NIPS 1991: Denver, CO, USA

Neurobiology

• J. Allan Hobson, Adam N. Mamelak, Jeffrey P. Sutton:
  Models Wanted: Must Fit Dimensions of Sleep and Dreaming. 3-10
  
• Mark Sydorenko, Eric D. Young:
  Stationarity of Synaptic Coupling Strength Between Neurons with Nonstationary Discharge Properties. 11-18
  
• Peter Dayan, Geoffrey J. Goodhill:
  Perturbing Hebbian Rules. 19-26
  
• Robert R. de Ruyter van Steveninck, William Bialek:
  Statistical Reliability of a Blowfly Movement-Sensitive Neuron. 27-34
  
• Bartlett W. Mel:
  The Clusteron: Toward a Simple Abstraction for a Complex Neuron. 35-42
  
• Öjvind Bernander, Christof Koch, Rodney J. Douglas:
  Network Activity Determines Spatio-Temporal Integration in Single Cells. 43-50
  
• Anthony M. Zador, Brenda J. Claiborne, Thomas H. Brown:
  Nonlinear Pattern Separation in Single Hippocampal Neurons with Active Dendritic Membrane. 51-58
  
• Anthony J. Bell:
  Self-organization in Real Neurons: Anti-Hebb in 'Channel Space'? 59-66
  
• Adi R. Bulsara, E. W. Jacobs, Frank Moss:
  Single Neuron Model: Response to Weak Modulation in the Presence of Noise. 67-74
  
• A. B. Bonds:
  Dual Inhibitory Mechanisms for Definition of Receptive Field Characteristics in a Cat Striate Cortex. 75-82
  
• Klaus Obermayer, Klaus Schulten, Gary G. Blasdel:
  A Comparison between a Neural Network Model for the Formation of Brain Maps and Experimental Data. 83-90
  
• Ron Keesing, David G. Stork, Carla J. Shatz:
  Retinogeniculate Development: The Role of Competition and Correlated Retinal Activity. 91-97
  

Neuro-Dynamics

• James T. Buchanan:
  Locomotion in a Lower Vertebrate: Studies of the Cellular Basis of Rhythmogenesis and Oscillator Coupling. 101-108
  
• Kenji Doya, Shuji Yoshizawa:
  Adaptive Synchronization of Neural and Physical Oscillators. 109-116
  
• Heinz G. Schuster, Christof Koch:
  Burst Synchronization without Frequency Locking in a Completely Solvable Neural Network Model. 117-124
  
• David Horn, Marius Usher:
  Oscillatory Model of Short Term Memory. 125-132
  

Speech

• Patrick Haffner, Alex Waibel:
  Multi-State Time Delay Networks for Continuous Speech Recognition. 135-142
  
• José Carlos Príncipe, Bert de Vries, Jyh-Ming Kuo, Pedro Guedes de Oliveira:
  Modeling Applications with the Focused Gamma Net. 143-150
  
• Esther Levin, Roberto Pieraccini, Enrico Bocchieri:
  Time-Warping Network: A Hybrid Framework for Speech Recognition. 151-158
  
• Elliot Singer, Richard Lippmann:
  Improved Hidden Markov Models Speech Recognition Using Radial Basis Function Networks. 159-166
  
• Steve Renals, Nelson Morgan, Hervé Bourlard, Horacio Franco, Michael Cohen:
  Connectionist Optimisation of Tied Mixture Hidden Markov Models. 167-174
  
• Yoshua Bengio, Renato de Mori, Giovanni Flammia, Ralf Kompe:
  Neural Network - Gaussian Mixture Hybrid for Speech Recognition or Density Estimation. 175-182
  
• Alex Waibel, Ajay N. Jain, Arthur E. McNair, Joe Tebelskis, Louise Osterholtz, Hiroaki Saito, Otto Schmidbauer, Tilo Sloboda, Monika Woszczyna:
  JANUS: Speech-to-Speech Translation Using Connectionist and Non-Connectionist Techniques. 183-190
  
• Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato, Michael I. Jordan:
  Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. 191-198
  
