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
Cognitive Science
- Thea B. Ghiselli-Crippa, Paul W. Munro: Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information. 17-23
- Soo-Young Lee, Michael Mozer: Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. 31-37
- 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
- Ton Weijters, Antal van den Bosch, Eric O. Postma: Learning Statistically Neutral Tasks without Expert Guidance. 73-79
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: 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
- 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
- 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
- Anthony C. C. Coolen, C. W. H. Mace: Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers. 237-243
- Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther: Efficient Approaches to Gaussian Process Classification. 251-257
- 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
- Wolfgang Maass: Neural Computation with Winner-Take-All as the Only Nonlinear Operation. 293-299
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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, 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
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
- Martin J. Wainwright, Eero P. Simoncelli: Scale Mixtures of Gaussians and the Statistics of Natural Images. 855-861
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
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
- Kevin P. Murphy: Bayesian Map Learning in Dynamic Environments. 1015-1021
- 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
- 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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last updated on 2013-06-18T211851 by the dblp team