NIPS 1992: Denver, CO, USA
Stephen Jose Hanson, Jack D. Cowan, C. Lee Giles (Eds.): Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30 - December 3, 1992]. Morgan Kaufmann 1993 ISBN 1-55860-274-7
Part 1: Learning and Generalization
- Nathan Intrator: On the Use of Projection Pursuit Constraints for Training Neural Networks. 3-10
- Andreas Stolcke, Stephen M. Omohundro: Hidden Markov Model} Induction by Bayesian Model Merging. 11-18
- Kai-Yeung Siu, Vwani P. Roychowdhury, Thomas Kailath: Computing with Almost Optimal Size Neural Networks. 19-26
- Janet Wiles, Mark Ollila: Intersecting Regions: The Key to Combinatorial Structure in Hidden Unit Space. 27-33
- Tony Plate: Holographic Recurrent Networks. 34-41
- Harris Drucker, Robert E. Schapire, Patrice Simard: Improving Performance in Neural Networks Using a Boosting Algorithm. 42-49
- Patrice Simard, Yann LeCun, John S. Denker: Efficient Pattern Recognition Using a New Transformation Distance. 50-58
- Kai-Yeung Siu, Vwani P. Roychowdhury: Optimal Depth Neural Networks for Multiplication and Related Problems. 59-64
- Sreerupa Das, C. Lee Giles, Guo-Zheng Sun: Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages. 65-72
- Yaser S. Abu-Mostafa: A Method for Learning From Hints. 73-80
- Charles W. Anderson: Q-Learning with Hidden-Unit Restarting. 81-88
- J. Stephen Judd, Paul W. Munro: Nets with Unreliable Hidden Nodes Learn Error-Correcting Codes. 89-96
Part 2: Architectures and Algorithms
- Richard K. Belew: Interposing an Ontogenetic Model Between Genetic Algorithms and Neural Networks. 99-106
- J. Jeffrey Mahoney, Raymond J. Mooney: Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases. 107-114
- Mark B. Ring: Learning Sequential Tasks by Incrementally Adding Higher Orders. 115-122
- Bernd Fritzke: Kohonen Feature Maps and Growing Cell Structures - a Performance Comparison. 123-130
- Brian V. Bonnlander, Michael Mozer: Metamorphosis Networks: An Alternative to Constructive Models. 131-138
- Eric I. Chang, Richard Lippmann: A Boundary Hunting Radial Basis Function Classifier which Allocates Centers Constructively. 139-146
- Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik: Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. 147-155
- Yann LeCun, Patrice Simard, Barak A. Pearlmutter: Automatic Learning Rate Maximization in Large Adaptive Machines. 156-163
- Babak Hassibi, David G. Stork: Second Order Derivatives for Network Pruning: Optimal Brain Surgeon. 164-171
- Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Directional-Unit Boltzmann Machines. 172-179
- Gerhard Paass: Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm. 196-203
- Lorien Y. Pratt: Discriminability-Based Transfer between Neural Networks. 204-211
- Barry Flower, Marwan A. Jabri: Summed Weight Neuron Perturbation: An O(N) Improvement Over Weight Perturbation. 212-219
- William Finnoff, Ferdinand Hergert, Hans-Georg Zimmermann: Extended Regularization Methods for Nonconvergent Model Selection. 228-235
- Bill Baird, Todd Troyer, Frank H. Eeckman: Synchronization and Grammatical Inference in an Oscillating Elman Net. 236-243
- Gert Cauwenberghs: A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization. 244-251
Part 3: Control, Navigation, and Planning
- David DeMers, Kenneth Kreutz-Delgado: Global Regularization of Inverse Kinematics for Redundant Manipulators. 255-262
- Andrew W. Moore, Christopher G. Atkeson: Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping. 263-270
