeson -
         Department of Brain and Cognitive Sciencesand the Artificial
         Intelligence Laboratory, Massachusetts Institute of Technology.

2. "Computational Neural Theory for Learning Nonlinear Mappings," by Jacob
         Barhen and Sandeep Gulati - Jet PropulsionLab oratory, California
         Institute of Technology.

3. "Learning to Control an Unstable System Using Forward Modeling," by
         Michael I. Jordan and Robert A. Jacobs - Department of Brain and
         Cognitive Sciences, Massachusetts Institute ofTechnology.

4. "Discovering High Order Features With Mean Field Networks," by Conrad
         Galand and Geoffrey E. Hinton - Departmentof Computer Science,
         University of Toronto.

5. "Designing Application-Specific Neural Networks Using the Genetic
         Algorithm," by Steven A. Harp, Tariq Samad, and Aloke Guha -
         Honeywell CSDD.

6. "Two vs. Three Layers:  An Empirical Study of Learning Performance and
         Emergent Representations," by Charles Martin and John Moody   -
         Department of Computer Science, Yale University.

7. "Operational Fault Tolerance of CMAC Networks," by Michael J. Carter,
         Frank Rudolph, and Adam Nucci - IntelligentStructures Group, Dept.
         of Electrical and Computer Engineering, University of New Hampshire.

8. "A Model of Unification in Connectionist Networks," by Andreas Stolcke -
         Computer Science Division, University of California, Berkeley.

9. "Two-Dimensional Shape Recognition Using Sparse Distributed Memory:  An
         Example of a Machine Vision System that Exploits Massive Parallelism
         for Both High-Level and Low-Level Processing," by Bruno Olshausen
         and Pentti Kanerva - Research Institute for Advanced Computer
         Science, NASA Ames Research Center.

10.  "Predicting Weather Using a Genetic Memory:  A Combination of
         Kanerva's Sparce Distributed Memory With Holland's Genetic
         Algorithms," by David Rogers - Research Institute for Advanced
         Computer Science, NASA Ames Research Center.

11.  "Neural Network Weight Matrix Synthesis Using Optimal Control," by O.
         Farotimi, A. Dembo, and T. Kailath - Information Systems Laboratory,
         Department of Electrical Engineering, Stanford University.

12.  "The CHIR Algorithm:  A Generalization for Multiple Output Networks," by
         Tal Grossman - Department ofElectronics, Weizmann Institute of
         Science, Israel.

13.  "Analysis of Linsker's Application of Hebbian Rules to Linear Networks," by
         David J. C. MacKay and Kenneth D. Miller - Department of
         Computation and Neural Systems, California Institute of Technology
	 and Department of Physiology, University of California, 
	 San Francisco.

14.  "A Generative Framework for Unsupervised Learning," by Steven J. Nowlan
         - Department of Computer Science, University of Toronto.

15.  "An Adaptive Network Model of Basic-Level Learning in Hierarchically
         Structured Categories," by Mark A. Gluck, James E. Corter, and Gordon
         H. Bower - Stanford University.

16.  "Generalization and Scaling in Reinforcement Learning," by David H. Ackley
         and Michael S. Littman - Bell Communications Research, Cognitive
         Science Research Group.

17.  "Neural Implementation of Motivated Behavior:  Feeding in an Artificial
         Insect," by Randall D. Beer and Hillel J.Chiel - Departments of
         Computer Engineering and Science and Biology and the Center for
         Automation and Intelligent Systems Research, Case Western Reserve
         University.

18.  "Back Propagation in a Genetic Search Environment," by Wayne Mesard
         and Lawrence Davis - Bolt Beranek and Newman Systems and
         Technologies, Inc., Laboratories  Division.

19.  "A Method for the Associative Storage of Analog Vectors," by Amir F.
         Atiya and Yaser S. Abu-Mostafa - Department of Electrical Engineering,
         California Institute of Technology.

20.  "Generalization and Parameter Estimation in Feedforward  Nets: Some
         Experiments," by N. Morgan and H. Bourlard - International Computer
         Science Institute, Berkeley.