• Mark A. Fanty, Ronald A. Cole, Krist Roginski:
  English Alphabet Recognition with Telephone Speech. 199-206
  

Language

• Ajay N. Jain:
  Generalization Performance in PARSEC - A Structured Connectionist Parsing Architecture. 209-216
  
• Gadi Pinkus:
  Constructing Proofs in Symmetric Networks. 217-224
  
• Prahlad Gupta, David S. Touretzky:
  A Connectionist Learning Approach to Analyzing Linguistic Stress. 225-232
  
• Ronald A. Sumida, Michael G. Dyer:
  Propagation Filters in PDS Networks for Sequencing and Ambiguity Resolution. 233-240
  
• Yeshwant K. Muthusamy, Ronald A. Cole:
  A Segment-Based Automatic Language Identification System. 241-248
  

Temporal Sequences

• Satinder P. Singh:
  The Efficient Learning of Multiple Task Sequences. 251-258
  
• Gerald Tesauro:
  Practical Issues in Temporal Difference Learning. 259-266
  
• Hermann Hild, Johannes Feulner, Wolfram Menzel:
  HARMONET: A Neural Net for Harmonizing Chorales in the Style of J. S. Bach. 267-274
  
• Michael Mozer:
  Induction of Multiscale Temporal Structure. 275-282
  
• Jeffrey P. Sutton, Adam N. Mamelak, J. Allan Hobson:
  Network Model of State-Dependent Sequencing. 283-290
  
• Jürgen Schmidhuber:
  Learning Unambiguous Reduced Sequence Descriptions. 291-298
  

Recurrent Networks

• Jerome Connor, Les E. Atlas, Douglas R. Martin:
  Recurrent Networks and NARMA Modeling. 301-308
  
• Raymond L. Watrous, Gary M. Kuhn:
  Induction of Finite-State Automata Using Second-Order Recurrent Networks. 309-317
  
• C. Lee Giles, Clifford B. Miller, Dong Chen, Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee:
  Extracting and Learning an Unknown Grammar with Recurrent Neural Networks. 317-324
  
• Janet Wiles, Anthony Bloesch:
  Operators and Curried Functions: Training and Analysis of Simple Recurrent Networks. 325-332
  
• Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee:
  Green's Function Method for Fast On-Line Learning Algorithm of Recurrent Neural Networks. 333-340
  
• Trent E. Lange:
  Dynamically-Adaptive Winner-Take-All Networks. 341-348
  

Vision

• David A. Robinson:
  Information Processing to Create Eye Movements. 351-355
  
• Emad N. Eskandar, Barry J. Richmond, John A. Hertz, Lance M. Optican, Troels W. Kjær:
  Decoding of Neuronal Signals in Visual Pattern Recognition. 356-363
  
• Davi Geiger, Ricardo A. Marques Pereira:
  Learning How to Teach or Selecting Minimal Surface Data. 364-371
  
• Suzanna Becker, Geoffrey E. Hinton:
  Learning to Make Coherent Predictions in Domains with Discontinuities. 372-379
  
• Paul A. Viola, Stephen G. Lisberger, Terrence J. Sejnowski:
  Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion. 380-387
  
• Trevor Darrell, Alex Pentland:
  Against Edges: Function Approximation with Multiple Support Maps. 388-395
  
• Paul R. Cooper, Peter N. Prokopowicz:
  Markov Random Fields Can Bridge Levels of Abstraction. 396-403
  
• Amnon Shashua:
  Illumination and View Position in 3D Visual Recognition. 404-411
  
• Alexandre Pouget, Stephen A. Fisher, Terrence J. Sejnowski:
  Hierarchical Transformation of Space in the Visual System. 412-419
  
• Subutai Ahmad:
  VISIT: A Neural Model of Covert Visual Attention. 420-427
  
• Eric Mjolsness:
  Visual Grammars and Their Neural Nets. 428-435
  
• Michael Mozer, Richard S. Zemel, Marlene Behrmann:
  Learning to Segment Images Using Dynamic Feature Binding. 436-443
  
• Hayit Greenspan, Rodney M. Goodman, Rama Chellappa:
  Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery. 444-451
  
