NIPS 1993:
Denver, CO, USA
Jack D. Cowan, Gerald Tesauro, Joshua Alspector (Eds.):
Advances in Neural Information Processing Systems 6, [7th NIPS Conference, Denver, Colorado, USA, 1993].
Morgan Kaufmann 1994, ISBN 1-55860-322-0
Learning Algorithms
- Geoffrey E. Hinton, Richard S. Zemel:
Autoencoders, Minimum Description Length and Helmholtz Free Energy.
3-10

- Richard S. Zemel, Geoffrey E. Hinton:
Developing Population Codes by Minimizing Description Length.
11-18

- Sreerupa Das, Michael Mozer:
A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction.
19-26

- Eric Saund:
Unsupervised Learning of Mixtures of Multiple Causes in Binary Data.
27-34

- Asriel U. Levin, Todd K. Leen, John E. Moody:
Fast Pruning Using Principal Components.
35-42

- Christoph Bregler, Stephen M. Omohundro:
Surface Learning with Applications to Lipreading.
43-50

- Melanie Mitchell, John H. Holland, Stephanie Forrest:
When will a Genetic Algorithm Outperform Hill Climbing.
51-58

- Oded Maron, Andrew W. Moore:
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation.
59-66

- Bill Baird, Todd Troyer, Frank H. Eeckman:
Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network.
67-74

- Yoshua Bengio, Paolo Frasconi:
Credit Assignment through Time: Alternatives to Backpropagation.
75-82

- Javier R. Movellan:
A Local Algorithm to Learn Trajectories with Stochastic Neural Networks.
83-87

- Gregory M. Saunders, Peter J. Angeline, Jordan B. Pollack:
Structural and Behavioral Evolution of Recurrent Networks.
88-95

- Steven Gold, Eric Mjolsness, Anand Rangarajan:
Clustering with a Domain-Specific Distance Measure.
96-103

- Joachim M. Buhmann, Thomas Hofmann:
Central and Pairwise Data Clustering by Competitive Neural Networks.
104-111

- Virginia R. de Sa:
Learning Classification with Unlabeled Data.
112-119

- Zoubin Ghahramani, Michael I. Jordan:
Supervised learning from incomplete data via an EM approach.
120-127

- Volker Tresp, Subutai Ahmad, Ralph Neuneier:
Training Neural Networks with Deficient Data.
128-135

- Mats Österberg, Reiner Lenz:
Unsupervised Parallel Feature Extraction from First Principles.
136-143

- Terence D. Sanger:
Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples.
144-151

- Nanda Kambhatla, Todd K. Leen:
Fast Non-Linear Dimension Reduction.
152-159

- Stefan Schaal, Christopher G. Atkeson:
Assessing the Quality of Learned Local Models.
160-167

- Patrice Simard:
Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering.
168-175

- Dana Ron, Yoram Singer, Naftali Tishby:
The Power of Amnesia.
176-183

- Dietrich Wettschereck, Thomas G. Dietterich:
Locally Adaptive Nearest Neighbor Algorithms.
184-191

- Yong Liu:
Robust Parameter Estimation and Model Selection for Neural Network Regression.
192-199

- David Wolpert:
Bayesian Backpropagation Over I-O Functions Rather Than Weights.
200-207

- Hans Henrik Thodberg:
Bayesian Backprop in Action: Pruning, Committees, Error Bars and an Application to Spectroscopy.
208-215

- Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez:
A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction.
216-223

- Iris Ginzburg, David Horn:
Combined Neural Networks for Time Series Analysis.
224-231

- Patrice Simard, Hans Peter Graf:
Backpropagation without Multiplication.
232-239

- Richard T. J. Bostock, Alan J. Harget:
A Comparative Study of a Modified Bumptree Neural Network with Radial Basis Function Networks and the Standard Multi Layer Perceptron.
240-246

- Hossein Lari-Najafi, Vladimir Cherkassky:
Adaptive knot Placement for Nonparametric Regression.
247-254

- Bernd Fritzke:
Supervised Learning with Growing Cell Structures.
255-262

- Babak Hassibi, David G. Stork, Gregory J. Wolff:
Optimal Brain Surgeon: Extensions and performance comparison.
263-270

- Ryotaro Kamimura:
Generation of Internal Representation by alpha.
271-278

- Laurens R. Leerink, Marwan A. Jabri:
Constructive Learning Using Internal Representation Conflicts.
279-284

- Joachim Utans:
Learning in Compositional Hierarchies: Inducing the Structure of Objects from Data.
285-292

Learning Theory, Generalization, and Complexity
- Sumio Watanabe:
An Optimization Method of Layered Neural Networks based on the Modified Information Criterion.
293-302

