jose@LEARNING.SIEMENS.COM (Steve Hanson) (09/27/90)
NIPS 1990 Preliminary Program, November 26-29, Denver, Colorado Monday, November 26, 1990 12:00 PM: Registration Begins 6:30 PM: Reception and Conference Banquet 8:30 PM: After Banquet Talk, "Cortical Memory Systems in Humans", by Antonio Damasio. Tuesday, November 27, 1990 7:30 AM: Continental Breakfast 8:30 AM: Oral Session 1: Learning and Memory 10:30 AM: Break 11:00 AM: Oral Session 2: Navigation and Planning 12:35 PM: Poster Preview Session I, Demos 2:30 PM: Oral Session 3: Temporal and Real Time Processing 4:10 PM: Break 4:40 PM: Oral Session 4: Representation, Learning, and Generalization I 6:40 PM: Free 7:30 PM: Refreshments and Poster Session I Wednesday, November 28, 1990 7:30 AM: Continental Breakfast 8:30AM: Oral Session 5: Visual Processing 10:20 AM: Break 10:50 AM: Oral Session 6: Speech Processing 12:20 PM: Poster Preview Session II, Demos 2:30 PM: Oral Session 7: Representation, Learning, and Generalization II 4:10 PM: Break 4:40 PM: Oral Session 8: Control 6:40 PM: Free 7:30 PM: Refreshments and Poster Session II Thursday, November 29, 1990 7:30 AM: Continental Breakfast 8:30 AM: Oral Session 9: Self-Organization and Unsupervised Learning 10:20 AM: Break 10:50 AM: Session Continues 12:10 PM: Conference Adjourns 5:00 PM Reception and Registration for Post-Conference Workshop (Keystone, CO) Friday, November 30 -- Saturday, December 1, 1990 Post-Conference Workshops at Keystone ------------------------------------------------------------------------------ ORAL PROGRAM Monday, November 26, 1990 12:00 PM: Registration Begins 6:30 PM: Reception and Conference Banquet 8:30 PM: After Banquet Talk, "Cortical Memory Systems in Humans", by Antonio Damasio. Tuesday, November 27, 1990 7:30 AM: Continental Breakfast ORAL SESSION 1: LEARNING AND MEMORY Session Chair: John Moody, Yale University. 8:30 AM: "Multiple Components of Learning and Memory in Aplysia", by Thomas Carew. 9:00 AM: "VLSI Implementations of Learning and Memory Systems: A Review", by Mark Holler. 9:30 AM: "A Short-Term Memory Architecture for the Learning of Morphophonemic Rules", by Michael Gasser and Chan-Do Lee. 9:50 AM "Short Term Active Memory: A Recurrent Network Model of the Neural Mechanism", by David Zipser. 10:10 AM "Direct Memory Access Using Two Cues: Finding the Intersection of Sets in a Connectionist Model", by Janet Wiles, Michael Humphreys and John Bain. 10:30 AM Break ORAL SESSION 2: NAVIGATION AND PLANNING Session Chair: Lee Giles, NEC Research. 11:00 AM "Real-Time Autonomous Robot Navigation Using VLSI Neural Networks", by Alan Murray, Lionel Tarassenko and Michael Brownlow. 11:20 AM "Planning with an Adaptive World Model" by Sebastian B. Thrun, Knutt Moller and Alexander Linden . 11:40 AM "A Connectionist Learning Control Architecture for Navigation", by Jonathan Bachrach. 12:00 PM Spotlight on Language: Posters La1 and La3. 12:10 PM Spotlight on Applications: Posters App1, App6, App7, App10, and App11. 12:35 PM Poster Preview Session I, Demos ORAL SESSION 3: TEMPORAL AND REAL TIME PROCESSING Session Chair: Josh Alspector, Bellcore 2:30 PM "Learning and Adaptation in Real Time Systems", by Carver Mead. 3:00 PM "Applications of Neural Networks in Video Signal Processing", by John Pearson. 3:30 PM "Predicting the Future: A Connectionist Approach", by Andreas S. Weigend, Bernardo Huberman and David E. Rumelhart. 3:50 PM "Algorithmic Musical Composition with Melodic and Stylistic Constraints", by Michael Mozer and Todd Soukup. 