NIPS 1996:
Denver, CO, USA
Michael Mozer, Michael I. Jordan, Thomas Petsche (Eds.):
Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996.
MIT Press 1997 
Cognitive Science
Neuroscience
- Hagai Attias, Christoph E. Schreiner:
Temporal Low-Order Statistics of Natural Sounds.
27-33
- Wyeth Bair, James R. Cavanaugh, J. Anthony Movshon:
Reconstructing Stimulus Velocity from Neuronal Responses in Area MT.
34-40
- Emanuela Bricolo, Tomaso Poggio, Nikos K. Logothetis:
3D Object Recognition: A Model of View-Tuned Neurons.
41-47
- Peter Dayan:
A Hierarchical Model of Visual Rivalry.
48-54
- Thomas C. Ferrée, Ben A. Marcotte, Shawn R. Lockery:
Neural Network Models of Chemotaxis in the Nematode Caenorhabditis Elegans.
55-61
- Fabrizio Gabbiani, Walter Metzner, Ralf Wessel, Christof Koch:
Extraction of Temporal Features in the Electrosensory System of Weakly Electric Fish.
62-68
- Zhaoping Li:
A Neural Model of Visual Contour Integration.
69-75
- Laura Martignon, Kathryn B. Laskey, Gustavo Deco, Eilon Vaadia:
Learning Exact Patterns of Quasi-synchronization among Spiking Neurons from Data on Multi-unit Recordings.
76-82
- Bartlett W. Mel, Daniel L. Ruderman, Kevin A. Archie:
Complex-Cell Responses Derived from Center-Surround Inputs: The Surprising Power of Intradendritic Computation.
83-89
- Klaus Pawelzik, Udo Ernst, Fred Wolf, Theo Geisel:
Orientation Contrast Sensitivity from Long-range Interactions in Visual Cortex.
90-96
- Alexandre Pouget, Kechen Zhang:
Statistically Efficient Estimations Using Cortical Lateral Connections.
97-103
- Silvio P. Sabatini, Fabio Solari, Giacomo M. Bisio:
An Architectural Mechanism for Direction-tuned Cortical Simple Cells: The Role of Mutual Inhibition.
104-110
- Akaysha C. Tang, Andreas M. Bartels, Terrence J. Sejnowski:
Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input.
111-117
- Emanuel Todorov, Athanassios Siapas, David Somers:
A Model of Recurrent Interactions in Primary Visual Cortex.
118-126
Theory
- Shun-ichi Amari:
Neural Learning in Structured Parameter Spaces - Natural Riemannian Gradient.
127-133
- Peter L. Bartlett:
For Valid Generalization the Size of the Weights is More Important than the Size of the Network.
134-140
- Siegfried Bös, Manfred Opper:
Dynamics of Training.
141-147
- Graham Brightwell, Claire Kenyon, Hélène Paugam-Moisy:
Multilayer Neural Networks: One or Two Hidden Layers?
148-154
- Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik:
Support Vector Regression Machines.
155-161
- André Elisseeff, Hélène Paugam-Moisy:
Size of Multilayer Networks for Exact Learning: Analytic Approach.
162-168
- Søren Halkjær, Ole Winther:
The Effect of Correlated Input Data on the Dynamics of Learning.
169-175
- Tom Heskes:
Practical Confidence and Prediction Intervals.
176-182
- Kukjin Kang, Jong-Hoon Oh:
Statistical Mechanics of the Mixture of Experts.
183-189
- Adam Kowalczyk, Herman L. Ferrá:
MLP Can Provably Generalize Much Better than VC-bounds Indicate.
190-196
- Adam Krzyzak, Tamás Linder:
Radial Basis Function Networks and Complexity Regularization in Function Learning.
197-203
- Nick Littlestone, Chris Mesterharm:
An Apobayesian Relative of Winnow.
204-210
- Wolfgang Maass:
Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons.
211-217
- Wolfgang Maass, Pekka Orponen:
On the Effect of Analog Noise in Discrete-Time Analog Computations.
218-224
- Manfred Opper, Ole Winther:
A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks.
225-231
- Genevieve B. Orr:
Removing Noise in On-Line Search using Adaptive Batch Sizes.
232-238
- Ian Parberry, Hung-Li Tseng:
Are Hopfield Networks Faster than Conventional Computers?
239-245
- Ferdinand Peper, Hideki Noda:
Hebb Learning of Features based on their Information Content.