- Changfeng Wang, Santosh S. Venkatesh, J. Stephen Judd:
Optimal Stopping and Effective Machine Complexity in Learning.
303-310

- Wolfgang Maass:
Agnostic PAC-Learning of Functions on Analog Neural Nets.
311-318

- Hrushikesh Narhar Mhaskar, Charles A. Micchelli:
How to Choose an Activation Function.
319-326

- Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker:
Learning Curves: Asymptotic Values and Rate of Convergence.
327-334

- Charles Fefferman, Scott Markel:
Recovering a Feed-Forward Net From Its Output.
335-342

- Tal Grossman, Alan S. Lapedes:
Use of Bad Training Data for Better Predictions.
343-350

- Babak Hassibi, Ali H. Sayed, Thomas Kailath:
Optimality Criteria for LMS and Backpropagation.
351-358

- Bill G. Horne, Don R. Hush:
Bounds on the Complexity of Recurrent Neural Network Implementations of Finite State Machines.
359-366

- Chuanyi Ji:
Generalization Error and the Expected Network Complexity.
367-374

- Adam Kowalczyk:
Counting Function Theorem for Multi-Layer Networks.
375-382

- Olvi L. Mangasarian, Mikhail V. Solodov:
Backpropagation Convergence via Deterministic Nonmonotone Perturbed Minimization.
383-390

- Mark Plutowski, Shinichi Sakata, Halbert White:
Cross-Validation Estimates ISME.
391-398

- Holm Schwarze, John A. Hertz:
Discontinuous Generalization in Large Committee Machines.
399-406

- Jonathan L. Shapiro, Adam Prügel-Bennett:
Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks.
407-414

- Grace Wahba, Yuedong Wang, Chong Gu, Ronald Klein, Barbara Klein:
Structured Machine Learning for Soft Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing, and Evaluation.
415-422

- Sumio Watanabe:
Solvable Models of Artificial Neural Networks.
423-430

Theoretical Analysis: Dynamics and Statistics
- Herbert Wiklicky:
On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks.
431-436

- Scott Kirkpatrick, Géza Györgyi, Naftali Tishby, Lidror Troyansky:
The Statistical Mechanics of k-Satisfaction.
439-446

- Anthony C. C. Coolen, R. W. Penney, D. Sherrington:
Coupled Dynamics of Fast Neurons and Slow Interactions.
447-454

- Max H. Garzon, Fernanda Botelho:
Observability of Neural Network Behavior.
455-462

- Wulfram Gerstner, J. Leo van Hemmen:
How to Describe Neuronal Activity: Spikes, Rates, or Assemblies?
463-470

- Iris Ginzburg, Haim Sompolinsky:
Correlation Functions in a Large Stochastic Network.
471-476

- Todd K. Leen, Genevieve B. Orr:
Optimal Stochastic Search and Adaptive Momentum.
477-484

- Isaac Meilijson, Eytan Ruppin:
Optimal Signalling in Attractor Neural Networks.
485-492

- Xin Wang, Qingnan Li, Edward K. Blum:
Asynchronous Dynamics of Continuous Time Neural Networks.
493-500

Neuroscience
- John F. Kolen:
Fool's Gold: Extracting Finite State Machines from Recurrent Network Dynamics.
501-508

- Eve Marder:
Dynamic Modulation of Neurons and Networks.
511-518

- Öjvind Bernander, Christof Koch, Rodney J. Douglas:
Amplifying and Linearizing Apical Synaptic Inputs to Cortical Pyramidal Cells.
519-526

- Christiane Linster, David Marsan, Claudine Masson, Michel Kerszberg:
Odor Processing in the Bee: A Preliminary Study of the Role of Central Input to the Antennal Lobe.
527-534

- Mitchell Gil Maltenfort, Robert E. Druzinsky, C. J. Heckman, W. Zev Rymer:
Lower Boundaries of Motoneuron Desynchronization via Renshaw Interneurons.
535-542

- Klaus Obermayer, Lynne Kiorpes, Gary G. Blasdel:
Development of Orientation and Ocular Dominance Columns in Infant Macaques.
543-550

- Daniel L. Ruderman, William Bialek:
Statistics of Natural Images: Scaling in the Woods.
551-558

- Eric Boussard, Jean-François Vibert:
Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina.
559-565

- Kenji Doya, Allen I. Selverston, Peter F. Rowat:
A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillations.
566-573

- Audrey L. Guzik, Robert C. Eaton:
Directional Hearing by the Mauthner System.
574-581

- Timothy K. Horiuchi, Brooks Bishofberger, Christof Koch:
An Analog VLSI Saccadic Eye Movement System.
582-589

