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NIPS 1999: Denver, CO, USA
- Sara A. Solla, Todd K. Leen, Klaus-Robert Müller:
Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29 - December 4, 1999]. The MIT Press 2000, ISBN 0-262-19450-3
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 M. 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 - Alexander 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 S. 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, Alexander 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, Alexander 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, Alexander 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 W. 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
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
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 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 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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