NIPS 1992: Denver, CO, USA

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
  
• Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee:
  Time Warping Invariant Neural Networks. 180-187
  
• Enno Littmann, Helge Ritter:
  Generalization Abilities of Cascade Network Architecture. 188-195
  
• 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
  
• Virginia R. de Sa, Dana H. Ballard:
  A Note on Learning Vector Quantization. 220-227
  
• 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
  
• Peter Dayan, Geoffrey E. Hinton:
  Feudal Reinforcement Learning. 271-278
  
• 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
  
• Subutai Ahmad, Volker Tresp:
  Some Solutions to the Missing Feature Problem in Vision. 393-400
  
• 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
  
• Lei Xu, Alan L. Yuille:
  Self-Organizing Rules for Robust Principal Component Analysis. 467-474
  
• 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
  
• Ali A. Minai, William B. Levy:
  Predicting Complex Behavior in Sparse Asymmetric Networks. 556-563
  
• Isaac Meilijson, Eytan Ruppin, Moshe Sipper:
  Single-Iteration Threshold Hamming Networks. 564-571
  
• Isaac Meilijson, Eytan Ruppin:
  History-Dependent Attractor Neural Networks. 572-579
  
• David DeMers, Garrison W. Cottrell:
  Non-Linear Dimensionality Reduction. 580-587
  

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
  
• Uwe Helmke, Robert C. Williamson:
  Rational Parametrizations of Neural Networks. 623-630
  
• N. H. Wulff, John A. Hertz:
  Learning Cellular Automation Dynamics with Neural Networks. 631-638
  
• 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
  
• Jen-Lun Yuan, Terrence Fine:
  Forecasting Demand for Electric Power. 739-746
  
• 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

• Janeen Anderson, John C. Platt, David Kirk:
  An Analog VLSI Chip for Radial Basis Functions. 765-772
  
• 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
  
• Daphne Bavelier, Michael I. Jordan:
  A Dynamical Model of Priming and Repetition Blindness. 879-886
  
• Geoffrey G. Towell, Richard Lehrer:
  A Knowledge-Based Model of Geometry Learning. 887-894
  
• 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
  

Last update Fri May 25 08:29:34 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page