246-252
- Richard Rohwer, Michal Morciniec:
The Generalisation Cost of RAMnets.
253-259
- David Saad, Sara A. Solla:
Learning with Noise and Regularizers in Multilayer Neural Networks.
260-266
- Lawrence K. Saul, Michael I. Jordan:
A Variational Principle for Model-based Morphing.
267-273
- Peter Sollich, David Barber:
Online Learning from Finite Training Sets: An Analytical Case Study.
274-280
- Vladimir Vapnik, Steven E. Golowich, Alex J. Smola:
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing.
281-287
- Ansgar H. L. West, David Saad, Ian T. Nabney:
The Learning Dynamcis of a Universal Approximator.
288-294
- Christopher K. I. Williams:
Computing with Infinite Networks.
295-301
- K. Y. Michael Wong:
Microscopic Equations in Rough Energy Landscape for Neural Networks.
302-308
- Assaf J. Zeevi, Ron Meir, Robert J. Adler:
Time Series Prediction using Mixtures of Experts.
309-318
Algorithms and Architecture
- Shumeet Baluja:
Genetic Algorithms and Explicit Search Statistics.
319-325
- Yoram Baram:
Consistent Classification, Firm and Soft.
326-332
- David Barber, Christopher M. Bishop:
Bayesian Model Comparison by Monte Carlo Chaining.
333-339
- David Barber, Christopher K. I. Williams:
Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo.
340-346
- Christopher M. Bishop, Cazhaow S. Quazaz:
Regression with Input-Dependent Noise: A Bayesian Treatment.
347-353
- Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map.
354-360
- Andrew Blake, Michael Isard:
The CONDENSATION Algorithm - Conditional Density Propagation and Applications to Visual Tracking.
361-367
- Paul S. Bradley, Olvi L. Mangasarian, W. Nick Street:
Clustering via Concave Minimization.
368-374
- Christopher J. C. Burges, Bernhard Schölkopf:
Improving the Accuracy and Speed of Support Vector Machines.
375-381
- A. Neil Burgess:
Estimating Equivalent Kernels for Neural Networks: A Data Perturbation Approach.
382-388
- Rich Caruana, Virginia R. de Sa:
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs.
389-395
- Chanchal Chatterjee, Vwani P. Roychowdhury:
Self-Organizing and Adaptive Algorithms for Generalized Eigen-Decomposition.
396-402
- Daniel S. Clouse, C. Lee Giles, Bill G. Horne, Garrison W. Cottrell:
Representation and Induction of Finite State Machines using Time-Delay Neural Networks.
403-409
- Frans Coetzee, Virginia L. Stonick:
488 Solutions to the XOR Problem.
410-416
- David A. Cohn:
Minimizing Statistical Bias with Queries.
417-423
- Jeremy S. De Bonet, Charles Lee Isbell Jr., Paul A. Viola:
MIMIC: Finding Optima by Estimating Probability Densities.
424-430
- A. P. Dunmur, D. M. Titterington:
On a Modification to the Mean Field EM Algorithm in Factorial Learning.
431-437
- Andrew M. Finch, Richard C. Wilson, Edwin R. Hancock:
Softening Discrete Relaxation.
438-444
- Arthur Flexer:
Limitations of Self-organizing Maps for Vector Quantization and Multidimensional Scaling.
445-451
- Brendan J. Frey:
Continuous Sigmoidal Belief Networks Trained using Slice Sampling.
452-458
- Jürgen Fritsch, Michael Finke, Alex Waibel:
Adaptively Growing Hierarchical Mixtures of Experts.
459-465
- Tom Heskes:
Balancing Between Bagging and Bumping.
466-472
- Sepp Hochreiter, Jürgen Schmidhuber:
LSTM can Solve Hard Long Time Lag Problems.
473-479
- Aapo Hyvärinen, Erkki Oja:
One-unit Learning Rules for Independent Component Analysis.
480-486
- Tommi Jaakkola, Michael I. Jordan:
Recursive Algorithms for Approximating Probabilities in Graphical Models.
487-493
- Chuanyi Ji, Sheng Ma:
Combinations of Weak Classifiers.
494-500
- Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul:
Hidden Markov Decision Trees.
501-507
- Ryotaro Kamimura:
Unification of Information Maximization and Minimization.
508-514
- Daniel D. Lee, H. Sebastian Seung:
Unsupervised Learning by Convex and Conic Coding.
