NIPS 1999: Denver, CO, USA

Cognitive Science

• Jessica D. Bayliss, Dana H. Ballard:
  Recognizing Evoked Potentials in a Virtual Environment. 3-9
  
• Gustavo Deco, Josef Zihl:
  A Neurodynamical Approach to Visual Attention. 10-16
  
• Thea B. Ghiselli-Crippa, Paul W. Munro:
  Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information. 17-23
  
• Sham Kakade, Peter Dayan:
  Acquisition in Autoshaping. 24-30
  
• Soo-Young Lee, Michael Mozer:
  Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. 31-37
  
• Xiuwen Liu, DeLiang L. Wang:
  Perceptual Organization Based on Temporal Dynamics. 38-44
  
• Javier R. Movellan, James L. McClelland:
  Information Factorization in Connectionist Models of Perception. 45-51
  
• Shan Parfitt, Peter Tiño, Georg Dorffner:
  Graded Grammaticality in Prediction Fractal Machines. 52-58
  
• Joshua B. Tenenbaum:
  Rules and Similarity in Concept Learning. 59-65
  
• Bradley Tonkes, Alan D. Blair, Janet Wiles:
  Evolving Learnable Languages. 66-72
  
• Ton Weijters, Antal van den Bosch, Eric O. Postma:
  Learning Statistically Neutral Tasks without Expert Guidance. 73-79
  
• Richard S. Zemel, Michael Mozer:
  A Generative Model for Attractor Dynamics. 80-88
  

Neuroscience

• Péter Adorján, Lars Schwabe, Christian Piepenbrock, Klaus Obermayer:
  Recurrent Cortical Competition: Strengthen or Weaken? 89-95
  
• Gal Chechik, Isaac Meilijson, Eytan Ruppin:
  Effective Learning Requires Neuronal Remodeling of Hebbian Synapses. 96-102
  
• Dmitri B. Chklovskii, Charles F. Stevens:
  Wiring Optimization in the Brain. 103-107
  
• Dmitri B. Chklovskii:
  Optimal Sizes of Dendritic and Axonal Arbors. 108-114
  
• Christian W. Eurich, Stefan D. Wilke, Helmut Schwegler:
  Neural Representation of Multi-Dimensional Stimuli. 115-121
  
• Geoffrey E. Hinton, Andrew D. Brown:
  Spiking Boltzmann Machines. 122-128
  
• David Horn, Nir Levy, Isaac Meilijson, Eytan Ruppin:
  Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly. 129-135
  
• Zhaoping Li:
  Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects? 136-142
  
• Amit Manwani, Peter N. Steinmetz, Christof Koch:
  Channel Noise in Excitable Neural Membranes. 143-149
  
• Paul W. Munro, Gerardina Hernández:
  LTD Facilitates Learning in a Noisy Environment. 150-156
  
• Panayiota Poirazi, Bartlett W. Mel:
  Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration. 157-163
  
• Rajesh P. N. Rao, Terrence J. Sejnowski:
  Predictive Sequence Learning in Recurrent Neocortical Circuits. 164-170
  
• Alfonso Renart, Néstor Parga, Edmund T. Rolls:
  A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay Tasks. 171-177
  
• Elad Schneidman, Idan Segev, Naftali Tishby:
  Information Capacity and Robustness of Stochastic Neuron Models. 178-184
  
• Akaysha C. Tang, Barak A. Pearlmutter, Tim A. Hely, Michael Zibulevsky, Michael P. Weisend:
  An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task. 185-191
  
• Si Wu, Hiroyuki Nakahara, Noboru Murata, Shun-ichi Amari:
  Population Decoding Based on an Unfaithful Model. 192-198
  
• Xiaohui Xie, H. Sebastian Seung:
  Spike-based Learning Rules and Stabilization of Persistent Neural Activity. 199-208
  

Theory

• Hagai Attias:
  A Variational Baysian Framework for Graphical Models. 209-215
  
• Joachim M. Buhmann, Marcus Held:
  Model Selection in Clustering by Uniform Convergence Bounds. 216-222
  
• Christopher J. C. Burges, David J. Crisp:
  Uniqueness of the SVM Solution. 223-229
  
• Olivier Chapelle, Vladimir Vapnik:
  Model Selection for Support Vector Machines. 230-236
  
• Anthony C. C. Coolen, C. W. H. Mace:
  Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers. 237-243
  
• David J. Crisp, Christopher J. C. Burges:
  A Geometric Interpretation of v-SVM Classifiers. 244-250
  
• Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther:
  Efficient Approaches to Gaussian Process Classification. 251-257
  
