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NIPS 2000: Denver, CO, USA
- Todd K. Leen, Thomas G. Dietterich, Volker Tresp:
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 M. 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 Elliott Sorensen, Ronald L. Calabrese, Stephen P. DeWeerth:
Development of Hybrid Systems: Interfacing a Silicon Neuron to a Leech Heart Interneuron. NIPS 2000: 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 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 - Alexander 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 A. 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 M. 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 S. 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
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 - Alexander J. Smola, Peter L. Bartlett:
Sparse Greedy Gaussian Process Regression. 619-625 - Martin Szummer, Tommi S. 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 A. 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 W. 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 E. Figueroa, Chris Diorio:
A Silicon Primitive for Competitive Learning. 713-719 - Hiroyuki Kurino, Masaki 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, Ángel 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 J. 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
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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