• Ronen Basri, Shimon Ullman:
  Linear Operator for Object Recognition. 452-459
  
• Nathan Intrator, Joshua I. Gold, Heinrich H. Bülthoff, Shimon Edelman:
  3D Object Recognition Using Unsupervised Feature Extraction. 460-467
  

Optical Character Recognition

• Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla:
  Structural Risk Minimization for Character Recognition. 471-479
  
• Hans Peter Graf, Craig R. Nohl, Jan Ben:
  Image Segmentation with Networks of Variable States. 480-487
  
• Ofer Matan, Christopher J. C. Burges, Yann LeCun, John S. Denker:
  Multi-Digit Recognition Using a Space Displacement Neural Network. 488-495
  
• James D. Keeler, David E. Rumelhart:
  A Self-Organizing Integrated Segmentation and Recognition Neural Net. 496-503
  
• Gale Martin, Mosfeq Rashid:
  Recognizing Overlapping Hand-Printed Characters by Centered-Object Integrated Segmentation and Recognition. 504-511
  
• Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow:
  Adaptive Elastic Models for Hand-Printed Character Recognition. 512-519
  

Control and Planning

• Tony J. Prescott, John E. W. Mayhew:
  Obstacle Avoidance through Reinforcement Learning. 523-530
  
• Sebastian Thrun, Knut Möller:
  Active Exploration in Dynamic Environments. 531-538
  
• Michael D. Lemmon:
  Oscillatory Neural Fields for Globally Optimal Path Planning. 539-546
  
• Hiroaki Gomi, Mitsuo Kawato:
  Recognition of Manipulated Objects by Motor Learning. 547-554
  
• Gary M. Scott, Jude W. Shavlik, W. Harmon Ray:
  Refined PID Controllers Using Neural Networks. 555-562
  
• Carlos D. Brody:
  Fast Learning with Predictive Forward Models. 563-570
  
• Andrew W. Moore:
  Fast, Robust Adaptive Control by Learning only Forward Models. 571-578
  
• Patrice Simard, Yann LeCun:
  Reverse TDNN: An Architecture For Trajectory Generation. 579-588
  
• David DeMers, Kenneth Kreutz-Delgado:
  Learning Global Direct Inverse Kinematics. 589-595
  
• Paul Dean, John E. W. Mayhew, Pat Langdon:
  A Neural Net Model for Adaptive Control of Saccadic Accuracy by Primate Cerebellum and Brainstem. 595-602
  
• Thomas J. Anastasio:
  Learning in the Vestibular System: Simulations of Vestibular Compensation Using Recurrent Back-Propagation. 603-610
  
• N. E. Berthier, Satinder P. Singh, Andrew G. Barto, James C. Houk:
  A Cortico-Cerebellar Model that Learns to Generate Distributed Motor Commands to Control a Kinematic Arm. 611-618
  
• Ealan A. Henis, Tamar Flash:
  A Computational Mechanism to Account for Averaged Modified Hand Trajectories. 619-626
  
• Menashe Dornay, Yoji Uno, Mitsuo Kawato, Ryoji Suzuki:
  Simulation of Optimal Movements Using the Minimum-Muscle-Tension-Change Model. 627-634
  

Applications

• Marwan A. Jabri, Stephen Pickard, P. Leong, Z. Chi, Barry Flower, Y. Xie:
  ANN Board Classification for Heart Defibrillators. 637-644
  
• Armando Manduca, Paul Christy, Richard L. Ehman:
  Neural Network Diagnosis of Avascular Necrosis from Magnetic Resonance Images. 645-650
  
• Rita Venturini, William W. Lytton, Terrence J. Sejnowski:
  Neural Network Analysis of Event Related Potentials and Electroencephalogram Predicts Vigilance. 651-658
  
• Martin Röscheisen, Reimar Hofmann, Volker Tresp:
  Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency. 659-666
  
• Padhraic Smyth, Jeff Mellstrom:
  Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models. 667-674
  
• Sylvie Thiria, Carlos Mejia, Fouad Badran, Michel Crépon:
  Multimodular Architecture fir Remote Sensing Options. 675-682
  
• John E. Moody, Joachim Utans:
  Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction. 683-690
  
