NIPS 1998: Denver, CO, USA

Cognitive Science

• Nikhil Bhushan, Reza Shadmehr:
  Evidence for a Forward Dynamics Model in Human Adaptive Motor Control. 3-9
  
• Ke Chen, DeLiang L. Wang:
  Perceiving without Learning: From Spirals to Inside/Outside Relations. 10-16
  
• G. Bjorn Christianson, Suzanna Becker:
  A Model for Associative Multiplication. 17-23
  
• Matthew N. Dailey, Garrison W. Cottrell, Thomas A. Busey:
  Facial Memory Is Kernel Density Estimation (Almost). 24-30
  
• Masahiko Haruno, Daniel M. Wolpert, Mitsuo Kawato:
  Multiple Paired Forward-Inverse Models for Human Motor Learning and Control. 31-37
  
• Bradley C. Love:
  Utilizing lime: Asynchronous Binding. 38-44
  
• Zili Liu, Daphna Weinshall:
  Mechanisms of Generalization in Perceptual Learning. 45-51
  
• Michael Mozer:
  A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes. 52-58
  

Neuroscience

• Joshua B. Tenenbaum:
  Bayesian Modeling of Human Concept Learning. 59-68
  
• L. F. Abbott, Sen Song:
  Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability. 69-75
  
• Péter Adorján, Klaus Obermayer:
  Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability. 76-82
  
• Pierre Baraduc, Emmanuel Guigon, Yves Burnod:
  Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements? 83-89
  
• Frances S. Chance, Sacha B. Nelson, L. F. Abbott:
  Recurrent Cortical Amplification Produces Complex Cell Responses. 90-96
  
• Gal Chechik, Isaac Meilijson, Eytan Ruppin:
  Neuronal Regulation Implements Efficient Synaptic Pruning. 97-103
  
• Sophie Denève, Alexandre Pouget, Peter E. Latham:
  Divisive Normalization, Line Attractor Networks and Ideal Observers. 104-110
  
• Itay Gat, Naftali Tishby:
  Synergy and Redundancy among Brain Cells of Behaving Monkeys. 111-117
  
• Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, Terrence J. Sejnowski:
  Analyzing and Visualizing Single-Trial Event-Related Potentials. 118-124
  
• Richard Kempter, Wulfram Gerstner, J. Leo van Hemmen:
  Spike-Based Compared to Rate-Based Hebbian Learning. 125-131
  
• Amit Manwani, Christof Koch:
  Signal Detection in Noisy Weakly-Active Dendrites. 132-138
  
• Christian Piepenbrock, Klaus Obermayer:
  The Role of Lateral Cortical Competition in Ocular Dominance Development. 139-145
  
• Dmitry Rinberg, Hanan Davidowitz, Naftali Tishby:
  Multi-Electrode Spike Sorting by Clustering Transfer Functions. 146-152
  
• Eero P. Simoncelli, Odelia Schwartz:
  Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model. 153-159
  
• Martin Stemmler, Christof Koch:
  Information Maximization in Single Neurons. 160-166
  
• Hyoungsoo Yoon, Haim Sompolinsky:
  The Effect of Correlations on the Fisher Information of Population Codes. 167-173
  

Theory

• Richard S. Zemel, Peter Dayan:
  Distributional Population Codes and Multiple Motion Models. 174-182
  
• David Barber, Wim Wiegerinck:
  Tractable Variational Structures for Approximating Graphical Models. 183-189
  
• Peter L. Bartlett, Vitaly Maiorov, Ron Meir:
  Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. 190-196
  
• Anthony C. C. Coolen, David Saad:
  Dynamics of Supervised Learning with Restricted Training Sets. 197-203
  
• Nello Cristianini, Colin Campbell, John Shawe-Taylor:
  Dynamically Adapting Kernels in Support Vector Machines. 204-210
  
• A. Düring, Anthony C. C. Coolen, D. Sherrington:
  Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks. 211-217
  
• Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper:
  Finite-Dimensional Approximation of Gaussian Processes. 218-224
  
• Claudio Gentile, Manfred K. Warmuth:
  Linear Hinge Loss and Average Margin. 225-231
  
• Didier Herschkowitz, Jean-Pierre Nadal:
  Unsupervised and Supervised Clustering: The Mutual Information between Parameters and Observations. 232-238
  
• Shiro Ikeda, Shun-ichi Amari, Hiroyuki Nakahara:
  Convergence of the Wake-Sleep Algorithm. 239-245
  
• Yoshiyuki Kabashima, David Saad:
  The Belief in TAP. 246-252
  
• Grigoris I. Karakoulas, John Shawe-Taylor:
  Optimizing Classifers for Imbalanced Training Sets. 253-259
  
