NIPS 1999: Denver, CO, USA

Sara A. Solla, Todd K. Leen, Klaus-Robert Müller (Eds.): 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 BibTeX

@proceedings{DBLP:conf/nips/1999,
  editor    = {Sara A. Solla and
               Todd K. Leen and
               Klaus-Robert M{\"u}ller},
  title     = {Advances in Neural Information Processing Systems 12, [NIPS Conference,
               Denver, Colorado, USA, November 29 - December 4, 1999]},
  booktitle = {NIPS},
  publisher = {The MIT Press},
  year      = {2000},
  isbn      = {0-262-19450-3},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Cognitive Science

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

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

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

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

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

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

John Hershey, Javier R. Movellan:
Audio Vision: Using Audio-Visual Synchrony to Locate Sounds. 813-819
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Nicholas R. Howe, Michael E. Leventon, William T. Freeman:
Bayesian Reconstruction of 3D Human Motion from Single-Camera Video. 820-826
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Aapo Hyvärinen, Patrik O. Hoyer:
Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. 827-833
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Tai Sing Lee, Stella X. Yu:
An Information-Theoretic Framework for Understanding Saccadic Eye Movements. 834-840
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Bruno A. Olshausen, K. Jarrod Millman:
Learning Sparse Codes with a Mixture-of-Gaussians Prior. 841-847
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Clay Spence, Lucas C. Parra:
Hierarchical Image Probability (H1P) Models. 848-854
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Martin J. Wainwright, Eero P. Simoncelli:
Scale Mixtures of Gaussians and the Statistics of Natural Images. 855-861
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Ming-Hsuan Yang, Dan Roth, Narendra Ahuja:
A SNoW-Based Face Detector. 862-868
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Zhiyong Yang, Richard S. Zemel:
Managing Uncertainty in Cue Combination. 869-878
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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
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Marian Stewart Bartlett, Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman, Terrence J. Sejnowski:
Image Representations for Facial Expression Coding. 886-892
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Timothy X. Brown:
Low Power Wireless Communication via Reinforcement Learning. 893-899
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John W. Fisher III, Alexander T. Ihler, Paul A. Viola:
Learning Informative Statistics: A Nonparametnic Approach. 900-906
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Richard M. Golden:
Kirchoff Law Markov Fields for Analog Circuit Design. 907-913
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Thomas Hofmann:
Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization. 914-920
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Yuansong Liao, John E. Moody:
Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting. 921-927
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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
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Michael Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky:
Churn Reduction in the Wireless Industry. 935-941
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Lucas C. Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus-Robert Müller:
Unmixing Hyperspectral Data. 942-948
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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
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Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker:
Reinforcement Learning for Spoken Dialogue Systems. 956-962
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Xubo B. Song, Joseph Sill, Yaser S. Abu-Mostafa, Harvey Kasdan:
Image Recognition in Context: Application to Microscopic Urinalysis. 963-969
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Shivakumar Vaithyanathan, Byron Dom:
Generalized Model Selection for Unsupervised Learning in High Dimensions. 970-976
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Nuno Vasconcelos, Andrew Lippman:
Learning from User Feedback in Image Retrieval Systems. 977-986
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Control, Navigation and Planning

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