NIPS*93 MEETING PROGRAM and REGISTRATION REMINDER The 1993 Neural Information Processing Systems (NIPS*93) meeting is the seventh meeting of an inter-disciplinary conference which brings together neuroscientists, engineers, computer scientists, cognitive scientists, physicists, and mathematicians interested in all aspects of neural processing and computation. There will be an afternoon of tutorial presentations (Nov. 29), two and a half days of regular meeting sessions (Nov. 30 - Dec. 2), and two days of focused workshops at a nearby ski area (Dec. 3-4). An electronic copy of the 1993 NIPS registration brochure is available in postscript format via anonymous ftp at helper.systems.caltech.edu in /pub/nips/NIPS_93_brochure.ps.Z. For a hardcopy of the brochure or other information, please send a request to nips93@systems.caltech.edu or to: NIPS Foundation, P.O. Box 60035, Pasadena, CA 91116-6035. EARLY REGISTRATION DEADLINE (for $100 discount): Oct. 30 _________________ NIPS*93 ORAL PRESENTATIONS PROGRAM Tues. AM: Cognitive Science 8:30 Invited Talk: Jeff Elman, UC San Diego: From Weared to Wore: A Connectionist Account of the History of the Past Tense 9:00 Richard O. Duda, San Jose State Univ.: Connectionist Models for Auditory Scene Analysis 9:20 Reza Shadmehr and Ferdinando A. Mussa-Ivaldi, MIT: Computational Elements of the Adaptive Controller of the Human Arm 9:40 Catherine Stevens and Janet Wiles, University of Queensland: Tonal Music as a Componential Code: Learning Temporal Relationships Between and Within Pitch and Timing Components 10:00 Poster Spotlights: Thea B. Ghiselli-Crispa and Paul Munro, Univ. of Pittsburgh: Emergence of Global Structure from Local Associations Tony A. Plate, University of Toronto: Estimating Structural Similarity by Vector Dot Products of Holographic Reduced Representations 10:10 BREAK Speech Recognition 10:40 Jose C. Principe, Hui-H. Hsu and Jyh-M. Kuo, Univ. of Florida: Analysis of Short Term Neural Memory Structures for Nonlinear Prediction 11:00 Eric I. Chang and Richard P. Lippmann, MIT Lincoln Laboratory: Figure of Merit Training for Detection and Spotting 11:20 Gregory J. Wolff, K. Venkatesh Prasad, David G. Stork and Marcus Hennecke, Ricoh California Research Center: Lipreading by Neural Networks: Visual Preprocessing, Learning and Sensory Integration 11:40 Poster Spotlights: Steve Renals, Mike Hochberg and Tony Robinson, Cambridge University: Learning Temporal Dependencies In Large-Scale Connectionist Speech Recognition Ying Zhao, John Makhoul, Richard Schwartz and George Zavaliagkos, BBN Systems and Technologies: Segmental Neural Net Optimization for Continuous Speech Recognition 11:50 Rod Goodman, Caltech: Posner Memorial Lecture Tues. PM: Temporal Prediction and Control 2:00 Invited Talk: Doyne Farmer, Prediction Co.: Time Series Analysis of Nonlinear and Chaotic Time Series: State Space Reconstruction and the Curse of Dimensionality 2:30 Kenneth M. Buckland and Peter D. Lawrence, Univ. of British Columbia: Transition Point Dynamic Programming 2:50 Gary W. Flake, Guo-Zhen Sun, Yee-Chun Lee and Hsing-Hen Chen, University of Maryland: Exploiting Chaos to Control The Future 3:10 Satinder P. Singh, Andrew G. Barto, Roderic Grupen and Christopher Connolly, University of Massachusetts: Robust Reinforcement Learning in Motion Planning 3:30 BREAK Theoretical Analysis 4:00 Scott Kirkpatrick, Naftali Tishby, Lidror Troyansky, The Hebrew Univ. of Jerusalem, and Geza Gyorgi, Eotvos Univ.: The Statistical Mechanics of K-Satisfaction 4:20 Santosh S. Venkatesh, Changfeng Wang, Univ. of Pennsylvania, and Stephen