- Dean Pomerleau: Input Reconstruction Reliability Estimation. 279-286
- Tom M. Mitchell, Sebastian Thrun: Explanation-Based Neural Network Learning for Robot Control. 287-294
- Steven J. Bradtke: Reinforcement Learning Applied to Linear Quadratic Regulation. 295-302
- Christopher Bowman: Neural Network On-Line Learning Control of Spacecraft Smart Structures. 303-310
- Yoji Uno, Naohiro Fukumura, Ryoji Suzuki, Mitsuo Kawato: Integration of Visual and Somatosensory Information for Preshaping Hand in Grasping Movements. 311-318
- James K. Peterson: On Line Estimation of Optimal Control Sequences: HJB Estimators. 319-326
- Vijaykumar Gullapalli: Learning Control Under Extreme Uncertainty. 327-334
- Terence D. Sanger: A Practice Strategy for Robot Learning Control. 335-341
- Gerald Fahner, Rolf Eckmiller: Learning Spatio-Temporal Planning from a Dynamic Programming Teacher: Feed-Forward Neurocontrol for Moving Obstacle Avoidance. 342-349
- Charles M. Higgins, Rodney M. Goodman: Learning Fuzzy Rule-Based Neural Networks for Control. 350-357
Part 4: Visual Processing
- Suzanna Becker: Learning to Categorize Objects Using Temporal Coherence. 361-368
- Steven J. Nowlan, Terrence J. Sejnowski: Filter Selection Model for Generating Visual Motion Signals. 369-376
- Edward Stern, Ad Aertsen, Eilon Vaadia, Shaul Hochstein: Stimulus Encoding by Multidimensional Receptive Fields in Single Cells and Cell Populations in V1 of Awake Monkey. 377-384
- Suthep Madarasmi, Daniel Kersten, Ting-Chuen Pong: The Computation of Stereo Disparity for Transparent and for Opaque Surfaces. 385-392
- Joachim Utans, Gene Gindi: Improving Convergence in Hierarchical Matching Networks for Object Recognition. 401-408
- Carlos D. Brody: A Model of Feedback to the Lateral Geniculate Nucleus. 409-416
- Kevin E. Martin, Jonathan A. Marshall: Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections. 417-424
- Hayit Greenspan, Rodney M. Goodman: Remote Sensing Image Analysis via a Texture Classification Neural Network. 425-432
- Markus Lappe, Josef P. Rauschecker: Computation of Heading Direction from Optic Flow in Visual Cortex. 433-440
- Gale Martin, Mosfeq Rashid, David Chapman, James A. Pittman: Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters. 441-447
Part 5: Stochastic Learning and Analysis
- Todd K. Leen, John E. Moody: Weight Space Probability Densities in Stochastic Learning: I. Dynamics and Equilibria. 451-458
- William Finnoff: Diffusion Approximations for the Constant Step Size Backpropagation Algorithm and Resistance to Local Minima. 459-466
- Radford M. Neal: Bayesian Learning via Stochastic Dynamics. 475-482
- Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby: Information, Prediction, and Query by Committee. 483-490
- Alan F. Murray, Peter J. Edwards: Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance, Generalization and Learning Trajectory. 491-498
- Nicol N. Schraudolph, Terrence J. Sejnowski: Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain. 499-506
- Genevieve B. Orr, Todd K. Leen: Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times. 507-514
- Satoru Shiono, Satoshi Yamada, Michio Nakashima, Kenji Matsumoto: Information Theoretic Analysis of Connection Structure from Spike Trains. 515-522
- Holm Schwarze, John A. Hertz: Statistical Mechanics of Learning in a Large Committee Machine. 523-530
- John W. Miller, Rodney M. Goodman: Probability Estimator from a Database Using a Gibbs Energy Model. 531-538