4:10 PM Break ORAL SESSION 4: REPRESENTATION, LEARNING, AND GENERALIZATION I Session Chair: Gerry Tesauro, IBM Research Labs. 4:40 PM "An Overview of Representation and Convergence Results for Multilayer Feedforward Networks", by Hal White . 5:10 PM "A Simplified Linear-Threshold-Based Neural Network Pattern Classifier", by Terrence L. Fine. 5:30 PM "A Novel approach to predicition of the 3-dimensional structures of protein backbones by neural networks", by H. Bohr, J. Bohr, S. Brunak, R.M.J. Cotterill, H. Fredholm, B. Lautrup and S.B. Petersen. 5:50 PM "On the Circuit Complexity of Neural Networks", by Vwani Roychowdhury, Kai- Yeung Siu, Alon Orlitsky and Thomas Kailath . 6:10 PM Spotlight on Learning and Generalization: Posters LG2, LG3, LG8, LS2, LS5, and LS8. 6:40 PM Free 7:30 PM Refreshments and Poster Session I Wednesday, November 28, 1990 7:30 AM Continental Breakfast ORAL SESSION 5: VISUAL PROCESSING Session Chair: Yann Le Cun, AT&T Bell Labs 8:30 AM "Neural Dynamics of Motion Segmentation", by Ennio Mingolla. 9:00 AM "VLSI Implementation of a Network for Color Constancy", by Andrew Moore, John Allman, Geoffrey Fox and Rodney Goodman. 9:20 AM "Optimal Filtering in the Salamander Retina", by Fred Rieke, Geoffrey Owen and William Bialek. 9:40 AM "Grouping Contour Elements Using a Locally Connected Network", by Amnon Shashua and Shimon Ullman. 10:00 AM Spotlight on Visual Motion Processing: Posters VP3, VP6, VP9, and VP12. 10:20 AM Break ORAL SESSION 6: SPEECH PROCESSING Session Chair: Richard Lippmann, MIT Lincoln Labs 10:50 AM "From Speech Recognition to Understanding: Development of the MIT, SUMMIT, and VOYAGER Systems", by James Glass. 11:20 PM "Speech Recognition using Connectionist Approaches", by K.Chouki, S. Soudoplatoff, A. Wallyn, F. Bimbot and H. Valbret. 11:40 AM "Continuous Speech Recognition Using Linked Predictive Neural Networks", by Joe Tebelskis, Alex Waibel and Bojan Petek. 12:00 PM Spotlight on Speech and Signal Processing: Posters Sig1, Sig2, Sp2, and Sp7. 12:20 PM Poster Preview Session II, Demos ORAL SESSION 7: REPRESENTATION, LEARNING AND GENERALIZATION II Session Chair: Steve Hanson, Siemens Research. 2:30 PM "Learning and Understanding Functions of Many Variables Through Adaptive Spline Networks", by Jerome Friedman. 3:00 PM "Connectionist Modeling of Generalization and Classification", by Roger Shepard. 3:30 PM "Bumptrees for Efficient Function, Constraint, and Classification Learning", by Stephen M.Omohundro. 3:50 PM "Generalization Properties of Networks using the Least Mean Square Algorithm", by Yves Chauvin. 4:10 PM Break ORAL SESSION 8: CONTROL Session Chair: David Touretzky, Carnegie-Mellon University. 4:40 PM "Neural Network Application to Diagnostics and Control of Vehicle Control Systems", by Kenneth Marko. 5:10 PM "Neural Network Models Reveal the Organizational Principles of the Vestibulo- Ocular Reflex and Explain the Properties of its Interneurons", by T.J. Anastasio. 5:30 PM "A General Network Architecture for Nonlinear Control Problems", by Charles Schley, Yves Chauvin, Van Henkle and Richard Golden. 5:50 PM "Design and Implementation of a High Speed CMAC Neural Network Using Programmable CMOS Logic Cell Arrays", by W. Thomas Miller, Brain A. Box, Erich C. Whitney and James M. Glynn. 6:10 PM Spotlight on Control: Posters CN2, CN6, and CN7. 6:25 PM Spotlight on Oscillations: Posters Osc1, Osc2, and Osc3. 6:40 PM Free 7:30 PM Refreshments and Poster Session II Thursday, November 29, 1990 7:30 AM Continental Breakfast ORAL SESSION 9: SELF ORGANIZATION AND UNSUPERVISED LEARNING Session Chair: Terry Sejnowki, The Salk Institute. 8:30 AM "Self-Organization in a Developing Visual Pattern", by Martha Constantine-Paton. 