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ORCIDNIPS 1996: Denver, CO, USA
- Michael Mozer, Michael I. Jordan, Thomas Petsche:
Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996. MIT Press 1997
Cognitive Science
- Ron Papka, James P. Callan, Andrew G. Barto:
Text-Based Information Retrieval Using Exponentiated Gradient Descent. 3-9 - Jordan B. Pollack, Alan D. Blair:
Why did TD-Gammon Work? 10-16 - Maximilian Riesenhuber, Peter Dayan:
Neural Models for Part-Whole Hierarchies. 17-26
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 A. 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 R. Brightwell, Claire Kenyon, Hélène Paugam-Moisy:
Multilayer Neural Networks: One or Two Hidden Layers? 148-154 - Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander 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, Alexander J. Smola:
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. 281-287 - Ansgar Heinrich Ludolf 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 S. 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 A. 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 - Axel 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 D. 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 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 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
Control, Navigation and Planning
- Scott Davies:
Multidimensional Triangulation and Interpolation for Reinforcement Learning. 1005-1011 - Kenji Doya:
Efficient Nonlinear Control with Actor-Tutor Architecture. 1012-1018 - Michael O. Duff, Andrew G. Barto:
Local Bandit Approximation for Optimal Learning Problems. 1019-1025 - Eric A. Hansen, Andrew G. Barto, Shlomo Zilberstein:
Reinforcement Learning for Mixed Open-loop and Closed-loop Control. 1026-1032 - Stephan Pareigis:
Multi-Grid Methods for Reinforcement Learning in Controlled Diffusion Processes. 1033-1039 - Stefan Schaal:
Learning from Demonstration. 1040-1046 - Jeff G. Schneider:
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning. 1047-1053 - Satinder Singh, Peter Dayan:
Analytical Mean Squared Error Curves in Temporal Difference Learning. 1054-1060 - Magnus Stensmo, Terrence J. Sejnowski:
Learning Decision Theoretic Utilities through Reinforcement Learning. 1061-1067 - Gerald Tesauro, Gregory R. Galperin:
On-line Policy Improvement using Monte-Carlo Search. 1068-1074 - John N. Tsitsiklis, Benjamin Van Roy:
Analysis of Temporal-Diffference Learning with Function Approximation. 1075-1081 - John N. Tsitsiklis, Benjamin Van Roy:
Approximate Solutions to Optimal Stopping Problems. 1082-1088
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