- David Wolpert: On the Use of Evidence in Neural Networks. 539-546
Part 6: Network Dynamics and Chaos
- Bernard Doyon, Bruno Cessac, Mathias Quoy, Manuel Samuelides: Destabilization and Route to Chaos in Neural Networks with Random Connectivity. 549-555
Part 7: Theory and Analysis
- Mostefa Golea, Mario Marchand, Thomas R. Hancock: On Learning µ-Perceptron Networks with Binary Weights. 591-598
- Yong Liu: Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method. 599-606
- Noboru Murata, Shuji Yoshizawa, Shun-ichi Amari: Learning Curves, Model Selection and Complexity of Neural Networks. 607-614
- Bhaskar DasGupta, Georg Schnitger: The Power of Approximation: A Comparison of Activation Functions. 615-622
- Adam Kowalczyk: Some Estimates on the Number of Connections and Hidden Units for Feed-Forward Networks. 639-646
Part 8: Speech and Signal Processing
- Michael Cohen, Horacio Franco, Nelson Morgan, David E. Rumelhart, Victor Abrash: Context-Dependent Multiple Distribution Phonetic Modeling with MLPs. 649-657
- Makoto Hirayama, Eric Vatikiotis-Bateson, Kiyoshi Honda, Yasuharu Koike, Mitsuo Kawato: Physiologically Based Speech Synthesis. 658-665
- Weimin Liu, Andreas G. Andreou, Moise H. Goldstein Jr.: Analog Cochlear Model for Multiresolution Speech Analysis. 666-673
- Wei-Tsih Lee, John C. Pearson: A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems. 674-681
- Yochai Konig, Nelson Morgan, Chuck Wooters, Victor Abrash, Michael Cohen, Horacio Franco: Modeling Consistency in a Speaker Independent Continuous Speech Recognition System. 682-687
- José Carlos Príncipe, Abir Zahalka: Transient Signal Detection with Neural Networks: The Search for the Desired Signal. 688-695
- Joe Tebelskis, Alex Waibel: Performance Through Consistency: MS-TDNN's for Large Vocabulary Continuous Speech Recognition. 696-703
- George Zavaliagkos, Ying Zhao, Richard M. Schwartz, John Makhoul: A Hybrid Neural Net System for State-of-the-Art Continuous Speech Recognition. 704-711
- Hermann Hild, Alex Waibel: Connected Letter Recognition with a Multi-State Time Delay Neural Network. 712-719
Part 9: Applications
- Markus Schenkel, H. Weissman, Isabelle Guyon, C. Nohl, Donnie Henderson: Recognition-Based Segmentation of On-Line Hand-Printed Words. 723-730
- Esther Levin, Roberto Pieraccini: Planar Hidden Markov Modeling: From Speech to Optical Character Recognition. 731-738
- Pierre Baldi, Yves Chauvin, Tim Hunkapiller, Marcella A. McClure: Hidden Markov Models in Molecular Biology: New Algorithms and Applications. 747-754
- Charles R. Rosenberg, Jacob Erel, Henri Atlan: A Neural Network that Learns to Interpret Myocardial Planar Thallium Scintigrams. 755-762
Part 10: Implementations
- Stephen Churcher, Donald J. Baxter, Alister Hamilton, Alan F. Murray, H. Martin Reekie: Generic Analog Neural Computation - The Epsilon Chip. 773-780
- Rahul Sarpeshkar, Wyeth Bair, Christof Koch: Visual Motion Computation in Analog VLSI Using Pulses. 781-788
- David B. Kirch, Douglas Kerns, Kurt W. Fleischer, Alan H. Barr: Analog VLSI Implementation of Gradient Descent. 789-796
- Alexander Linden, Thomas Sudbrak, Christoph Tietz, F. Weber: An Object-Oriented Framework for the Simulation of Neural Networks. 797-804
- Eros Pasero, Riccardo Zecchina: Attractor Neural Networks with Local Inhibition: From Statistical Physics to a Digitial Programmable Integrated Circuit. 805-812