Judd, Siemens Corporate Research: When To Stop: On Optimal Stopping And Effective Machine Size In Learning 4:40 Wolfgang Maass, Technische Univ. Graz: Agnostic PAC-Learning Functions on Analog Neural Nets 5:00 H.N. Mhaskar, California State Univ. and Charles A. Micchelli, IBM: How To Choose An Activation Function 5:20 Poster Spotlights Iris Ginzburg, Tel Aviv Univ. and Haim Sompolinsky, Hebrew Univ.: Correlation Functions on a Large Stochastic Neural Network Xin Wang, Qingnan Li and Edward K. Blum, USC: Asynchronous Dynamics of Continuous-Time Neural Networks Tal Grossman and Alan Lapedes, Los Alamos National Laboratory: Use of Bad Training Data for Better Predictions Wed. AM: Learning Algorithms 8:30 Invited Talk: Geoff Hinton, Univ. of Toronto: Using the Minimum Description Length Principle to Discover Factorial Codes 9:00 Richard S. Zemel, Salk Institute, and G. Hinton, Univ. of Toronto: Developing Population Codes By Minimizing Description Length 9:20 Sreerupa Das and Michael C. Mozer, University of Colorado: A Hybrid Gradient-Descent/Clustering Technique for Finite State Machine Induction 9:40 Eric Saund, Xerox Palo Alto Research Center: Unsupervised Learning of Mixtures of Multiple Causes in Binary Data 10:00 BREAK 10:30 A. Uzi Levin and Todd Leen, Oregon Graduate Institute: Fast Pruning Using Principal Components 10:50 Christoph Bregler and Stephen Omohundro, ICSI: Surface Learning with Applications to Lip Reading 11:10 Melanie Mitchell, Santa Fe Inst. and John H. Holland, Univ. Michigan: When Will a Genetic Algorithm Outperform Hill Climbing 11:30 Oded Maron and Andrew W. Moore, MIT: Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation 11:50 Poster Spotlights: Zoubin Ghahramani and Michael I. Jordan, MIT: Supervised Learning from Incomplete Data via an EM Approach Mats Osterberg and Reiner Lenz, Linkoping Univ. Unsupervised Parallel Feature Extraction from First Principles Terence D. Sanger, LAC-USC Medical Center: Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples Patrice Y. Simard and Edi Sackinger, AT&T Bell Laboratories: Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering Wed. PM: Neuroscience 2:00 Invited Talk: Eve Marder, Brandeis Univ.: Dynamic Modulation of Neurons and Networks 2:30 Ojvind Bernander, Rodney Douglas and Christof Koch, Caltech: Amplifying and Linearizing Apical Synaptic Inputs to Cortical Pyramidal Cells 2:50 Christiane Linster and David Marsan, ESPCI, Claudine Masson and Michel Kerzberg, CNRS: Odor Processing in the Bee: a Preliminary Study of the Role of Central Input to the Antennal Lobe 3:10 M.G. Maltenfort, R. E. Druzinsky, C. J. Heckman and W. Z. Rymer, Northwestern Univ.: Lower Boundaries of Motoneuron Desynchronization Via Renshaw Interneurons 3:30 BREAK Visual Processing 4:00 K. Obermayer, The Salk Institute, L. Kiorpes, NYU and Gary G. Blasdel, Harvard Medical School: Development of Orientation and Ocular Dominance Columns in Infant Macaques 4:20 Yoshua Bengio, Yann Le Cun and Donnie Henderson, AT&T Bell Labs: Globally Trained Handwritten Word Recognizer using Spatial Representation, Spatial Displacement Neural Networks and Hidden Markov Models 4:40 Trevor Darrell and A. P. Pentland, MIT: Classification of Hand Gestures using a View-based Distributed Representation 5:00 Ko Sakai and Leif H. Finkel, Univ. of Pennsylvania: A Network Mechanism for the Determination of Shape-from-Texture 5:20 Video Poster Spotlights (to be announced) Thurs. AM: Implementations and Applications 8:30 Invited Talk: Dan Seligson, Intel: A Radial Basis Function Classifier with On-chip Learning 9:00 Michael