21.  "Subgrouping Reduces Complexity and Speeds Up Learning in Recurrent
         Networks," by David Zipser- Department of Cognitive Science,
         University of California, San Diego.

22.  "Sigma-Pi Learning:  A Model for Associative Learning in Cerebral Cortex,"
         by Bartlett W. Mel and Christof Koch - Computation and Neural
         Systems Program, California Institute of Technology.

23.  "Complexity of Finite Precision Neural Network Classifier," by 
	 K. Y. Siu, A. Dembo, and T. Kailath - Information Systems 
	 Laboratory, Stanford University.

24.  "Analog Neural Networks of Limited Precision I: Computing With
         Multilinear Threshold Functions," by Zoran Obradovic and Ian Parberry -
         Department of Computer Science, Pennsylvania State University.

25.  "On the Distribution of the Local Minima of a Random Function of a
         Graph," by P. Baldi, Y. Rinott, and C. Stein - University of 
	 California, San Diego.

26.  "A Neural Network For Feature Extraction," by Nathan Intrator - Center for
         Neural Science and Division of Applied Mathematics,  Brown University.

27.  "Meiosis Networks," by Stephen Jose Hanson - Cognitive Science Laboratory,
         Princeton University.

28.  "Unsupervised Learning Using Velocity Field Approach," by Michail Zak -
         Jet Propulsion Laboratory,California Institute of Technology.

29.  "Algorithms for Better Representation and Faster Learning in Radial Basis
         Function Networks," by Avijit Saha and James D. Keeler - MCC Austin,
         Texas.

30.  "Generalization Performance of Overtrained Back-Propagation Networks:
         Some Experiments," by Y. Chauvin - Psychology Department, Stanford
         University.

31.  "The 'Moving Targets' Training Method," by Richard Rohwer - Centre for
         Speech Technology Research, University of Edinburgh, Scotland.

32.  "Optimal Learning and Inference Over MRF Models:  Application To
         Computational Vision on Connectionist Architectures," by Kurt R.
         Smith, Badrinath Roysam, and Michael I. Miller - Washington University.

33.  "A Cost Function for Learning Internal Representations," by J.A. Hertz, A.
         Krogh, and G.I. Thorbergsson - Niels Bohr Institute, Denmark.

34.  "The Cascade-Correlation Learning Architecture," by Scott E. Fahlman and
         Christian Lebiere - School of Computer Science, Carnegie-Mellon
         University.

35.  "Training Connectionist Networks With Queries and Selective Sampling," by
     	 D. Cohn, L. Atlas, R. Ladner, R. Marks II, M. El-ASharkawi,
	 M. Aggoune, D. Park - Dept.  of Electrical Engineering, 
	 University of Washington.

36.  "Rule Representations in a Connectionist Chunker," by David S. Touretzky -
         School of Computer Science, Carnegie Mellon University.

37.  "Unified Theory for Symmetric and Asymmetric Systems and the Relevance
         to the Class of Undecidable Problems," by I. Kanter - Princeton
         University.

38.  "Synergy of Clustering Multiple Back Propagation Networks," by William P.
         Lincoln and Josef Skrzypek - Hughes Aircraft Company and Machine
	 Perception Laboratory, UCLA.

39.  "Training Stochastic Model Recognition Algorithms as Networks Can Lead
         to Maximum Mutual Information Estimation of Parameters," by John
         S. Bridle - Machine Intelligence Theory Section, Royal Signals and
         Radar Establishment, Great Britain.

40.  "Self-Organizing Multiple-View Representations of 3D Objects," by D.
         Weinshall, S. Edelman, and H. Bulthoff  - MIT Center for Biological
         Information Processing.

41.  "A Recurrent Network that Learns Context-Free Grammars," by 
         G.Z. Sun, H.H. Chen, C.L. Giles, Y.C. Lee, and D. Chen - Laboratory 
	 for Plasma Physics Research and Institute for Advanced Computer 
	 Studies, University of Maryland.

42.  "Time Dependent Adaptive Neural Networks," by F. J. Pineda - 
	 Jet Propulsion Laboratory, California Institute of Technology.

