NIPS 1996: Denver, CO, USA

@proceedings{DBLP:conf/nips/1996N,
  editor    = {Michael Mozer and
               Michael I. Jordan and
               Thomas Petsche},
  title     = {Advances in Neural Information Processing Systems 9, NIPS, Denver,
               CO, USA, December 2-5, 1996},
  booktitle = {NIPS},
  publisher = {MIT Press},
  year      = {1997},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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 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
  

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 P. 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
  

Last update Thu May 24 04:38:55 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page