NIPS Proceedings^β

Advances in Neural Information Processing Systems 11 (NIPS 1998)

The papers below appear in Advances in Neural Information Processing Systems 11 edited by M.J. Kearns and S.A. Solla and D.A. Cohn.
They are proceedings from the conference, "Neural Information Processing Systems 1998."

• Evidence for a Forward Dynamics Model in Human Adaptive Motor Control Nikhil Bhushan, Reza Shadmehr
• Perceiving without Learning: From Spirals to Inside/Outside Relations Ke Chen, DeLiang L. Wang
• A Model for Associative Multiplication G. Bjorn Christianson, Suzanna Becker
• Facial Memory Is Kernel Density Estimation (Almost) Matthew N. Dailey, Garrison W. Cottrell, Thomas A. Busey
• Multiple Paired Forward-Inverse Models for Human Motor Learning and Control Masahiko Haruno, Daniel M. Wolpert, Mitsuo Kawato
• Utilizing lime: Asynchronous Binding Bradley C. Love
• Mechanisms of Generalization in Perceptual Learning Zili Liu, Daphna Weinshall
• A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes Michael C. Mozer
• Bayesian Modeling of Human Concept Learning Joshua B. Tenenbaum
• Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability L. F. Abbott, Sen Song
• Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability Péter Adorján, Klaus Obermayer
• Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements? Pierre Baraduc, Emmanuel Guigon, Yves Burnod
• Recurrent Cortical Amplification Produces Complex Cell Responses Frances S. Chance, Sacha B. Nelson, L. F. Abbott
• Neuronal Regulation Implements Efficient Synaptic Pruning Gal Chechik, Isaac Meilijson, Eytan Ruppin
• Divisive Normalization, Line Attractor Networks and Ideal Observers Sophie Denève, Alexandre Pouget, Peter E. Latham
• Synergy and Redundancy among Brain Cells of Behaving Monkeys Itay Gat, Naftali Tishby
• Analyzing and Visualizing Single-Trial Event-Related Potentials Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, Terrence J. Sejnowski
• Spike-Based Compared to Rate-Based Hebbian Learning Richard Kempter, Wulfram Gerstner, J. Leo van Hemmen
• Signal Detection in Noisy Weakly-Active Dendrites Amit Manwani, Christof Koch
• The Role of Lateral Cortical Competition in Ocular Dominance Development Christian Piepenbrock, Klaus Obermayer
• Multi-Electrode Spike Sorting by Clustering Transfer Functions Dmitry Rinberg, Hanan Davidowitz, Naftali Tishby
• Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model Eero P. Simoncelli, Odelia Schwartz
• Information Maximization in Single Neurons Martin Stemmler, Christof Koch
• The Effect of Correlations on the Fisher Information of Population Codes Hyoungsoo Yoon, Haim Sompolinsky
• Distributional Population Codes and Multiple Motion Models Richard S. Zemel, Peter Dayan
• Tractable Variational Structures for Approximating Graphical Models David Barber, Wim Wiegerinck
• Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks Peter L. Bartlett, Vitaly Maiorov, Ron Meir
• Dynamics of Supervised Learning with Restricted Training Sets Anthony C. C. Coolen, David Saad
• Dynamically Adapting Kernels in Support Vector Machines Nello Cristianini, Colin Campbell, John Shawe-Taylor
• Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks A. Düring, Anthony C. C. Coolen, D. Sherrington
• Finite-Dimensional Approximation of Gaussian Processes Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper
• Linear Hinge Loss and Average Margin Claudio Gentile, Manfred K. Warmuth
• Unsupervised and Supervised Clustering: The Mutual Information between Parameters and Observations Didier Herschkowitz, Jean-Pierre Nadal
• Convergence of the Wake-Sleep Algorithm Shiro Ikeda, Shun-ichi Amari, Hiroyuki Nakahara
• The Belief in TAP Yoshiyuki Kabashima, David Saad
• Optimizing Classifers for Imbalanced Training Sets Grigoris I. Karakoulas, John Shawe-Taylor
• Inference in Multilayer Networks via Large Deviation Bounds Michael J. Kearns, Lawrence K. Saul
• Stationarity and Stability of Autoregressive Neural Network Processes Friedrich Leisch, Adrian Trapletti, Kurt Hornik
• Computational Differences between Asymmetrical and Symmetrical Networks Zhaoping Li, Peter Dayan
• A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions Wolfgang Maass, Eduardo D. Sontag
• Direct Optimization of Margins Improves Generalization in Combined Classifiers Llew Mason, Peter L. Bartlett, Jonathan Baxter
• On the Optimality of Incremental Neural Network Algorithms Ron Meir, Vitaly Maiorov