• Nigel Duffy, David P. Helmbold:
  Potential Boosters? 258-264
  
• Lars Kai Hansen:
  Bayesian Averaging is Well-Temperated. 265-271
  
• Yoshiyuki Kabashima, Tatsuto Murayama, David Saad, Renato Vicente:
  Regular and Irregular Gallager-zype Error-Correcting Codes. 272-278
  
• Jonathan Q. Li, Andrew R. Barron:
  Mixture Density Estimation. 279-285
  
• Song Li, K. Y. Michael Wong:
  Statistical Dynamics of Batch Learning. 286-292
  
• Wolfgang Maass:
  Neural Computation with Winner-Take-All as the Only Nonlinear Operation. 293-299
  
• Yishay Mansour, David A. McAllester:
  Boosting with Multi-Way Branching in Decision Trees. 300-306
  
• Claude Nadeau, Yoshua Bengio:
  Inference for the Generalization Error. 307-313
  
• Toru Ohira, Yuzuru Sato, Jack D. Cowan:
  Resonance in a Stochastic Neuron Model with Delayed Interaction. 314-320
  
• Sebastian Risau-Gusman, Mirta B. Gordon:
  Understanding Stepwise Generalization of Support Vector Machines: a Toy Model. 321-327
  
• Michael Schmitt:
  Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks. 328-334
  
• Hava T. Siegelmann, Alexander Roitershtein, Asa Ben-Hur:
  Noisy Neural Networks and Generalizations. 335-341
  
• Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson:
  The Entropy Regularization Information Criterion. 342-348
  
• Peter Sollich:
  Probabilistic Methods for Support Vector Machines. 349-355
  
• Sumio Watanabe:
  Algebraic Analysis for Non-regular Learning Machines. 356-362
  
• Liqing Zhang, Shun-ichi Amari, Andrzej Cichocki:
  Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems. 363-369
  
• Tong Zhang:
  Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions. 370-378
  

Algorithms and Architecture

• Christophe Andrieu, João F. G. de Freitas, Arnaud Doucet:
  Robust Full Bayesian Methods for Neural Networks. 379-385
  
• Hagai Attias:
  Independent Factor Analysis with Temporally Structured Sources. 386-392
  
• David Barber, Peter Sollich:
  Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. 393-399
  
• Yoshua Bengio, Samy Bengio:
  Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks. 400-406
  
• Thomas Briegel, Volker Tresp:
  Robust Neural Network Regression for Offline and Online Learning. 407-413
  
• Miguel Á. Carreira-Perpiñán:
  Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints. 414-420
  
• Olivier Chapelle, Vladimir Vapnik, Jason Weston:
  Transductive Inference for Estimating Values of Functions. 421-427
  
• Oliver B. Downs, David J. C. MacKay, Daniel D. Lee:
  The Nonnegative Boltzmann Machine. 428-434
  
• Gary William Flake, Barak A. Pearlmutter:
  Differentiating Functions of the Jacobian with Respect to the Weights. 435-441
  
• Brendan J. Frey:
  Local Probability Propagation for Factor Analysis. 442-448
  
• Zoubin Ghahramani, Matthew J. Beal:
  Variational Inference for Bayesian Mixtures of Factor Analysers. 449-455
  
• Thore Graepel, Ralf Herbrich, Klaus Obermayer:
  Bayesian Transduction. 456-462
  
• Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh:
  Learning to Parse Images. 463-469
  
• Tommi Jaakkola, Marina Meila, Tony Jebara:
  Maximum Entropy Discrimination. 470-476
  
• Nebojsa Jojic, Brendan J. Frey:
  Topographic Transformation as a Discrete Latent Variable. 477-483
  
• Pavel Laskov:
  An Improved Decomposition Algorithm for Regression Support Vector Machines. 484-490
  
• Daniel D. Lee, Uri Rokni, Haim Sompolinsky:
  Algorithms for Independent Components Analysis and Higher Order Statistics. 491-497
  
• Yi Li, Philip M. Long:
  The Relaxed Online Maximum Margin Algorithm. 498-504
  
• Dimitris Margaritis, Sebastian Thrun:
  Bayesian Network Induction via Local Neighborhoods. 505-511
  
• Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean:
  Boosting Algorithms as Gradient Descent. 512-518
  
• Chris Mesterharm:
  A Multi-class Linear Learning Algorithm Related to Winnow. 519-525
  
• Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller:
  Invariant Feature Extraction and Classification in Kernel Spaces. 526-532
  
• Andrew Y. Ng, Michael I. Jordan:
  Approximate Inference A lgorithms for Two-Layer Bayesian Networks. 533-539
  
