NIPS 2000:
Denver, CO, USA
Todd K. Leen, Thomas G. Dietterich, Volker Tresp (Eds.):
Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA.
MIT Press 2001 
- Ranit Aharonov-Barki, Isaac Meilijson, Eytan Ruppin:
Who Does What? A Novel Algorithm to Determine Function Localization.
3-9
- Shimon Edelman, Nathan Intrator:
A Productive, Systematic Framework for the Representation of Visual Structure.
10-16
- David B. Grimes, Michael Mozer:
The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving.
17-23
- Szabolcs Káli, Peter Dayan:
Hippocampally-Dependent Consolidation in a Hierarchical Model of Neocortex.
24-30
- Zhaoping Li, Peter Dayan:
Position Variance, Recurrence and Perceptual Learning.
31-37
- In Jae Myung, Mark A. Pitt, Shaobo Zhang, Vijay Balasubramanian:
The Use of MDL to Select among Computational Models of Cognition.
38-44
- Jonathan D. Nelson, Javier R. Movellan:
Active Inference in Concept Learning.
45-51
- Mark A. Smith, Garrison W. Cottrell, Karen L. Anderson:
The Early Word Catches the Weights.
52-58
- Joshua B. Tenenbaum, Thomas L. Griffiths:
Structure Learning in Human Causal Induction.
59-65
- Bosco S. Tjan:
Adaptive Object Representation with Hierarchically-Distributed Memory Sites.
66-72
- Blaise Agüera y Arcas, Adrienne L. Fairhall, William Bialek:
What Can a Single Neuron Compute?
75-81
- Kevin A. Archie, Bartlett W. Mel:
Dendritic Compartmentalization Could Underlie Competition and Attentional Biasing of Simultaneous Visual Stimuli.
82-88
- Angelo Arleo, Fabrizio Smeraldi, Stéphane Hug, Wulfram Gerstner:
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning.
89-95
- Suzanna Becker, Neil Burgess:
Modelling Spatial Recall, Mental Imagery and Neglect.
96-102
- William Bialek:
Stability and Noise in Biochemical Switches.
103-109
- Gal Chechik, Naftali Tishby:
Temporally Dependent Plasticity: An Information Theoretic Account.
110-116
- Sophie Denève, Jean-René Duhamel, Alexandre Pouget:
A New Model of Spatial Representation in Multimodal Brain Areas.
117-123
- Adrienne L. Fairhall, Geoffrey D. Lewen, William Bialek, Robert R. de Ruyter van Steveninck:
Multiple Timescales of Adaptation in a Neural Code.
124-130
- Sham Kakade, Peter Dayan:
Dopamine Bonuses.
131-137
- Thomas Natschläger, Wolfgang Maass:
Finding the Key to a Synapse.
138-144
- Thomas Natschläger, Wolfgang Maass, Eduardo D. Sontag, Anthony M. Zador:
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics.
145-151
- Silvia Scarpetta, Zhaoping Li, John A. Hertz:
Spike-Timing-Dependent Learning for Oscillatory Networks.
152-158
- Elad Schneidman, Naama Brenner, Naftali Tishby, Robert R. de Ruyter van Steveninck, William Bialek:
Universality and Individuality in a Neural Code.
159-165
- Odelia Schwartz, Eero P. Simoncelli:
Natural Sound Statistics and Divisive Normalization in the Auditory System.
166-172
- Mario F. Simoni, Gennady S. Cymbalyuk, Michael E. Sorensen, Ronald L. Calabrese, Stephen P. DeWeerth:
Development of Hybrid Systems: Interfacing a Silicon Neuron to a Leech Heart Interneuron.
173-179
- Carl van Vreeswijk:
Whence Sparseness?
180-186
- Shai Ben-David, Hans-Ulrich Simon:
Efficient Learning of Linear Perceptrons.
189-195
- Olivier Bousquet, André Elisseeff:
Algorithmic Stability and Generalization Performance.
196-202
- Peter Dayan:
Competition and Arbors in Ocular Dominance.
203-209
- Thore Graepel, Ralf Herbrich, Robert C. Williamson:
From Margin to Sparsity.
210-216
- Richard H. R. Hahnloser, H. Sebastian Seung:
Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks.
217-223
- Ralf Herbrich, Thore Graepel:
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work.
224-230
- Tony Jebara, Alex Pentland:
On Reversing Jensen's Inequality.
231-237
- Hilbert J. Kappen, Wim Wiegerinck:
Second Order Approximations for Probability Models.
238-244
- Vladimir Koltchinskii, Dmitriy Panchenko, Fernando Lozano:
Some New Bounds on the Generalization Error of Combined Classifiers.
245-251
- Adam Kowalczyk:
Sparsity of Data Representation of Optimal Kernel Machine and Leave-one-out Estimator.