• Michael J. Kearns, Lawrence K. Saul:
  Inference in Multilayer Networks via Large Deviation Bounds. 260-266
  
• Friedrich Leisch, Adrian Trapletti, Kurt Hornik:
  Stationarity and Stability of Autoregressive Neural Network Processes. 267-273
  
• Zhaoping Li, Peter Dayan:
  Computational Differences between Asymmetrical and Symmetrical Networks. 274-280
  
• Wolfgang Maass, Eduardo D. Sontag:
  A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions. 281-287
  
• Llew Mason, Peter L. Bartlett, Jonathan Baxter:
  Direct Optimization of Margins Improves Generalization in Combined Classifiers. 288-294
  
• Ron Meir, Vitaly Maiorov:
  On the Optimality of Incremental Neural Network Algorithms. 295-301
  
• Manfred Opper, Francesco Vivarelli:
  General Bounds on Bayes Errors for Regression with Gaussian Processes. 302-308
  
• Manfred Opper, Ole Winther:
  Mean Field Methods for Classification with Gaussian Processes. 309-315
  
• H. C. Rae, Peter Sollich, Anthony C. C. Coolen:
  On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories. 316-322
  
• Akito Sakurai:
  Tight Bounds for the VC-Dimension of Piecewise Polynomial Networks. 323-329
  
• Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson:
  Shrinking the Tube: A New Support Vector Regression Algorithm. 330-336
  
• N. S. Skantzos, C. F. Beckmann, Anthony C. C. Coolen:
  Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent Networks. 337-345
  
• Peter Sollich:
  Learning Curves for Gaussian Processes. 344-350
  

Algorithms and Architecture

• Toshiyuki Tanaka:
  A Theory of Mean Field Approximation. 351-360
  
• Hagai Attias:
  Learning a Hierarchical Belief Network of Independent Factor Analyzers. 361-367
  
• Kristin P. Bennett, Ayhan Demiriz:
  Semi-Supervised Support Vector Machines. 368-374
  
• Mauro Birattari, Gianluca Bontempi, Hugues Bersini:
  Lazy Learning Meets the Recursive Least Squares Algorithm. 375-381
  
• Christopher M. Bishop:
  Bayesian PCA. 382-388
  
• Andrew Blake, Ben North, Michael Isard:
  Learning Multi-Class Dynamics. 389-395
  
• Xavier Boyen, Daphne Koller:
  Approximate Learning of Dynamic Models. 396-402
  
• Thomas Briegel, Volker Tresp:
  Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models. 403-409
  
• João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee:
  Global Optimisation of Neural Network Models via Sequential Sampling. 410-416
  
• Nir Friedman, Yoram Singer:
  Efficient Bayesian Parameter Estimation in Large Discrete Domains. 417-423
  
• Yoram Gdalyahu, Daphna Weinshall, Michael Werman:
  A Randomized Algorithm for Pairwise Clustering. 424-430
  
• Zoubin Ghahramani, Sam T. Roweis:
  Learning Nonlinear Dynamical Systems Using an EM Algorithm. 431-437
  
• Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer:
  Classification on Pairwise Proximity Data. 438-444
  
• Yves Grandvalet, Stéphane Canu:
  Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage. 445-451
  
• Marcus Held, Jan Puzicha, Joachim M. Buhmann:
  Visualizing Group Structure. 452-458
  
• Sepp Hochreiter, Jürgen Schmidhuber:
  Source Separation as a By-Product of Regularization. 459-465
  
• Thomas Hofmann, Jan Puzicha, Michael I. Jordan:
  Learning from Dyadic Data. 466-472
  
• Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja:
  Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation. 473-479
  
• Charles Lee Isbell Jr., Paul A. Viola:
  Restructuring Sparse High Dimensional Data for Effective Retrieval. 480-486
  
• Tommi Jaakkola, David Haussler:
  Exploiting Generative Models in Discriminative Classifiers. 487-493
  
• Tony Jebara, Alex Pentland:
  Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm. 494-500
  
• Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger:
  A Polygonal Line Algorithm for Constructing Principal Curves. 501-507
  
• Te-Won Lee, Michael S. Lewicki, Terrence J. Sejnowski:
  Unsupervised Classification with Non-Gaussian Mixture Models Using ICA. 508-514
  
• Daniel D. Lee, Haim Sompolinsky:
  Learning a Continuous Hidden Variable Model for Binary Data. 515-521
  
• Malik Magdon-Ismail, Amir F. Atiya:
  Neural Networks for Density Estimation. 522-528
  