• Dirk Ormoneit, Trevor Hastie:
  Optimal Kernel Shapes for Local Linear Regression. 540-546
  
• John C. Platt, Nello Cristianini, John Shawe-Taylor:
  Large Margin DAGs for Multiclass Classification. 547-553
  
• Carl Edward Rasmussen:
  The Infinite Gaussian Mixture Model. 554-560
  
• Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika:
  v-Arc: Ensemble Learning in the Presence of Outliers. 561-567
  
• Volker Roth, Volker Steinhage:
  Nonlinear Discriminant Analysis Using Kernel Functions. 568-574
  
• Paat Rusmevichientong, Benjamin Van Roy:
  An Analysis of Turbo Decoding with Gaussian Densities. 575-581
  
• Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt:
  Support Vector Method for Novelty Detection. 582-588
  
• Mike Schuster:
  Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks. 589-595
  
• Dale Schuurmans:
  Greedy Importance Sampling. 596-602
  
• Matthias Seeger:
  Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. 603-609
  
• Yoram Singer:
  Leveraged Vector Machines. 610-616
  
• Noam Slonim, Naftali Tishby:
  Agglomerative Information Bottleneck. 617-623
  
• Masashi Sugiyama, Hidemitsu Ogawa:
  Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. 624-630
  
• S. Sundararajan, S. Sathiya Keerthi:
  Predictive App roaches for Choosing Hyperparameters in Gaussian Processes. 631-637
  
• Peter Sykacek:
  On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling. 638-644
  
• Peter Tiño, Georg Dorffner:
  Building Predictive Models from Fractal Representations of Symbolic Sequences. 645-651
  
• Michael E. Tipping:
  The Relevance Vector Machine. 652-658
  
• Vladimir Vapnik, Sayan Mukherjee:
  Support Vector Method for Multivariate Density Estimation. 659-665
  
• Eric A. Wan, Rudolph van der Merwe, Alex T. Nelson:
  Dual Estimation and the Unscented Transformation. 666-672
  
• Yair Weiss, William T. Freeman:
  Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology. 673-679
  
• Christopher K. I. Williams:
  A MCMC Approach to Hierarchical Mixture Modelling. 680-686
  
• Howard Hua Yang, John E. Moody:
  Data Visualization and Feature Selection: New Algorithms for Nongaussian Data. 687-702
  
• Mark Zlochin, Yoram Baram:
  Manifold Stochastic Dynamics for Bayesian Learning. 694-702
  

Implementation

• Charles Lee Isbell Jr., Parry Husbands:
  The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning. 703-709
  
• Oliver Landolt, Steve Gyger:
  An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control. 710-716
  
• Shih-Chii Liu:
  A Winner-Take-All Circuit with Controllable Soft Max Property. 717-723
  
• Girish N. Patel, Edgar A. Brown, Stephen P. DeWeerth:
  A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion. 724-730
  
• Girish N. Patel, Gennady S. Cymbalyuk, Ronald L. Calabrese, Stephen P. DeWeerth:
  Bifurcation Analysis of a Silicon Neuron. 731-737
  
• André van Schaik:
  An Analog VLSI Model of Periodicity Extraction. 738-746
  

Speech, Handwriting and Signal Processing

• Guy J. Brown, DeLiang L. Wang:
  An Oscillatory Correlation Frame work for Computational Auditory Scene Analysis. 747-753
  
• Pedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen:
  Bayesian Modelling of fMRI lime Series. 754-760
  
• Craig T. Jin, Simon Carlile:
  Neural System Model of Human Sound Localization. 761-767
  
• Craig T. Jin, Anna Corderoy, Simon Carlile, André van Schaik:
  Spectral Cues in Human Sound Localization. 768-774
  
• Justinian P. Rosca, Joseph Ó Ruanaidh, Alexander Jourjine, Scott Rickard:
  Broadband Direction-Of-Arrival Estimation Based on Second Order Statistics. 775-781
  
• Sam T. Roweis:
  Constrained Hidden Markov Models. 782-788
  
• Nicol N. Schraudolph, Xavier Giannakopoulos:
  Online Independent Component Analysis with Local Learning Rate Adaptation. 789-795
  
• Gavin Smith, João F. G. de Freitas, Tony Robinson, Mahesan Niranjan:
  Speech Modelling Using Subspace and EM Techniques. 796-802
  
• Howard Hua Yang, Hynek Hermansky:
  Search for Information Bearing Components in Speech. 803-812
  

Visual Processing

• John R. Hershey, Javier R. Movellan:
  Audio Vision: Using Audio-Visual Synchrony to Locate Sounds. 813-819
  