252-258
- Robert A. Legenstein, Wolfgang Maass:
Foundations for a Circuit Complexity Theory of Sensory Processing.
259-265
- Martijn A. R. Leisink, Hilbert J. Kappen:
A Tighter Bound for Graphical Models.
266-272
- Dörthe Malzahn, Manfred Opper:
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations.
273-279
- Shie Mannor, Ron Meir:
Weak Learners and Improved Rates of Convergence in Boosting.
280-286
- Ilya Nemenman, William Bialek:
Learning Continuous Distributions: Simulations With Field Theoretic Priors.
287-293
- Carl Edward Rasmussen, Zoubin Ghahramani:
Occam's Razor.
294-300
- Bernhard Schölkopf:
The Kernel Trick for Distances.
301-307
- Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson:
Regularization with Dot-Product Kernels.
308-314
- Toshiyuki Tanaka:
Analysis of Bit Error Probability of Direct-Sequence CDMA Multiuser Demodulators.
315-321
- Renato Vicente, David Saad, Yoshiyuki Kabashima:
Error-correcting Codes on a Bethe-like Lattice.
322-328
- Sumio Watanabe:
Algebraic Information Geometry for Learning Machines with Singularities.
329-335
- Ole Winther:
Computing with Finite and Infinite Networks.
336-342
- K. Y. Michael Wong, Hidetoshi Nishimori:
Stagewise Processing in Error-correcting Codes and Image Restoration.
343-349
- Xiaohui Xie, Richard H. R. Hahnloser, H. Sebastian Seung:
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks.
350-356
- Tong Zhang:
Convergence of Large Margin Separable Linear Classification.
357-363
- Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik:
A Support Vector Method for Clustering.
367-373
- Chiranjib Bhattacharyya, S. Sathiya Keerthi:
A Variational Mean-Field Theory for Sigmoidal Belief Networks.
374-380
- Timothy X. Brown:
Direct Classification with Indirect Data.
381-387
- Igor V. Cadez, Padhraic Smyth:
Model Complexity, Goodness of Fit and Diminishing Returns.
388-394
- Colin Campbell, Kristin P. Bennett:
A Linear Programming Approach to Novelty Detection.
395-401
- Rich Caruana, Steve Lawrence, C. Lee Giles:
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping.
402-408
- Gert Cauwenberghs, Tomaso Poggio:
Incremental and Decremental Support Vector Machine Learning.
409-415
- Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik:
Vicinal Risk Minimization.
416-422
- Scott Saobing Chen, Ramesh A. Gopinath:
Gaussianization.
423-429
- David A. Cohn, Thomas Hofmann:
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity.
430-436
- Koby Crammer, Yoram Singer:
Improved Output Coding for Classification Using Continuous Relaxation.
437-443
- Lehel Csató, Manfred Opper:
Sparse Representation for Gaussian Process Models.
444-450
- Peter Dayan, Sham Kakade:
Explaining Away in Weight Space.
451-457
- Carlotta Domeniconi, Jing Peng, Dimitrios Gunopulos:
An Adaptive Metric Machine for Pattern Classification.
458-464
- Oliver B. Downs:
High-temperature Expansions for Learning Models of Nonnegative Data.
465-471
- Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, René Garcia:
Incorporating Second-Order Functional Knowledge for Better Option Pricing.
472-478
- Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller:
Discovering Hidden Variables: A Structure-Based Approach.
479-485
- Brendan J. Frey, Anitha Kannan:
Accumulator Networks: Suitors of Local Probability Propagation.
486-492
- Brendan J. Frey, Relu Patrascu, Tommi Jaakkola, Jodi Moran:
Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks.
493-499
- Claudio Gentile:
A New Approximate Maximal Margin Classification Algorithm.
500-506
- Zoubin Ghahramani, Matthew J. Beal:
Propagation Algorithms for Variational Bayesian Learning.
507-513
- Thore Graepel, Ralf Herbrich:
The Kernel Gibbs Sampler.
514-520
- Alexander G. Gray, Andrew W. Moore:
`N-Body' Problems in Statistical Learning.
521-527
- Ralf Herbrich, Thore Graepel:
Large Scale Bayes Point Machines.
528-534
- Sepp Hochreiter, Michael Mozer:
Beyond Maximum Likelihood and Density Estimation: A Sample-Based Criterion for Unsupervised Learning of Complex Models.
535-541
- Pedro A. d. F. R. Højen-Sørensen, Ole Winther, Lars Kai Hansen:
Ensemble Learning and Linear Response Theory for ICA.
542-548
- Ulrik Kjems, Lars Kai Hansen, Stephen C. Strother:
Generalizable Singular Value Decomposition for Ill-posed Datasets.
549-555
- Daniel D. Lee, H. Sebastian Seung:
Algorithms for Non-negative Matrix Factorization.