• Sheri L. Gish, Mario Blaum:
  Adaptive Development of Connectionist Decoders for Complex Error-Correcting Codes. 691-697
  
• Ah Chung Tsoi:
  Application of Neural Network Methodology to the Modelling of the Yield Strength in a Steel Rolling Plate Mill. 698-705
  
• Donald T. Freeman:
  Computer Recognition of Wave Location in Graphical Data by a Neural Network. 706-713
  
• Hilary Tunley:
  A Neural Network for Motion Detection of Drift-Balanced Stimuli. 714-721
  
• Mark W. Goudreau, C. Lee Giles:
  Neural Network Routing for Random Multistage Interconnection Networks. 722-729
  
• John C. Platt, Federico Faggin:
  Networks for the Separation of Sources that Are Superimposed and Delayed. 730-737
  

Implementation

• Alice M. Chiang, Michael L. Chuang, Jeffrey R. LaFranchise:
  CCD Neural Network Processors for Pattern Recognition. 741-747
  
• Charles F. Neugebauer, Amnon Yariv:
  A Parallel Analog CCD/CMOS Signal Processor. 748-755
  
• Ronald G. Benson, Tobi Delbrück:
  Direction Selective Silicon Retina that Uses Null Inhibition. 756-763
  
• Kwabena Boahen, Andreas G. Andreou:
  A Contrast Sensitive Silicon Retina with Reciprocal Synapses. 764-772
  
• Eduard Säckinger, Bernhard E. Boser, Lawrence D. Jackel:
  A Neurocomputer Board Based on the ANNA Neural Network Chip. 773-780
  
• Phil Kohn, Jeff Bilmes, Nelson Morgan, James Beck:
  Software for ANN Training on a Ring Array Processor. 781-788
  
• David Kirk, Kurt W. Fleischer, Lloyd Watts, Alan H. Barr:
  Constrained Optimization Applied to the Parameter Setting Problem for Analog Circuits. 789-796
  
• John G. Harris:
  Segmentation Circuits Using Constrained Optimization. 797-804
  
• Marc H. Cohen, Philippe O. Pouliquen, Andreas G. Andreou:
  Analog LSI Implementation of an Auto-Adaptive Network for Real-Time Separation of Independent Signals. 805-812
  
• John Lazzaro:
  Temporal Adaptation in a Silicon Auditory Nerve. 813-820
  
• Dana Z. Anderson, Claus Benkert, Verena Hebler, Ju-Seog Jang, Don Montgomery, Mark Saffman:
  Optical Implementation of a Self-Organizing Feature Extractor. 821-828
  

Learning and Generalization

• Vladimir Vapnik:
  Principles of Risk Minimization for Learning Theory. 831-838
  
• David J. C. MacKay:
  Bayesian Model Comparison and Backprop Nets. 839-846
  
• John E. Moody:
  The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems. 847-854
  
• David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire:
  Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. 855-862
  
• J. Stephen Judd:
  Constant-Time Loading of Shallow 1-Dimensional Networks. 863-870
  
• Joshua Alspector, Anthony Jayakumar, Stephan Luna:
  Experimental Evaluation of Learning in a Neural Microsystem. 871-878
  
• John Shawe-Taylor:
  Threshold Network Learning in the Presence of Equivalences. 879-886
  
• Barak A. Pearlmutter:
  Gradient Descent: Second Order Momentum and Saturating Error. 887-894
  
• Patrice Simard, Bernard Victorri, Yann LeCun, John S. Denker:
  Tangent Prop - A Formalism for Specifying Selected Invariances in an Adaptive Network. 895-903
  
• Alberto Bertoni, Paola Campadelli, Anna Morpurgo, Sandra Panizza:
  Polynomial Uniform Convergence of Relative Frequencies to Probabilities. 904-911
  
• Yoav Freund, David Haussler:
  Unsupervised Learning of Distributions of Binary Vectors Using 2-Layer Networks. 912-919
  
• Anthony Kuh, Thomas Petsche, Ronald L. Rivest:
  Incrementally Learning Time-Varying Half Planes. 920-927
  