• Nicholas R. Howe, Michael E. Leventon, William T. Freeman:
  Bayesian Reconstruction of 3D Human Motion from Single-Camera Video. 820-826
  
• Aapo Hyvärinen, Patrik O. Hoyer:
  Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. 827-833
  
• Tai Sing Lee, Stella X. Yu:
  An Information-Theoretic Framework for Understanding Saccadic Eye Movements. 834-840
  
• Bruno A. Olshausen, K. Jarrod Millman:
  Learning Sparse Codes with a Mixture-of-Gaussians Prior. 841-847
  
• Clay Spence, Lucas C. Parra:
  Hierarchical Image Probability (H1P) Models. 848-854
  
• Martin J. Wainwright, Eero P. Simoncelli:
  Scale Mixtures of Gaussians and the Statistics of Natural Images. 855-861
  
• Ming-Hsuan Yang, Dan Roth, Narendra Ahuja:
  A SNoW-Based Face Detector. 862-868
  
• Zhiyong Yang, Richard S. Zemel:
  Managing Uncertainty in Cue Combination. 869-878
  

Applications

• Rembrandt Bakker, Jaap C. Schouten, Marc-Olivier Coppens, Floris Takens, C. Lee Giles, Cor M. van den Bleek:
  Robust Learning of Chaotic Attractors. 879-885
  
• Marian Stewart Bartlett, Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman, Terrence J. Sejnowski:
  Image Representations for Facial Expression Coding. 886-892
  
• Timothy X. Brown:
  Low Power Wireless Communication via Reinforcement Learning. 893-899
  
• John W. Fisher III, Alexander T. Ihler, Paul A. Viola:
  Learning Informative Statistics: A Nonparametnic Approach. 900-906
  
• Richard M. Golden:
  Kirchoff Law Markov Fields for Analog Circuit Design. 907-913
  
• Thomas Hofmann:
  Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization. 914-920
  
• Yuansong Liao, John E. Moody:
  Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting. 921-927
  
• Eric Mjolsness, Tobias Mann, Rebecca Castaño, Barbara J. Wold:
  From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data. 928-934
  
• Michael Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky:
  Churn Reduction in the Wireless Industry. 935-941
  
• Lucas C. Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus-Robert Müller:
  Unmixing Hyperspectral Data. 942-948
  
• Holger Schoner, Martin Stetter, Ingo Schießl, John E. W. Mayhew, Jennifer S. Lund, Niall McLoughlin, Klaus Obermayer:
  Application of Blind Separation of Sources to Optical Recording of Brain Activity. 949-955
  
• Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker:
  Reinforcement Learning for Spoken Dialogue Systems. 956-962
  
• Xubo B. Song, Joseph Sill, Yaser S. Abu-Mostafa, Harvey Kasdan:
  Image Recognition in Context: Application to Microscopic Urinalysis. 963-969
  
• Shivakumar Vaithyanathan, Byron Dom:
  Generalized Model Selection for Unsupervised Learning in High Dimensions. 970-976
  
• Nuno Vasconcelos, Andrew Lippman:
  Learning from User Feedback in Image Retrieval Systems. 977-986
  

Control, Navigation and Planning

• Samuel P. M. Choi, Dit-Yan Yeung, Nevin Lianwen Zhang:
  An Environment Model for Nonstationary Reinforcement Learning. 987-993
  
• Thomas G. Dietterich:
  State Abstraction in MAXQ Hierarchical Reinforcement Learning. 994-1000
  
• Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
  Approximate Planning in Large POMDPs via Reusable Trajectories. 1001-1007
  
• Vijay R. Konda, John N. Tsitsiklis:
  Actor-Critic Algorithms. 1008-1014
  
• Kevin P. Murphy:
  Bayesian Map Learning in Dynamic Environments. 1015-1021
  
• Andrew Y. Ng, Ronald Parr, Daphne Koller:
  Policy Search via Density Estimation. 1022-1028
  
• Stephen Piche, James D. Keeler, Greg Martin, Gene Boe, Doug Johnson, Mark Gerules:
  Neural Network Based Model Predictive Control. 1029-1035
  
• Andres C. Rodriguez, Ronald Parr, Daphne Koller:
  Reinforcement Learning Using Approximate Belief States. 1036-1042
  
• Nicholas Roy, Sebastian Thrun:
  Coastal Navigation with Mobile Robots. 1043-1049
  
• Brian Sallans:
  Learning Factored Representations for Partially Observable Markov Decision Processes. 1050-1056
  
• Richard S. Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour:
  Policy Gradient Methods for Reinforcement Learning with Function Approximation. 1057-1063
  
• Sebastian Thrun:
  Monte Carlo POMDPs. 1064-1070
  

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