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Neuron Digest Volume 05 Number 40
Neuron Digest Tuesday, 3 Oct 1989 Volume 5 : Issue 40
Today's Topics:
NIPS '89 preliminary program
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------------------------------------------------------------
Subject: NIPS '89 preliminary program
From: Dave.Touretzky@B.GP.CS.CMU.EDU
Date: Mon, 02 Oct 89 17:00:06 -0400
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@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
------------------------------
End of Neurons Digest
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