• Alan D. Marrs, Andrew R. Webb:
  Exploratory Data Analysis Using Radial Basis Function Latent Variable Models. 529-535
  
• Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch:
  Kernel PCA and De-Noising in Feature Spaces. 536-542
  
• Andrew W. Moore:
  Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees. 543-549
  
• Marcello Pelillo:
  Replicator Equations, Maximal Cliques, and Graph Isomorphism. 550-556
  
• John C. Platt:
  Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. 557-563
  
• Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller:
  Regularizing AdaBoost. 564-570
  
• Patrice Simard, Léon Bottou, Patrick Haffner, Yann LeCun:
  Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks. 571-577
  
• Yoram Singer, Manfred K. Warmuth:
  Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. 578-584
  
• Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf:
  Semiparametric Support Vector and Linear Programming Machines. 585-591
  
• Michael E. Tipping:
  Probabilistic Visualisation of High-Dimensional Binary Data. 592-598
  
• Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
  SMEM Algorithm for Mixture Models. 599-605
  
• Nuno Vasconcelos, Andrew Lippman:
  Learning Mixture Hierarchies. 606-612
  
• Francesco Vivarelli, Christopher K. I. Williams:
  Discovering Hidden Features with Gaussian Processes Regression. 613-619
  
• Grace Wahba, Xiwu Lin, Fangyu Gao, Dong Xiang, Ronald Klein, Barbara Klein:
  The Bias-Variance Tradeoff and the Randomized GACV. 620-626
  
• Kevin R. Wheeler, Atam P. Dhawan:
  Basis Selection for Wavelet Regression. 627-633
  
• Christopher K. I. Williams, Nicholas J. Adams:
  DTs: Dynamic Trees. 634-640
  
• Alan L. Yuille, James M. Coughlan:
  Convergence Rates of Algorithms for Visual Search: Detecting Visual Contours. 641-647
  

Implementation

• Liqing Zhang, Andrzej Cichocki:
  Blind Separation of Filtered Sources Using State-Space Approach. 648-656
  
• Gert Cauwenberghs, James Waskiewicz:
  Analog VLSI Cellular Implementation of the Boundary Contour System. 657-663
  
• Jung-Wook Cho, Soo-Young Lee:
  Active Noise Canceling Using Analog Neuro-Chip with On-Chip Learning Capability. 664-670
  
• Richard Coggins, Raymond J. Wang, Marwan A. Jabri:
  A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector Quantiser. 671-677
  
• R. Timothy Edwards, Gert Cauwenberghs, Fernando J. Pineda:
  Optimizing Correlation Algorithms for Hardware-Based Transient Classification. 678-684
  
• Ralph Etienne-Cummings, Viktor Gruev, Mohammed Abdel Ghani:
  VLSI Implementation of Motion Centroid Localization for Autonomous Navigation. 685-691
  
• John G. Harris, Chiang-Jung Pu, José Carlos Príncipe:
  A Neuromorphic Monaural Sound Localizer. 692-698
  
• Charles M. Higgins, Christof Koch:
  An Integrated Vision Sensor for the Computation of Optical Flow Singular Points. 699-705
  
• Alan Stocker, Rodney J. Douglas:
  Computation of Smooth Optical Flow in a Feedback Connected Analog Network. 706-712
  

Speech, Handwriting and Signal Processing

• Ping Zhou, Jim Austin, John Kennedy:
  A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory. 713-722
  
• Matthew Brand:
  An Entropic Estimator for Structure Discovery. 723-729
  
• Michael S. Lewicki, Terrence J. Sejnowski:
  Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations. 730-736
  
• Christoph Neukirchen, Gerhard Rigoll:
  Controlling the Complexity of HMM Systems by Regularization. 737-743
  
• David A. Nix, John E. Hogden:
  Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs. 744-750
  

Visual Processing

• Lawrence K. Saul, Mazin G. Rahim:
  Markov Processes on Curves for Automatic Speech Recognition. 751-760
  
• James M. Coughlan, Alan L. Yuille:
  A Phase Space Approach to Minimax Entropy Learning and the Minutemax Approximations. 761-767
  
• Trevor Darrell:
  Example-Based Image Synthesis of Articulated Figures. 768-774
  
• William T. Freeman, Egon C. Pasztor:
  Learning to Estimate Scenes from Images. 775-781
  
• Sergey Ioffe, David A. Forsyth:
  Learning to Find Pictures of People. 782-788
  
• Laurent Itti, Jochen Braun, Dale K. Lee, Christof Koch:
  Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative Model. 789-795
  