515-521
- Friedrich Leisch, Kurt Hornik:
ARC-LH: A New Adaptive Resampling Algorithm for Improving ANN Classifiers.
522-528
- Michael S. Lewicki, Terrence J. Sejnowski:
Bayesian Unsupervised Learning of Higher Order Structure.
529-535
- Juan K. Lin, Jack D. Cowan, David G. Grier:
Source Separation and Density Estimation by Faithful Equivariant SOM.
536-542
- David Lowe, Michael E. Tipping:
NeuroScale: Novel Topographic Feature Extraction using RBF Networks.
543-549
- Mark Mathieson:
Ordered Classes and Incomplete Examples in Classification.
550-556
- Marina Meila, Michael I. Jordan:
Triangulation by Continuous Embedding.
557-563
- Christopher J. Merz, Michael J. Pazzani:
Combining Neural Network Regression Estimates with Regularized Linear Weights.
564-570
- David J. Miller, Hasan S. Uyar:
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data.
571-577
- Stefano Monti, Gregory F. Cooper:
Learning Bayesian Belief Networks with Neural Network Estimators.
578-584
- John E. Moody, Thorsteinn S. Rögnvaldsson:
Smoothing Regularizers for Projective Basis Function Networks.
585-591
- Paul W. Munro, Bambang Parmanto:
Competition Among Networks Improves Committee Performance.
592-598
- Noboru Murata, Klaus-Robert Müller, Andreas Ziehe, Shun-ichi Amari:
Adaptive On-line Learning in Changing Environments.
599-605
- Genevieve B. Orr, Todd K. Leen:
Using Curvature Information for Fast Stochastic Search.
606-612
- Barak A. Pearlmutter, Lucas C. Parra:
Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA.
613-619
- Anand Rangarajan, Alan L. Yuille, Steven Gold, Eric Mjolsness:
A Convergence Proof for the Softassign Quadratic Assignment Algorithm.
620-626
- Kazumi Saito, Ryohei Nakano:
Second-order Learning Algorithm with Squared Penalty Term.
627-633
- Joseph Sill, Yaser S. Abu-Mostafa:
Monotonicity Hints.
634-640
- Yoram Singer, Manfred K. Warmuth:
Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions.
641-647
- Padhraic Smyth:
Clustering Sequences with Hidden Markov Models.
648-654
- Achim Stahlberger, Martin Riedmiller:
Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm.
655-661
- Joshua B. Tenenbaum, William T. Freeman:
Separating Style and Content.
662-668
- Volker Tresp, Ralph Neuneier, Hans-Georg Zimmermann:
Early Brain Damage.
669-675
- Richard S. Zemel, Peter Dayan, Alexandre Pouget:
Probabilistic Interpretation of Population Codes.
676-684
Implementation
- Ralph Etienne-Cummings, Jan Van der Spiegel, Naomi Takahashi, Alyssa B. Apsel, Paul Mueller:
VLSI Implementation of Cortical Visual Motion Detection Using an Analog Neural Computer.
685-691
- Philipp Häfliger, Misha Mahowald, Lloyd Watts:
A Spike Based Learning Neuron in Analog VLSI.
692-698
- John G. Harris, Yu-Ming Chiang:
An Analog Implementation of the Constant Average Statistics Constraint For Sensor Calibration.
699-705
- Timothy K. Horiuchi, Tonia G. Morris, Christof Koch, Stephen P. DeWeerth:
Analog VLSI Circuits for Attention-Based, Visual Tracking.
706-712
- Kunihiko Iizuka, Masayuki Miyamoto, Hirofumi Matsui:
Dynamically Adaptable CMOS Winner-Take-All Neural Network.
713-719
- W. Fritz Kruger, Paul E. Hasler, Bradley A. Minch, Christof Koch:
An Adaptive WTA using Floating Gate Technology.
720-726
- John Lazzaro, John Wawrzynek, Richard Lippmann:
A Micropower Analog VLSI HMM State Decoder for Wordspotting.
727-733
- Fernando J. Pineda, Gert Cauwenberghs, R. Timothy Edwards:
Bangs, Clicks, Snaps, Thuds and Whacks: An Architecture for Acoustic Transient Processing.
734-740
- André van Schaik, Eric Fragnière, Eric A. Vittoz:
A Silicon Model of Amplitude Modulation Detection in the Auditory Brainstem.