9:00 AM "Models for the Development of Eye-Brain Maps", by Jack Cowan. 9:20 AM "VLSI Implementation of TInMANN", by Matt Melton, Tan Pahn and Doug Reeves. 9:40 AM "Fast Adaptive K-Means Clustering", by Chris Darken and John Moody. 10:00 AM "Learning Theory and Experiments with Competitive Networks", by Griff Bilbro and David Van den Bout. 10:20 AM Break 10:50 AM "Self-Organization and Non-Linear Processing in Hippocampal Neurons", by Thomas H. Brown, Zachary Mainen, Anthony Zador and Brenda Claiborne. 11:10 AM "Weight-Space Dynamics of Recurrent Hebbian Networks", by Todd K. Leen. 11:30 AM "Discovering and Using the Single Viewpoint Constraint", by Richard S. Zemel and Geoffrey Hinton. 11:50 AM "Task Decompostion Through Competition in A Modular Connectionist Architecture: The What and Where Vision Tasks", by Robert A. Jacobs, Michael Jordan and Andrew Barto. 12:10 PM Conference Adjourns 5:00 PM Post-Conference Workshop Begins (Keystone, CO) ------------------------------------------------------------------------------ POSTER PROGRAM POSTER SESSION I Tuesday, November 27 (* denotes poster spotlight) APPLICATIONS App1* "A B-P ANN Commodity Trader", by J.E. Collard. App2 "Analog Neural Networks as Decoders", by Ruth A. Erlanson and Yaser Abu- Mostafa. App3 "Proximity Effect Corrections in Electron Beam Lithography Using a Neural Network", by Robert C. Frye, Kevin Cummings and Edward Rietman. App4 "A Neural Expert System with Automated Extraction of Fuzzy IF-THEN Rules and Its Application to Medical Diagnosis", by Yoichi Hayashi. App5 "Integrated Segmentation and Recognition of Machine and Hand--printed Characters", by James D. Keeler, Eric Hartman and Wee-Hong Leow. App6* "Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences", by Michael O. Noordewier, Geoffrey Towell and Jude Shavlik. App7* "Seismic Event Identification Using Artificial Neural Networks", by John L. Perry and Douglas Baumgardt. App8 "Rapidly Adapting Artificial Neural Networks for Autonomous Navigation", by Dean A. Pomerleau. App9 "Sequential Adaptation of Radial Basis Function Neural Networks and its Application to Time-series Prediction", by V. Kadirkamanathan, M. Niranjan and F. Fallside. App10* "EMPATH: Face, Emotion, and Gender Recognition Using Holons", by Garrison W. Cottrell and Janet Metcalf. App11* "Sexnet: A Neural Network Identifies Sex from Human Faces", by B. Golomb, D. Lawrence and T.J. Sejnowski. EVOLUTION AND LEARNING EL1 "Using Genetic Algorithm to Improve Pattern Classification Performance", by Eric I. Chang and Richard P. Lippmann. EL2 "Evolution and Learning in Neural Networks: The Number and Distribution of Learning Trials Affect the Rate of Evolution", by Ron Kessing and David Stork. LANGUAGE La1* "Harmonic Grammar", by Geraldine Legendre, Yoshiro Miyata and Paul Smolensky. La2 "Translating Locative Prepostions", by Paul Munro and Mary Tabasko. La3* "Language Acquisition via Strange Automata", by Jordon B. Pollack. La4 "Exploiting Syllable Structure in a Connectionist Phonology Model", by David S. Touretzky and Deirdre Wheeler. LEARNING AND GENERALIZATION LG1 "Generalization Properties of Radial Basis Functions", by Sherif M.Botros and C.G. Atkeson. LG2* "Neural Net Algorithms That Learn In Polynomial Time From Examples and Queries", by Eric Baum. LG3* "Looking for the gap: Experiments on the cause of exponential generalization", by David Cohn and Geral Tesauro. LG4 "Dynamics of Generalization in Linear Perceptrons ", by A. Krogh and John Hertz. LG5 "Second Order Properties of Error Surfaces, Learning Time, and Generalization", by Yann LeCun, Ido Kanter and Sara Solla. LG6 "Kolmogorow