                             ORAL  SESSION 4
                IMPLEMENTATION  AND  SIMULATION
        SESSION  CHAIR:  Jay  Sage,  MIT  Lincoln  Laboratory
                       Wednesday,  2:30  -  6:30  PM

2:30  "Visual Object Recognition" by Shimon Ullman - Massachusetts Institute
         of Technology and Weizmann Institute of Science (Invited Talk).

3:10  "A Reconfigurable Analog VLSI Neural Network Chip," by Srinagesh
         Satyanarayana, Yannis Tsividis, and Hans Peter Graf - Department of
         Electrical Engineering and Center for Telecommunications Research,
         Columbia University.

3:40  "Analog Circuits for Constrained Optimization," by John Platt - California
         Institute of Technology.

4:10  BREAK

5:00  "VLSI Implementation of a High-Capacity Neural Associative Memory," by
         Tzi-Dar Chiueh and Rodney M. Goodman - Department of Electrical
         Engineering, California Institute of Technology.

5:30  "Hybrid Analog-Digital 32x32x6-Bit Synapse Chips for Electronic Neural
         Networks," by A. Moopenn, T. Duong,and A. P. Thakoor  -  Jet
         Propulsion Laboratory, California Institute of Technology.

6:00  "Learning Aspect Graph Representations From View Sequences," by 
	 Michael Seibert and Allen M. Waxman - MIT Lincoln Laboratory.

                           POSTER  SESSION  2
         ARCHITECTURES,  ALGORITHMS,  AND  THEORY
                      Wednesday,  7:30  -  10:30  PM
          (Papers  are  Listed  Under  Poster  Preview  Session)

                 __________________________________
                 ! THURSDAY,  NOVEMBER  30, 1989  !
                 !________________________________!

                             ORAL  SESSION 5
        ARCHITECTURES,  ALGORITHMS,  AND  THEORY  II
        SESSION  CHAIR:  Eric  Baum,  NEC  Research  Institute
                     Thursday,  8:30  AM -  1:00  PM

8:30  "Identification and Control of Dynamical Systems Using Neural Networks,"
         by Bob Narendra - YaleUniversity (Invited Talk).

9:10  "Discovering the Structure of a Reactive Environment by Exploration," by
         Michael C. Mozer and Jonathan Bachrach - University of Colorado
         Boulder.

9:40  "The Perceptron Algorithm Is Fast at Modified Valiant Learning," by Eric
         B. Baum - Department of Physics, PrincetonUniversity.

10:10  BREAK

11:00  "Oscillations in Neural Computations," by Pierre Baldi and Amir Atiya -
         Jet Propulsion Laboratory and Division ofBiology, California Institute
         of Technology.

11:30  "Incremental Parsing by Modular Recurrent Connectionist Networks," by
         Ajay Jain and Alex Waibel - School of ComputerScience, Carnegie
         Mellon University.

12:00  "Neural Networks From Coupled Markov Random Fields via Mean Field
         Theory," by Davi Geiger and Federico Girosi - Artificial Intelligence
         Laboratory, Massachusetts Institute of Technology.

12:30  "Asymptotic Convergence of Back-Propagation," by Gerald Tesauro, Yu He,
         and Subatai Ahmad - IBM Thomas J. Watson Research Center.

  ____________________________________________________________
 !       POST  CONFERENCE  WORKSHOPS  AT  KEYSTONE            !
 !  THURSDAY,  NOVEMBER  30  -  SATURDAY,  DECEMBER  2,  1989 !
 !____________________________________________________________!

   Thursday,  November  30,  1989
       5:00 PM:  Registration and Reception at Keystone

   Friday,  December  1,  1989
       7:30 - 9:30 AM: Small Group Workshops
       4:30 - 6:30 PM: Small Group Workshops
       8:30 - 10:30 PM: Plenary Discussion Session 

   Saturday,  December  2,  1989
       7:30 - 9:30 AM: Small Group Workshops
       4:30 - 6:30 PM: Small Group Workshops
       7:00 PM: Banquet 


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