• General Bounds on Bayes Errors for Regression with Gaussian Processes Manfred Opper, Francesco Vivarelli
• Mean Field Methods for Classification with Gaussian Processes Manfred Opper, Ole Winther
• On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories H. C. Rae, Peter Sollich, Anthony C. C. Coolen
• Tight Bounds for the VC-Dimension of Piecewise Polynomial Networks Akito Sakurai
• Shrinking the Tube: A New Support Vector Regression Algorithm Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson
• Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent Networks N. S. Skantzos, C. F. Beckmann, Anthony C. C. Coolen
• Learning Curves for Gaussian Processes Peter Sollich
• A Theory of Mean Field Approximation Toshiyuki Tanaka
• Learning a Hierarchical Belief Network of Independent Factor Analyzers Hagai Attias
• Semi-Supervised Support Vector Machines Kristin P. Bennett, Ayhan Demiriz
• Lazy Learning Meets the Recursive Least Squares Algorithm Mauro Birattari, Gianluca Bontempi, Hugues Bersini
• Bayesian PCA Christopher M. Bishop
• Learning Multi-Class Dynamics Andrew Blake, Ben North, Michael Isard
• Approximate Learning of Dynamic Models Xavier Boyen, Daphne Koller
• Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models Thomas Briegel, Volker Tresp
• Global Optimisation of Neural Network Models via Sequential Sampling João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee
• Efficient Bayesian Parameter Estimation in Large Discrete Domains Nir Friedman, Yoram Singer
• A Randomized Algorithm for Pairwise Clustering Yoram Gdalyahu, Daphna Weinshall, Michael Werman
• Learning Nonlinear Dynamical Systems Using an EM Algorithm Zoubin Ghahramani, Sam T. Roweis
• Classification on Pairwise Proximity Data Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer
• Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage Yves Grandvalet, Stéphane Canu
• Visualizing Group Structure Marcus Held, Jan Puzicha, Joachim M. Buhmann
• Source Separation as a By-Product of Regularization Sepp Hochreiter, Juergen Schmidhuber
• Learning from Dyadic Data Thomas Hofmann, Jan Puzicha, Michael I. Jordan
• Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja
• Restructuring Sparse High Dimensional Data for Effective Retrieval Charles Lee Isbell Jr., Paul A. Viola
• Exploiting Generative Models in Discriminative Classifiers Tommi Jaakkola, David Haussler
• Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm Tony Jebara, Alex Pentland
• A Polygonal Line Algorithm for Constructing Principal Curves Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger
• Unsupervised Classification with Non-Gaussian Mixture Models Using ICA Te-Won Lee, Michael S. Lewicki, Terrence J. Sejnowski
• Learning a Continuous Hidden Variable Model for Binary Data Daniel D. Lee, Haim Sompolinsky
• Neural Networks for Density Estimation Malik Magdon-Ismail, Amir F. Atiya
• Exploratory Data Analysis Using Radial Basis Function Latent Variable Models Alan D. Marrs, Andrew R. Webb
• Kernel PCA and De-Noising in Feature Spaces Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch
• Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees Andrew W. Moore
• Replicator Equations, Maximal Cliques, and Graph Isomorphism Marcello Pelillo
• Using Analytic QP and Sparseness to Speed Training of Support Vector Machines John C. Platt
• Regularizing AdaBoost Gunnar Rätsch, Takashi Onoda, Klaus R. Müller
• Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks Patrice Simard, Léon Bottou, Patrick Haffner, Yann LeCun
• Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy Yoram Singer, Manfred K. Warmuth
• Semiparametric Support Vector and Linear Programming Machines Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf
• Probabilistic Visualisation of High-Dimensional Binary Data Michael E. Tipping
• SMEM Algorithm for Mixture Models Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton
• Learning Mixture Hierarchies Nuno Vasconcelos, Andrew Lippman
• Discovering Hidden Features with Gaussian Processes Regression Francesco Vivarelli, Christopher K. I. Williams
• The Bias-Variance Tradeoff and the Randomized GACV Grace Wahba, Xiwu Lin, Fangyu Gao, Dong Xiang, Ronald Klein, Barbara Klein
• Basis Selection for Wavelet Regression Kevin R. Wheeler, Atam P. Dhawan
• DTs: Dynamic Trees Christopher K. I. Williams, Nicholas J. Adams
• Convergence Rates of Algorithms for Visual Search: Detecting Visual Contours Alan L. Yuille, James M. Coughlan