Complexity and Generalization in Neural Networks", by Barak A. Pearlmutter and Ronal Rosenfeld. LG7 "Learning Versus Generalization in a Boolean Neural Network", by Johathan Shapiro. LG8* "On Stochastic Complexity and Admissible Models for Neural Network Classifiers", by Padhraic Smyth. LG9 "Asympotic slowing down of the nearest-neighbor classifier", by Robert R. Snapp, Demetri Psaltis and Santosh Venkatesh. LG10 "Remarks on Interpolation and Recognition Using Neural Nets", by Eduardo D. Sontag. LG11 "Epsilon-Entropy and the Complexity of Feedforward Neural Networks", by Robert C. Williamson. LEARNING SYSTEMS LS1 "Analysis of the Convergence Properties of Kohonen's LVQ", by John S. Baras and Anthony LaVigna. LS2* "A Framework for the Cooperation of Learning Algorithms", by Leon Bottou and Patrick Gallinari. LS3 "Back-Propagation is Sensitive to Initial Conditions", by John F. Kolen and Jordan Pollack. LS4 "Discovering Discrete Distributed Representations with Recursive Competitive Learning", by Michael C. Mozer. LS5* "From Competitive Learning to Adaptive Mixtures of Experts", by Steven J. Nowlan and Geoffrey Hinton. LS6 "ALCOVE: A connectionist Model of Category Learning", by John K. Kruschke. LS7 "Transforming NN Output Activation Levels to Probability Distributions", by John S. Denker and Yann LeCunn. LS8* "Closed-Form Inversion of Backropagation Networks: Theory and Optimization Issues", by Michael L. Rossen. LOCALIZED BASIS FUNCTIONS LBF1 "Computing with Arrays of Bell Shaped Functions Bernstein Polynomials and the Heat Equation", by Pierre Baldi. LBF2 "Function Approximation Using Multi-Layered Neural Networks with B-Spline Receptive Fields", by Stephen H. Lane, David Handelman, Jack Gelfand and Marshall Flax. LBF3 "A Resource-Allocating Neural Network for Function Interpolation" by John Platt. LBF4 "Adaptive Range Coding", by B.E. Rosen, J.M. Goodwin and J.J. Vidal. LBF5 "Oriented Nonradial Basis Function Networks for Image Coding and Analysis", by Avi Saha, Jim christian, D.S. Tang and Chuan-Lin Wu. LBF6 "A Tree-Structured Network for Approximation on High-Dimensional Spaces", by T. Sanger. LBF7 "Spherical Units as Dynamic Reconfigurable Consequential Regions and their Implications for Modeling Human Learning and Generalization", by Stephen Jose Hanson and Mark Gluck. LBF8 "Feedforward Neural Networks: Analysis and Synthesis Using Discrete Affine Wavelet Transformations", by Y.C. Pati and P.S. Krishnaprasad. LBF9 "A Network that Learns from Unreliable Data and Negative Examples", by Fredico Girosi, Tomaso Poggio and Bruno Caprile. LBF10 "How Receptive Field Parameters Affect Neural Learning", by Bartlett W. Mel and Stephen Omohundro. MEMORY SYSTEMS MS1 "The Devil and the Network: What Sparsity Implies to Robustness and Memory", by Sanjay Biswas and Santosh Venkatesh. MS2 "Cholinergic modulation selective for intrinsic fiber synapses may enhance associative memory properties of piriform cortex", by Michael E. Hasselmo, Brooke Anderson and James Bower. MS3 "Associative Memory in a Network of 'Biological' Neurons", by Wulfram Gerstner. MS4 "A Learning Rule for Guaranteed CAM Storage of Analog Patterns and Continuous Sequences in a Network of 3N^2 Weights", by William Baird. VLSI IMPLEMENTATIONS VLSI1 "A Highly Compact Linear Weight Function Based on the use of EEPROMs", by A. Krammer, C.K. Sin, R. Chu and P.K. Ko. VLSI2 "Back Propagation Implementation on the Adaptive Solutions Neurocomputer Chip", Hal McCartor. VLSI3 "Analog Non-Volatile VLSI Neural Network Chip and Back-Propagation Training", by Simon Tam, Bhusan Gupta, Hernan A. Castro and Mark Holler.