A. Glover, Current Technology, Inc. and W. Thomas Miller III, University of New Hampshire: A Massively-Parallel SIMD Processor for Neural Network and Machine Vision Application 9:20 Steven S. Watkins, Paul M. Chau, and Mark Plutowski, UCSD, Raoul Tawel and Bjorn Lambrigsten, JPL: A Hybrid Radial Basis Function Neurocomputer 9:40 Gert Cauwenberghs, Caltech : A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics 10:00 BREAK 10:30 Invited Talk: Paul Refenes, University College London: Neural Network Applications in the Capital Markets 11:00 Jane Bromley, Isabelle Guyon, Yann Le Cun, Eduard Sackinger and Roopak Shah, AT&T Bell Laboratories: Signature Verification using a "Siamese" Time Delay Neural Network 11:20 John Platt and Ralph Wolf, Synaptics, Inc.: Postal Address Block Location Using a Convolutional Locator Network 11:40 Shumeet Baluja and Dean Pomerleau, Carnegie Mellon University: Non-Intrusive Gaze Tracking Using Artificial Neural Networks 12:00 Adjourn to Vail for Workshops _____________________ NIPS*93 POSTER PROGRAM Tues. PM Posters: Cognitive Science (CS) CS-1 Blasig Using Backpropagation to Automatically Generate Symbolic Classif ication Rules CS-2 Munro, Ghiselli-Crispa Emergence of Global Structure from Local Associa tions CS-3 Plate Estimating structural similarity by vector dot products of Holog raphic Reduced Representations CS-4 Shultz, Elman Analyzing Cross Connected Networks CS-5 Sperduti Encoding of Labeled Graphs by Labeling RAAM Speech Processing (SP) SP-1 Farrell, Mammone Speaker Recognition Using Neural Tree Networks SP-2 Hirayama, Vatikiotis-Bateson, Kawato Inverse Dynamics of Speech Motor Control SP-3 Renals, Hochberg, Robinson Learning Temporal Dependencies In Large-Scale Co nnectionist Speech Recognition SP-4 Zhao, Makhoul, Schwartz, Zavaliagkos Segmental Neural Net Optimizatio n for Continuous Speech Recognition Control, Navigation and Planning (CT) CT-1 Atkeson Using Local Trajectory Optimizers To Speed Up Global Optimizatio n In Dynamic Programming CT-2 Boyan, Littman A Reinforcement Learning Scheme for Packet Routing Using a Network of Neural Networks CT-3 Cohn Queries and Exploration Using Optimal Experiment Design CT-4 Duff, Barto Monte Carlo Matrix Inversion and Reinforcement Learning CT-5 Gullapalli, Barto Convergence of Indirect Adaptive Asynchronous Dynamic Pr ogramming Algorithms CT-6 Jaakkola, Jordan, Singh Stochastic Convergence Of Iterative DP Algorithm s CT-7 Moore The Parti-game Algorithm for Variable Resolution Reinforcement L earning in Multidimensional State-spaces CT-8 Nowlan, Cacciatore Mixtures of Controllers for Jump Linear and Non-linear P lants CT-9 Wada, Koike, Vatikiotis-Bateson, Kawato A Computational Model for Cursiv e Handwriting Based on the Minimization Principle Learning Theory, Generalization and Complexity (LT) LT-01 Cortes, Jackel, Solla, Vapnik, Denker Learning Curves: Asymptotic Valu es and Rates of Convergence LT-02 Fefferman Recovering A Feed-Forward Net From Its Output LT-03 Grossman, Lapedes Use of Bad Training Data for Better Predictions LT-04 Hassibi, Sayed, Kailath H-inf Optimality Criteria for LMS and Backpropag ation LT-05 Hush, Horne Bounds on the complexity of recurrent neural network imp lementations of finite state machines LT-06 Ji A Bound on Generalization Error Using Network-Parameter-Dependen t Information and Its Applications LT-07 Kowalczyk Counting function theorem for multi-layer networks LT-08 Mangasarian, Solodov Backpropagation Convergence Via Deterministic No nmonotone Perturbed Minimization LT-09 Plutowski, White Delete-1 Cross-Validation Estimates