556-562
- Huma Lodhi, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins:
Text Classification using String Kernels.
563-569
- Wei Lu, Jagath C. Rajapakse:
Constrained Independent Component Analysis.
570-576
- Olvi L. Mangasarian, David R. Musicant:
Active Support Vector Machine Classification.
577-583
- Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric A. Wan:
The Unscented Particle Filter.
584-590
- Sebastian Mika, Gunnar Rätsch, Klaus-Robert Müller:
A Mathematical Programming Approach to the Kernel Fisher Algorithm.
591-597
- Thomas P. Minka:
Automatic Choice of Dimensionality for PCA.
598-604
- Eiji Mizutani, James Demmel:
On Iterative Krylov-Dogleg Trust-Region Steps for Solving Neural Networks Nonlinear Least Squares Problems.
605-611
- Oren Shriki, Haim Sompolinsky, Daniel D. Lee:
An Information Maximization Approach to Overcomplete and Recurrent Representations.
612-618
- Alex J. Smola, Peter L. Bartlett:
Sparse Greedy Gaussian Process Regression.
619-625
- Martin Szummer, Tommi Jaakkola:
Kernel Expansions with Unlabeled Examples.
626-632
- Michael E. Tipping:
Sparse Kernel Principal Component Analysis.
633-639
- Naftali Tishby, Noam Slonim:
Data Clustering by Markovian Relaxation and the Information Bottleneck Method.
640-646
- Simon Tong, Daphne Koller:
Active Learning for Parameter Estimation in Bayesian Networks.
647-653
- Volker Tresp:
Mixtures of Gaussian Processes.
654-660
- Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky:
Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles.
661-667
- Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik:
Feature Selection for SVMs.
668-674
- Christopher K. I. Williams:
On a Connection between Kernel PCA and Metric Multidimensional Scaling.
675-681
- Christopher K. I. Williams, Matthias Seeger:
Using the Nyström Method to Speed Up Kernel Machines.
682-688
- Jonathan S. Yedidia, William T. Freeman, Yair Weiss:
Generalized Belief Propagation.
689-695
- Richard S. Zemel, Toniann Pitassi:
A Gradient-Based Boosting Algorithm for Regression Problems.
696-702
- Tong Zhang:
Regularized Winnow Methods.
703-709
- David Hsu, Miguel Figueroa, Chris Diorio:
A Silicon Primitive for Competitive Learning.
713-719
- Hiroyuki Kurino, M. Nakagawa, Kang Wook Lee, Tomonori Nakamura, Yuusuke Yamada, Ki Tae Park, Mitsumasa Koyanagi:
Smart Vision Chip Fabricated Using Three Dimensional Integration Technology.
720-726
- Shih-Chii Liu, Bradley A. Minch:
Homeostasis in a Silicon Integrate and Fire Neuron.
727-733
- Fernando Pérez-Cruz, Pedro Luis Alarcón-Diana, Angel Navia-Vázquez, Antonio Artés-Rodríguez:
Fast Training of Support Vector Classifiers.
734-740
- Susanne Still, Bernhard Schölkopf, Klaus Hepp, Rodney J. Douglas:
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm.
741-747
- Hervé Bourlard, Samy Bengio, Katrin Weber:
New Approaches Towards Robust, Adaptive Speech Recognition (invited paper).
751-757
- Hagai Attias, John C. Platt, Alex Acero, Li Deng:
Speech Denoising and Dereverberation Using Probabilistic Models.
758-764
- Un-Min Bae, Soo-Young Lee:
Combining ICA and Top-Down Attention for Robust Speech Recognition.
765-771
- John W. Fisher III, Trevor Darrell, William T. Freeman, Paul A. Viola:
Learning Joint Statistical Models for Audio-Visual Fusion and Segregation.
772-778
- Mark J. F. Gales:
Factored Semi-Tied Covariance Matrices.
779-785
- Lucas C. Parra, Clay Spence, Paul Sajda:
Higher-Order Statistical Properties Arising from the Non-Stationarity of Natural Signals.
786-792
- Sam T. Roweis:
One Microphone Source Separation.
793-799
- George Saon, Mukund Padmanabhan:
Minimum Bayes Error Feature Selection for Continuous Speech Recognition.
800-806
- Lawrence K. Saul, Jont B. Allen:
Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech.
807-813
- Malcolm Slaney, Michele Covell:
FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks.
814-820
- Jürgen Tchorz, Michael Kleinschmidt, Birger Kollmeier:
Noise Suppression Based on Neurophysiologically-motivated SNR Estimation for Robust Speech Recognition.
821-827
- Serge Belongie, Jitendra Malik, Jan Puzicha:
Shape Context: A New Descriptor for Shape Matching and Object Recognition.
831-837
- Rafal Bogacz, Malcolm W. Brown, Christophe G. Giraud-Carrier:
Emergence of Movement Sensitive Neurons' Properties by Learning a Sparse Code for Natural Moving Images.