- Michael S. Lewicki:
Bayesian Modeling and Classification of Neural Signals.
590-597

- P. Read Montague, Peter Dayan, Terrence J. Sejnowski:
Foraging in an Uncertain Environment Using Predictive Hebbian Learning.
598-605

- Daniel J. Rosen, David E. Rumelhart, Eric I. Knudsen:
A Connectionist Model of the Owl's Sound Localization System.
606-613

- Terence D. Sanger:
Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements.
614-621

- Micah S. Siegel:
An Analog VLSI Model of Central Pattern Generation in the Leech.
622-628

Control, Navigation, and Planning
- Martin Stemmler, Marius Usher, Christof Koch, Zeev Olami:
Synchronization, Oscillations and 1/f Noise in Networks of Spiking Neurons.
629-636

- Kenneth M. Buckland, Peter D. Lawrence:
Transition Point Dynamic Programming.
639-646

- Gary William Flake, Guo-Zheng Sun, Yee-Chun Lee, Hsing-Hen Chen:
Exploiting Chaos to Control the Future.
647-654

- Satinder P. Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly:
Robust Reinforcement Learning in Motion Planning.
655-662

- Christopher G. Atkeson:
Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming.
663-670

- Justin A. Boyan, Michael L. Littman:
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach.
671-678

- David A. Cohn:
Neural Network Exploration Using Optimal Experiment Design.
679-686

- Andrew G. Barto, Michael O. Duff:
Monte Carlo Matrix Inversion and Reinforcement Learning.
687-694

- Vijaykumar Gullapalli, Andrew G. Barto:
Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms.
695-702

- Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh:
Convergence of Stochastic Iterative Dynamic Programming Algorithms.
703-710

- Andrew W. Moore:
The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces.
711-718

- Timothy W. Cacciatore, Steven J. Nowlan:
Mixtures of Controllers for Jump Linear and Non-Linear Plants.
719-726

Applications
- Yasuhiro Wada, Yasuharu Koike, Eric Vatikiotis-Bateson, Mitsuo Kawato:
A Computational Model for Cursive Handwriting Based on the Minimization Principle.
727-734

- Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, Roopak Shah:
Signature Verification Using a Siamese Time Delay Neural Network.
737-744

- Ralph Wolf, John C. Platt:
Postal Address Block Location Using a Convolutional Locator Network.
745-752

- Shumeet Baluja, Dean Pomerleau:
Non-Intrusive Gaze Tracking Using Artificial Neural Networks.
753-760

- Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh:
Hidden Markov Models for Human Genes.
761-768

- Joachim M. Buhmann, Martin Lades, Frank H. Eeckman:
Illumination-Invariant Face Recognition with a Contrast Sensitive Silicon Retina.
769-776

- Nicholas S. Flann:
Recognition-Based Segmentation of On-Line Cursive Handwriting.
777-784

- Hans Peter Graf, Eric Cosatto:
Address Block Location with a Neural Net System.
785-792

- Nachimuthu Karunanithi:
Identifying Fault-Prone Software Modules Using Feed-Forward Networks: A Case Study.
793-800

- Didier Keymeulen, Martine de Gerlache:
Comparison Training for a Rescheduling Problem in Neural Networks.
801-808

- Alan S. Lapedes, Evan W. Steeg, Robert M. Farber:
Neural Network Definition of Highly Predictable Protein Secondary Structure Classes.
809-816

- Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski:
Temporal Difference Learning of Position Evaluation in the Game of Go.
817-824

- Padhraic Smyth:
Probabilistic Anomaly Detection in Dynamic Systems.
825-832

Implementations
- Yoram Singer, Naftali Tishby:
Decoding Cursive Scripts.
833-840

- Michael A. Glover, W. Thomas Miller III:
A Massively-Parallel {SIMD} Processor for Neural Network and Machine Vision Applications.
843-849

- Steven S. Watkins, Paul M. Chau, Raoul Tawel, Bjorn Lambrigtsen, Mark Plutowski:
A Hybrid Radial Basis Function Neurocomputer and Its Applications.
850-857

- Gert Cauwenberghs:
A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics.
858-865

- Andreas G. Andreou, Thomas G. Edwards:
VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking Systems.
866-873

- Richard Coggins, Marwan A. Jabri:
WATTLE: A Trainable Gain Analogue VLSI Neural Network.
874-881

- Ibrahim M. Elfadel, John L. Wyatt Jr.:
The Softmax Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit Element.
882-887

- Urs A. Müller, Michael Kocheisen, Anton Gunzinger:
High Performance Neural Net Simulation on a Multiprocessor System with Intelligent Communication.
888-895