IMSE LT-10 Schwarze, Hertz Discontinuous Generalization in Large Commitee Machines LT-11 Shapiro, Prugel-Bennett Non-Linear Statistical Analysis and Self-Organiz ing Competitive Networks LT-12 Wahba Structured Machine Learning for 'Soft' Classification, with Smoo thing Spline ANOVA Models and Stacked Tuning, Testing and Evaluation LT-13 Watanabe Solvable models of artificial neural networks LT-14 Wiklicky On the Non-Existence of a Universal Learning Algorithm for Recur rent Neural Networks Dynamics/Statistical Analysis (DS) DS-1 Coolen, Penney, Sherrington Coupled Dynamics of Fast Neurons and Slo w Interactions DS-2 Garzon, Botelho Observability of neural network behavior DS-3 Gerstner, van Hemmen How to Describe Neuronal Activity: Spikes, Rates , or Assemblies? DS-4 Ginzburg, Sompolinsky Correlation Functions on a Large Stochastic Neur al Network DS-5 Leen, Orr Momentum and Optimal Stochastic Search DS-6 Ruppin, Meilijson Optimal signalling in Attractor Neural Networks DS-7 Wang, Li, Blum Asynchronous Dynamics of Continuous-Time Neural Networks Recurrent Networks (RN) RN-1 Baird, Troyer, Eeckman Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Net RN-2 Bengio, Frasconi Credit Assignment through Time: Alternatives to Backprop agation RN-3 Kolen Fool's Gold: Extracting Finite State Machines From Recurrent Net work Dynamics RN-4 Movellan A Reinforcement Algorithm to Learn Trajectories with Stochastic Neural Networks RN-5 Saunders, Angeline, Pollack Structural and behavioral evolution of recurrent networks Applications (AP) AP-01 Baldi, Brunak, Chauvin, Krogh Hidden Markov Models in Molecular Biolog y: Parsing the Human Genome AP-02 Eeckman, Buhmann, Lades A Silicon Retina for Face Recognition AP-03 Flann A Hierarchal Approach to Recognizing On-line Cursive Handwriting AP-04 Graf, Cosatto, Ting Locating Address Blocks with a Neural Net System AP-05 Karunanithi Identifying Fault-Prone Software Modules Using Feed-Forw ard Networks: A Case Study AP-06 Keymeulen Comparison Training for a Rescheduling Problem in Neural Network s AP-07 Lapedes, Steeg Use of Adaptive Networks to Find Highly Predictable Prot ein Structure Classes AP-08 Schraudolph, Dayan, Sejnowski Using the TD(lambda) Algorithm to Learn an Evaluation Funcion for the Game of Go AP-09 Smyth Probabilistic Anomaly Detection in Dynamic Systems AP-10 Tishby, Singer Decoding Cursive Scripts Wed. PM posters: Learning Algorithms (LA) LA-01 Gold, Mjolsness Clustering with a Domain-Specific Distance Metric LA-02 Buhmann Central and Pairwise Data Clustering by Competitive Neural Netwo rks LA-03 de Sa Learning Classification without Labeled Data LA-04 Ghahramani, Jordan Supervised learning from incomplete data via an EM approach LA-05 Tresp, Ahmad, Neuneier Training Neural Networks with Deficient Data LA-06 Osterberg, Lenz Unsupervised Parallel Feature Extraction from First Prin ciples LA-07 Sanger Two Iterative Algorithms for Computing the Singular Value Decomp osition from Input/Output Samples LA-08 Leen, Kambhatla Fast Non-Linear Dimension Reduction LA-09 Schaal, Atkeson Assessing The Quality of Learned Local Models LA-10 Simard, Sackinger Efficient computation of complex distance metrics using hierarchical filtering LA-11 Tishby, Ron, Singer The Power of Amnesia LA-12 Wettscherek, Dietterich Locally Adaptive Nearest Neighbor Algorithms LA-13 Liu Robust Parameter Estimation and Model Selection for Neural Netwo rk Regression LA-14 Wolpert Bayesian Backpropagation Over Functions Rather Than Weights LA-15 Thodberg Bayesian Backprop in Action: Pruning, Ensembles, Error Bars and Application to