- Sylvie Renaud-Le Masson, Gwendal Le Masson, Eve Marder, L. F. Abbott: Hybrid Circuits of Interacting Computer Model and Biological Neurons. 813-819
- John Lazzaro, John Wawrzynek, Misha Mahowald, Massimo Sivilotti, Dave Gillespie: Silicon Auditory Processors as Computer Peripherals. 820-827
- Christof Koch, Binnal Mathur, Shih-Chii Liu, John G. Harris, Jin Luo, Massimo Sivilotti: Object-Based Analog VLSI Vision Circuits. 828-835
- Joshua Alspector, Ronny Meir, Ben P. Yuhas, Anthony Jayakumar, D. Lippe: A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks. 836-844
Part 11: Cognitive Science
- Paul Smolensky: Harmonic Grammars for Formal Languages. 847-854
- Dedre Gentner, Arthur B. Markman: Analogy - Watershed or Waterloo? Structural Alignment and the Development of Connectionist Models of Cognition. 855-862
- Michael Mozer, Sreerupa Das: A Connectionist Symbol Manipulator that Discovers the Structure of Context-Free Languages. 863-870
- Volker Tresp, Jürgen Hollatz, Subutai Ahmad: Network Structuring and Training Using Rule-Based Knowledge. 871-878
- Hinrich Schütze: Word Space. 895-902
- Rainer Goebel: Perceiving Complex Visual Scenes: An Oscillator Neural Network Model that Integrates Selective Attention, Perceptual Organization, and Invariant Recognition. 903-910
Part 12: Computational and Theoretical Neurobiology
- Kenji Doya, Mary E. T. Boyle, Allen I. Selverston: Maaping Between Neural and Physical Activities of the Lobster Gastric Mill. 913-920
- Mark E. Nelson: A Neural Model of Descending Gain Control in the Electrosensory System. 921-928
- Neil Burgess, John O'Keefe, Michael Recce: Using Hippocampal `Plane Cells' for Navigation, Exploiting Phase Coding. 929-936
- Mark A. Gluck, Catherine Myers: Adaptive Stimulus Representations: A Computational Theory of Hippocampal-Region Functions. 937-944
- Itay Gat, Naftali Tishby: Statistical Modeling of Cell Assemblies Activities in Associative Cortex of Behaving Monkeys. 945-952
- Ralph Linsker: Deriving Receptive Fields Using an Optimal Encoding Criterion. 953-960
- Olivier J. M. D. Coenen, Terrence J. Sejnowski, Stephen G. Lisberger: Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex. 961-968
- P. Read Montague, Peter Dayan, Steven J. Nowlan, Terrence J. Sejnowski: Using Aperiodic Reinforcement for Directed Self-Organization During Development. 969-976
- Klaus Pawelzik, Hans-Ulrich Bauer, J. Deppisch, Theo Geisel: How Oscillatory Neuronal Responses Reflect Bistability and Switching of the Hidden Assembly Dynamics. 977-984
- Geoffrey J. Goodhill: Topography and Ocular Dominance with Positive Correlations. 985-992
- Frank Moss, André Longtin: Statistical and Dynamical Interpretation of ISIH Data from Periodically Stimulated Sensory Neurons. 993-1000
- John G. Milton, Po Hsiang Chu, Jack D. Cowan: Spiral Waves in Integrate-and-Fire Neural Networks. 1001-1006
- Lance C. Walton, David L. Bisset: Parametrizing Feature Sensitive Cell Formation in Linsker Networks in the Auditory System. 1007-1013
- Lina Massone: A Recurrent Neural Network for Generation of Occular Saccades. 1014-1021
- Christiane Linster, David Marsan, Claudine Masson, Michel Kerszberg, Gérard Dreyfus, Léon Personnaz: A Formal Model of the Insect Olfactory Macroglomerulus: Simulations and Analytic Results. 1022-1029
- William E. Skaggs, Bruce L. McNaughton, Katalin M. Gothard: An Information-Theoretic Approach to Deciphering the Hippocampal Code. 1030-1037
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