838-844
- James M. Coughlan, Alan L. Yuille:
The Manhattan World Assumption: Regularities in Scene Statistics which Enable Bayesian Inference.
845-851
- Frank Dellaert, Steven M. Seitz, Sebastian Thrun, Charles E. Thorpe:
Feature Correspondence: A Markov Chain Monte Carlo Approach.
852-858
- Trausti T. Kristjansson, Brendan J. Frey:
Keeping Flexible Active Contours on Track using Metropolis Updates.
859-865
- Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski:
Color Opponency Constitutes a Sparse Representation for the Chromatic Structure of Natural Scenes.
866-872
- Marina Meila, Jianbo Shi:
Learning Segmentation by Random Walks.
873-879
- Javier R. Movellan, Paul Mineiro, Ruth J. Williams:
Partially Observable SDE Models for Image Sequence Recognition Tasks.
880-886
- Bruno A. Olshausen, Phil Sallee, Michael S. Lewicki:
Learning Sparse Image Codes using a Wavelet Pyramid Architecture.
887-893
- Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie:
Learning and Tracking Cyclic Human Motion.
894-900
- Penio S. Penev:
Redundancy and Dimensionality Reduction in Sparse-Distributed Representations of Natural Objects in Terms of Their Local Features.
901-907
- Yee Whye Teh, Geoffrey E. Hinton:
Rate-coded Restricted Boltzmann Machines for Face Recognition.
908-914
- Barbara Zenger, Christof Koch:
Divisive and Subtractive Mask Effects: Linking Psychophysics and Biophysics.
915-921
- Cynthia Archer, Todd K. Leen:
From Mixtures of Mixtures to Adaptive Transform Coding.
925-931
- Yoshua Bengio, Réjean Ducharme, Pascal Vincent:
A Neural Probabilistic Language Model.
932-938
- Michael S. Gray, Terrence J. Sejnowski, Javier R. Movellan:
A Comparison of Image Processing Techniques for Visual Speech Recognition Applications.
939-945
- Paul M. Hayton, Bernhard Schölkopf, Lionel Tarassenko, Paul Anuzis:
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra.
946-952
- Guy Mayraz, Geoffrey E. Hinton:
Recognizing Hand-written Digits Using Hierarchical Products of Experts.
953-959
- Baback Moghaddam, Ming-Hsuan Yang:
Sex with Support Vector Machines.
960-966
- Milind R. Naphade, Igor Kozintsev, Thomas S. Huang:
Probabilistic Semantic Video Indexing.
967-973
- Predrag Neskovic, Philip C. Davis, Leon N. Cooper:
Interactive Parts Model: An Application to Recognition of On-line Cursive Script.
974-980
- Vladimir Pavlovic, James M. Rehg, John MacCormick:
Learning Switching Linear Models of Human Motion.
981-987
- Liam Pedersen, Dimitrios Apostolopoulos, William Whittaker:
Bayes Networks on Ice: Robotic Search for Antarctic Meteorites.
988-994
- Vasin Punyakanok, Dan Roth:
The Use of Classifiers in Sequential Inference.
995-1001
- Arno Schödl, Irfan A. Essa:
Machine Learning for Video-Based Rendering.
1002-1008
- Nuno Vasconcelos, Andrew Lippman:
Bayesian Video Shot Segmentation.
1009-1015
- David Andre, Stuart J. Russell:
Programmable Reinforcement Learning Agents.
1019-1025
- Justin A. Boyan, Michael L. Littman:
Exact Solutions to Time-Dependent MDPs.
1026-1032
- Jakob Carlström:
Decomposition of Reinforcement Learning for Admission Control of Self-Similar Call Arrival Processes.
1033-1039
- Geoffrey J. Gordon:
Reinforcement Learning with Function Approximation Converges to a Region.
1040-1046
- Natalia Hernandez-Gardiol, Sridhar Mahadevan:
Hierarchical Memory-Based Reinforcement Learning.
1047-1053
- Anders Jonsson, Andrew G. Barto:
Automated State Abstraction for Options using the U-Tree Algorithm.
1054-1060
- Jun Morimoto, Kenji Doya:
Robust Reinforcement Learning.
1061-1067
- Dirk Ormoneit, Peter W. Glynn:
Kernel-Based Reinforcement Learning in Average-Cost Problems: An Application to Optimal Portfolio Choice.
1068-1074
- Brian Sallans, Geoffrey E. Hinton:
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task.
1075-1081
- Christian R. Shelton:
Balancing Multiple Sources of Reward in Reinforcement Learning.
1082-1088
- Robert St-Aubin, Jesse Hoey, Craig Boutilier:
APRICODD: Approximate Policy Construction Using Decision Diagrams.
1089-1095
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