• Chuanyi Ji, Demetri Psaltis:
  The VC-Dimension versus the Statistical Capacity of Multilayer Networks. 928-935
  
• Ying Zhao, Christopher G. Atkeson:
  Some Approximation Properties of Projection Pursuit Learning Networks. 936-943
  
• Kai-Yeung Siu, Jehoshua Bruck:
  Neural Computing with Small Weights. 944-949
  
• Anders Krogh, John A. Hertz:
  A Simple Weight Decay Can Improve Generalization. 950-957
  
• Stephen M. Omohundro:
  Best-First Model Merging for Dynamic Learning and Recognition. 958-965
  

Architectures and Algorithms

• Clayton McMillan, Michael Mozer, Paul Smolensky:
  Rule Induction through Integrated Symbolic and Subsymbolic Processing. 969-976
  
• Geoffrey G. Towell, Jude W. Shavlik:
  Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules. 977-984
  
• Michael I. Jordan, Robert A. Jacobs:
  Hierarchies of Adaptive Experts. 985-992
  
• Steven J. Nowlan, Geoffrey E. Hinton:
  Adaptive Soft Weight Tying using Gaussian Mixtures. 993-1000
  
• Paul W. Munro:
  Repeat Until Bored: A Pattern Selection Strategy. 1001-1008
  
• Christian Darken, John E. Moody:
  Towards Faster Stochastic Gradient Search. 1009-1016
  
• Nicol N. Schraudolph, Terrence J. Sejnowski:
  Competitive Anti-Hebbian Learning of Invariants. 1017-1024
  
• Paul E. Stolorz:
  Merging Constrained Optimisation with Deterministic Annealing to Solve Combinatorially Hard Problems. 1025-1032
  
• Petri Koistinen, Lasse Holmström:
  Kernel Regression and Backpropagation Training With Noise. 1033-1039
  
• Robert C. Williamson, Peter L. Bartlett:
  Splines, Rational Functions and Neural Networks. 1040-1047
  
• John Moody, Norman Yarvin:
  Networks with Learned Unit Response Functions. 1048-1055
  
• Nicholas J. Redding, Tom Downs:
  Learning in Feedforward Networks with Nonsmooth Functions. 1056-1063
  
• Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus:
  Iterative Construction of Sparse Polynomial Approximations. 1064-1071
  
• Mike Wynne-Jones:
  Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks. 1072-1079
  
• Sowmya Ramachandran, Lorien Y. Pratt:
  Information Measure Based Skeletonisation. 1080-1087
  
• David Rogers:
  Data Analysis Using G/Splines. 1088-1095
  
• John S. Bridle, Anthony J. R. Heading, David J. C. MacKay:
  Unsupervised Classifiers, Mutual Information and 'Phantom Targets'. 1096-1101
  
• Martin S. Glassman:
  A Network of Localized Linear Discriminants. 1102-1109
  
• David J. Montana:
  A Weighted Probabilistic Neural Network. 1110-1117
  
• Igor Grebert, David G. Stork, Ron Keesing, Steve Mims:
  Network Generalization for Production: Learning and Producing Styled Letterforms. 1118-1124
  
• John B. Hampshire II, B. V. K. Vijaya Kumar:
  Shooting Craps in Search of an Optimal Strategy for Training Connectionist Pattern Classifiers. 1125-1132
  
• Dietrich Wettschereck, Thomas G. Dietterich:
  Improving the Performance of Radial Basis Function Networks by Learning Center Locations. 1133-1140
  
• Hans-Ulrich Bauer, Klaus Pawelzik, Theo Geisel:
  A Topographic Product for the Optimization of Self-Organizing Feature Maps. 1141-1147
  

Performance Comparisions

• Jakob Bernasconi, Karl Gustafson:
  Human and Machine 'Quick Modeling'. 1151-1158
  
• Jenq-Neng Huang, Hang Li, Martin Maechler, R. Douglas Martin, Jim Schimert:
  A Comparison of Projection Pursuit and Neural Network Regression Modeling. 1159-1166
  
• Leonard G. C. Hamey:
  Benchmarking Feed-Forward Neural Networks: Models and Measures. 1167-1174
  

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