- Michael Murray, Ming-Tak Leung, Kan Boonyanit, Kong Kritayakirana, James B. Burg, Gregory J. Wolff, Tokahiro Watanabe, Edward L. Schwartz, David G. Stork, Allern M. Peterson:
Digital Boltzmann VLSI for Constraint Satisfaction and Learning.
896-903

- Ernst Niebur, Dean Brettle:
Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube Architecture.
904-910

- Arlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli:
Learning Complex Boolean Functions: Algorithms and Applications.
911-918

- Tadashi Shibata, Koji Kotani, Takeo Yamashita, Hiroshi Ishii, Hideo Kosaka, Tadahiro Ohmi:
Implementing Intelligence on Silicon Using Neuron-Like Functional MOS Transistors.
919-926

Visual Processing
- Lloyd Watts:
Event-Driven Simulation of Networks of Spiking Neurons.
927-934

- Yoshua Bengio, Yann LeCun, Donnie Henderson:
Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models.
937-944

- Trevor Darrell, Alex Pentland:
Classifying Hand Gestures with a View-Based Distributed Representation.
945-952

- Kô Sakai, Leif H. Finkel:
A Network Mechanism for the Determination of Shape-from-Texture.
953-960

- Subutai Ahmad:
Feature Densities Are Required for Computing Feature Correspondences.
961-968

- G. T. Buracas, T. D. Albright:
The Role of MT Neuron Receptive Field Surrounds in Computing Object Shape from Velocity Fields.
969-976

- Kostas I. Diamantaras, Davi Geiger:
Resolving Motion Ambiguities.
977-984

- Chien-Ping Lu, Eric Mjolsness:
Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching.
985-992

- Paul Sajda, Leif H. Finkel:
Dual Mechanisms for Neural Binding and Segmentation.
993-1000

Speech and Signal Processing
- Alan L. Yuille, Stelios M. Smirnakis, Lei Xu:
Bayesian Self-Organization.
1001-1008

- José Carlos Príncipe, Hui-Huang Hsu, Jyh-Ming Kuo:
Analysis of Short Term Memories for Neural Networks.
1011-1018

- Eric I. Chang, Richard Lippmann:
Figure of Merit Training for Detection and Spotting.
1019-1026

- Gregory J. Wolff, K. Venkatesh Prasad, David G. Stork, Marcus E. Hennecke:
Lipreading by Neural Networks: Visual Preprocessing, Learning, and Sensory Integration.
1027-1034

- Kevin R. Farrell, Richard J. Mammone:
Speaker Recognition Using Neural Tree Networks.
1035-1042

- Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato:
Inverse Dynamics of Speech Motor Control.
1043-1050

- Steve Renals, Mike Hochberg, Anthony J. Robinson:
Learning Temporal Dependencies in Connectionist Speech Recognition.
1051-1058

Cognitive Science
Addenda to NIPS 5
Workshops
- Ah Chung Tsoi, D. S. C. So, Alex A. Sergejew:
Classification of Electroencephalogram Using Artificial Neural Networks.
1151-1158

- Vwani P. Roychowdhury, Kai-Yeung Siu:
Complexity Issues in Neural Computation and Learning.
1161-1162

- Andreas S. Weigend:
Connectionism for Music and Audition.
1163-1164

- Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore:
Memory-Based Methods for Regression and Classification.
1165-1166

- Ernst Niebur, Bruno A. Olshausen:
Neurobiology, Psychophysics, and Computational Models of Visual Attention.
1167-1168

- David A. Cohn:
Robot Learning: Exploration and Continuous Domains.
1169-1170

- Max H. Garzon, Fernanda Botelho:
Stability and Observability.
1171-1172

- Mark A. Gluck:
What Does the Hippocampus Compute?: A Precis of the 1993 NIPS Workshop.
1173-1175

- Robert M. French:
Catastrophic Interference in Connectionist Networks: Can It Be Predicted, Can It Be Prevented?
1176-1177

- Joachim Diederich, Ah Chung Tsoi:
Connectionist Modeling and Parallel Architectures.
1178-1179

- Thomas H. Hildebrandt:
Functional Models of Selective Attention and Context Dependency.
1180-1181

- Hayit Greenspan:
Learning in Computer Vision and Image Understanding.
1182-1183

- Arun K. Jagota:
Neural Network Models for Optimization Problems.
1184-1185

- Josef P. Rauschecker, Terrence J. Sejnowski:
Processing of Visual and Auditory Space and Its Modification by Experience.
1186-1187

- Michael P. Perrone:
Putting It All Together: Methods for Combining Neural Networks.
1188-1189

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