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NIPS 1998: Denver, CO, USA
- Michael J. Kearns, Sara A. Solla, David A. Cohn:
Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30 - December 5, 1998]. The MIT Press 1999, ISBN 0-262-11245-0
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, Alexander J. Smola, Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm. 330-336 - N. S. Skantzos, Christian 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 S. 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, Alexander 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 Y. 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 - Alexander 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 E. 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 H. 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 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 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 Randløv:
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 Singh, Doina Precup, Balaraman Ravindran:
Improved Switching among Temporally Abstract Actions. 1066-1072 - John K. Williams, Satinder Singh:
Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes. 1073-1080
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