741-750
Speech, Handwriting and Signal Processing
- Michael S. Gray, Javier R. Movellan, Terrence J. Sejnowski:
Dynamic Features for Visual Speechreading: A Systematic Comparison.
751-757
- Te-Won Lee, Anthony J. Bell, Russell H. Lambert:
Blind Separation of Delayed and Convolved Sources.
758-764
- John C. Platt, Nada Matic:
A Constructive RBF Network for Writer Adaptation.
765-771
- Gerhard Rigoll, Christoph Neukirchen:
A New Approach to Hybrid HMM/ANN Speech Recognition using Mutual Information Neural Networks.
772-778
- Alex Röbel:
Neural Network Modeling of Speech and Music Signals.
779-785
- Diego Sona, Alessandro Sperduti, Antonina Starita:
A Constructive Learning Algorithm for Discriminant Tangent Models.
786-792
- Eric A. Wan, Alex T. Nelson:
Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation.
793-799
- Steve R. Waterhouse, Gary Cook:
Ensemble Methods for Phoneme Classification.
800-806
- Larry S. Yaeger, Richard F. Lyon, Brandyn J. Webb:
Effective Training of a Neural Network Character Classifier for Word Recognition.
807-816
Visual Processing
- Marian Stewart Bartlett, Terrence J. Sejnowski:
Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks.
817-823
- Suzanna Becker:
Learning Temporally Persistent Hierarchical Representations.
824-830
- Anthony J. Bell, Terrence J. Sejnowski:
Edges are the Independent Components of Natural Scenes.
831-837
- Elie Bienenstock, Stuart Geman, Daniel Potter:
Compositionality, MDL Priors, and Object Recognition.
838-844
- Christoph Bregler, Jitendra Malik:
Learning Appearance Based Models: Mixtures of Second Moment Experts.
845-
- Dawei W. Dong:
Spatiotemporal Coupling and Scaling of Natural Images and Human Visual Sensitivities.
859-865
- Michael S. Gray, Alexandre Pouget, Richard S. Zemel, Steven J. Nowlan, Terrence J. Sejnowski:
Selective Integration: A Model for Disparity Estimation.
866-872
- Stephen Grossberg, James R. Williamson:
ARTEX: A Self-organizing Architecture for Classifying Image Regions.
873-879
- José A. F. Leite, Edwin R. Hancock:
Contour Organisation with the EM Algorithm.
880-886
- Trevor Mundel, Alexander Dimitrov, Jack D. Cowan:
Visual Cortex Circuitry and Orientation Tuning.
887-893
- Curtis Padgett, Garrison W. Cottrell:
Representing Face Images for Emotion Classification.
894-900
- Simon J. Thorpe, Jacques Gautrais:
Rapid Visual Processing using Spike Asynchrony.
901-907
- Yair Weiss:
Interpreting Images by Propagating Bayesian Beliefs.
908-914
- Shih-Cheng Yen, Leif H. Finkel:
Salient Contour Extraction by Temporal Binding in a Cortically-based Network.
915-924
Applications
- Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar-Pikielny:
An Orientation Selective Neural Network for Pattern Identification in Particle Detectors.
925-931
- Timothy X. Brown:
Adaptive Access Control Applied to Ethernet Data.
932-938
- David A. Cohn, Satinder P. Singh:
Predicting Lifetimes in Dynamically Allocated Memory.
939-945
- Joumana Ghosn, Yoshua Bengio:
Multi-Task Learning for Stock Selection.
946-952
- Michael Mozer, Lucky Vidmar, Robert H. Dodier:
The Neurothermostat: Predictive Optimal Control of Residential Heating Systems.
953-959
- Mahesan Niranjan:
Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches.
960-966
- Tony Plate, Pierre Band, Joel Bert, John Grace:
A Comparison between Neural Networks and other Statistical Techniques for Modeling the Relationship between Tobacco and Alcohol and Cancer.
967-973
- Satinder P. Singh, Dimitri P. Bertsekas:
Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems.
974-980
- Kagan Tumer, Nirmala Ramanujam, Rebecca R. Richards-Kortum, Joydeep Ghosh:
Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks.
981-987
- Ernest Wan, Don Bone:
Interpolating Earth-science Data using RBF Networks and Mixtures of Experts.
988-994
- Lizhong Wu, John E. Moody:
Multi-effect Decompositions for Financial Data Modeling.
995-1004
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