• Zhaoping Li:
  A V1 Model of Pop Out and Asymmetty in Visual Search. 796-802
  
• P. Jonathon Phillips:
  Support Vector Machines Applied to Face Recognition. 803-809
  
• Rajesh P. N. Rao, Daniel L. Ruderman:
  Learning Lie Groups for Invariant Visual Perception. 810-816
  
• Ruth Rosenholtz:
  General-Purpose Localization of Textured Image Regions. 817-823
  
• Ravi K. Sharma, Todd K. Leen, Misha Pavel:
  Probabilistic Image Sensor Fusion. 824-830
  
• Karvel K. Thornber, Lance R. Williams:
  Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape. 831-837
  

Applications

• Daphna Weinshall, David W. Jacobs, Yoram Gdalyahu:
  Classification in Non-Metric Spaces. 838-846
  
• Shumeet Baluja:
  Making Templates Rotationally Invariant. An Application to Rotated Digit Recognition. 847-853
  
• Shumeet Baluja:
  Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data. 854-860
  
• Dan Cornford, Ian T. Nabney, Christopher K. I. Williams:
  Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. 861-867
  
• Gideon Dror, Halina Abramowicz, David Horn:
  Vertex Identification in High Energy Physics Experiments. 868-874
  
• Eric Granger, Stephen Grossberg, Mark A. Rubin, William W. Streilein:
  Familiarity Discrimination of Radar Pulses. 875-881
  
• Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton:
  Fast Neural Network Emulation of Dynamical Systems for Computer Animation. 882-888
  
• Jaakko Hollmén, Volker Tresp:
  Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model. 889-895
  
• Benoit Huet, Andrew D. J. Cross, Edwin R. Hancock:
  Graph Matching for Shape Retrieval. 896-902
  
• Amy McGovern, J. Eliot B. Moss:
  Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts. 903-909
  
• Baback Moghaddam, Tony Jebara, Alex Pentland:
  Bayesian Modeling of Facial Similarity. 910-916
  
• John E. Moody, Matthew Saffell:
  Reinforcement Learning for Trading. 917-923
  
• Nuria Oliver, Barbara Rosario, Alex Pentland:
  Graphical Models for Recognizing Human Interactions. 924-930
  
• Klaus Prank, Julia Börger, Alexander von zur Mühlen, Georg Brabant, Christof Schöfl:
  Independent Component Analysis of Intracellular Calcium Spike Data. 931-937
  
• Clay Spence, Paul Sajda:
  Applications of Multi-Resolution Neural Networks to Mammography. 938-944
  
• Matthew M. Williamson, Roderick Murray-Smith, Volker Hansen:
  Robot Docking Using Mixtures of Gaussians. 945-951
  

Control, Navigation and Planning

• David Wolpert, Kagan Tumer, Jeremy Frank:
  Using Collective Intelligence to Route Internet Traffic. 952-960
  
• Mohammad A. Al-Ansari, Ronald J. Williams:
  Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm. 961-967
  
• Leemon C. Baird III, Andrew W. Moore:
  Gradient Descent for General Reinforcement Learning. 968-974
  
• Lyndon J. Brown, Gregory E. Gonye, James S. Schwaber:
  Non-Linear PI Control Inspired by Biological Control Systems. 975-981
  
• Timothy X. Brown, Hui Tong, Satinder P. Singh:
  Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning. 982-988
  
• Akira Hayashi, Nobuo Suematsu:
  Viewing Classifier Systems as Model Free Learning in POMDPs. 989-995
  
• Michael J. Kearns, Satinder P. Singh:
  Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. 996-1002
  
• Sven Koenig:
  Exploring Unknown Environments with Real-Time Search or Reinforcement Learning. 1003-1009
  
• John Loch:
  The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision Processes. 1010-1016
  
• Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton:
  Learning Instance-Independent Value Functions to Enhance Local Search. 1017-1023
  
• Rémi Munos, Andrew W. Moore:
  Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. 1024-1030
  
• Ralph Neuneier, Oliver Mihatsch:
  Risk Sensitive Reinforcement Learning. 1031-1037
  
• Eimei Oyama, Susumu Tachi:
  Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm. 1038-1044
  
• Jette Randlov:
  Learning Macro-Actions in Reinforcement Learning. 1045-1051
  
• Masa-aki Sato, Shin Ishii:
  Reinforcement Learning Based on On-Line EM Algorithm. 1052-1058
  
• Nobuo Suematsu, Akira Hayashi:
  A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory. 1059-1065
  
• Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran:
  Improved Switching among Temporally Abstract Actions. 1066-1072
  
• John K. Williams, Satinder P. Singh:
  Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes. 1073-1080
  

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