Strectroscopy LA-16 Dietterich, Jain, Lanthop Dynamic Reposing for Drug Activity Prediction LA-17 Ginzburg, Horn Combined Neural Networks For Time Series Analysis LA-18 Graf, Simard Backpropagation without Multiplication LA-19 Harget, Bostock A Comparative Study of the Performance of a Modified Bum ptree with Radial Basis Function Networks and the Standard Multi-Layer Perceptron LA-20 Najafi, Cherkassky Adaptive Knot Placement Based on Estimated Secon d Derivative of Regression Surface Constructive/Pruning Algorithms (CP) CP-1 Fritzke Supervised Learning with Growing Cell Structures CP-2 Hassibi, Stork, Wolff Optimal Brain Surgeon: Extensions, streamlining and performance comparisons CP-3 Kamimura Generation of Internal Representations by alpha-transformation CP-4 Leerink, Jabri Constructive Learning Using Internal Representation Conf licts CP-5 Utans Learning in Compositional Hierarchies: Inducing the Structure of Objects from Data CP-6 Watanabe An Optimization Method of Layered Neural Networks Based on the M odified Information Criterion Neuroscience (NS) NS-01 Bialek, Ruderman Statistics of Natural Images: Scaling in the Woods NS-02 Boussard, Vibert Dopaminergic neuromodulation brings a dynamical plastici y to the retina NS-03 Doya, Selverston, Rowat A Hodgkin-Huxley Type Neuron Model that Learns S low Non-Spike Oscillation NS-04 Gusik, Eaton Directional Hearing by the Mauthner System NS-05 Horiuchi, Bishofberger, Koch Building an Analog VLSI, Saccadic Eye Mo vement System NS-06 Lewicki Bayesian Modeling and Classification of Neural Signals NS-07 Montague, Dayan, Sejnowski Foraging in an Uncertain Environment Usi ng Predictive Hebbian Learning NS-08 Rosen, Rumelhart, Knudsen A Connectionist Model of the Owl's Sound Localiz ation System NS-09 Sanger Optimal Unsupervised Motor Learning Predicts the Internal Repres entation of Barn Owl Head Movements NS-10 Siegal An Analog VLSI Model Of Central Pattern Generation In The Medici nal Leech NS-11 Usher, Stemmler, Koch High spike rate variability as a consequence of network amplification of local fluctuations Visual Processing (VP) VP-1 Ahmad Feature Densities are Required for Computing Feature Corresponce s VP-2 Buracas, Albright Proposed function of MT neurons' receptive field surroun ds: computing shapes of objects from velocity fields VP-3 Geiger, Diamantaras Resolving motion ambiguities VP-4 Mjolsness Two-Dimensional Object Localization by Coarse-to-fine Correlatio n Matching VP-5 Sajda, Finkel Dual Mechanisms for Neural Binding and Segmentation and Their Role in Cortical Integration VP-6 Yuille, Smirnakis, Xu Bayesian Self-Organization Implementations (IM) IM-01 Andreou, Edwards VLSI Phase Locking Architecture for Feature Linking in M ultiple Target Tracking Systems IM-02 Coggins, Jabri WATTLE: A Trainable Gain Analogue VLSI Neural Network IM-03 Elfadel, Wyatt The "Softmax" Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit Element IM-04 Muller, Kocheisen, Gunzinger High Performance Neural Net Simulation o n a Multiprocessor System with "Intelligent" Communication IM-05 Murray, Burr, Stork, et al. Digital Boltzmann VLSI for constraint sa tisfaction and learning IM-06 Niebur, Brettle Efficient Simulation of Biological Neural Networks on Ma ssively Parallel Supercomputers with Hypercube Architecture IM-07 Oliveira, Sangiovanni-Vincentelli Learning Complex Boolean Functions: Algo rithms and Applications IM-08 Shibata, Kotani, Yamashita et al. Implementing Intelligence on Silicon Usi ng Neuron-Like Functional MOS Transistors IM-09 Watts Event-Driven Simulation of Networks of Spiking Neurons