• Blind Separation of Filtered Sources Using State-Space Approach Liqing Zhang, Andrzej Cichocki
• Analog VLSI Cellular Implementation of the Boundary Contour System Gert Cauwenberghs, James Waskiewicz
• Active Noise Canceling Using Analog Neuro-Chip with On-Chip Learning Capability Jung-Wook Cho, Soo-Young Lee
• A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector Quantiser Richard Coggins, Raymond J. Wang, Marwan A. Jabri
• Optimizing Correlation Algorithms for Hardware-Based Transient Classification R. Timothy Edwards, Gert Cauwenberghs, Fernando J. Pineda
• VLSI Implementation of Motion Centroid Localization for Autonomous Navigation Ralph Etienne-Cummings, Viktor Gruev, Mohammed Abdel Ghani
• A Neuromorphic Monaural Sound Localizer John G. Harris, Chiang-Jung Pu, José Carlos Príncipe
• An Integrated Vision Sensor for the Computation of Optical Flow Singular Points Charles M. Higgins, Christof Koch
• Computation of Smooth Optical Flow in a Feedback Connected Analog Network Alan Stocker, Rodney J. Douglas
• A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory Ping Zhou, Jim Austin, John Kennedy
• An Entropic Estimator for Structure Discovery Matthew Brand
• Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations Michael S. Lewicki, Terrence J. Sejnowski
• Controlling the Complexity of HMM Systems by Regularization Christoph Neukirchen, Gerhard Rigoll
• Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs David A. Nix, John E. Hogden
• Markov Processes on Curves for Automatic Speech Recognition Lawrence K. Saul, Mazin G. Rahim
• A Phase Space Approach to Minimax Entropy Learning and the Minutemax Approximations James M. Coughlan, Alan L. Yuille
• Example-Based Image Synthesis of Articulated Figures Trevor Darrell
• Learning to Estimate Scenes from Images William T. Freeman, Egon C. Pasztor
• Learning to Find Pictures of People Sergey Ioffe, David A. Forsyth
• Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative Model Laurent Itti, Jochen Braun, Dale K. Lee, Christof Koch
• A V1 Model of Pop Out and Asymmetty in Visual Search Zhaoping Li
• Support Vector Machines Applied to Face Recognition P. Jonathon Phillips
• Learning Lie Groups for Invariant Visual Perception Rajesh P. N. Rao, Daniel L. Ruderman
• General-Purpose Localization of Textured Image Regions Ruth Rosenholtz
• Probabilistic Image Sensor Fusion Ravi K. Sharma, Todd K. Leen, Misha Pavel
• Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape Karvel K. Thornber, Lance R. Williams
• Classification in Non-Metric Spaces Daphna Weinshall, David W. Jacobs, Yoram Gdalyahu
• Making Templates Rotationally Invariant. An Application to Rotated Digit Recognition Shumeet Baluja
• Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data Shumeet Baluja
• Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields Dan Cornford, Ian T. Nabney, Christopher K. I. Williams
• Vertex Identification in High Energy Physics Experiments Gideon Dror, Halina Abramowicz, David Horn
• Familiarity Discrimination of Radar Pulses Eric Granger, Stephen Grossberg, Mark A. Rubin, William W. Streilein
• Fast Neural Network Emulation of Dynamical Systems for Computer Animation Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton
• Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model Jaakko Hollmén, Volker Tresp
• Graph Matching for Shape Retrieval Benoit Huet, Andrew D. J. Cross, Edwin R. Hancock
• Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts Amy McGovern, J. Eliot B. Moss
• Bayesian Modeling of Facial Similarity Baback Moghaddam, Tony Jebara, Alex Pentland
• Reinforcement Learning for Trading John E. Moody, Matthew Saffell
• Graphical Models for Recognizing Human Interactions Nuria Oliver, Barbara Rosario, Alex Pentland
• Independent Component Analysis of Intracellular Calcium Spike Data Klaus Prank, Julia Börger, Alexander von zur Mühlen, Georg Brabant, Christof Schöfl
• Applications of Multi-Resolution Neural Networks to Mammography Clay Spence, Paul Sajda
• Robot Docking Using Mixtures of Gaussians Matthew M. Williamson, Roderick Murray-Smith, Volker Hansen
• Using Collective Intelligence to Route Internet Traffic David Wolpert, Kagan Tumer, Jeremy Frank
• Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm Mohammad A. Al-Ansari, Ronald J. Williams
• Gradient Descent for General Reinforcement Learning Leemon C. Baird III, Andrew W. Moore
• Non-Linear PI Control Inspired by Biological Control Systems Lyndon J. Brown, Gregory E. Gonye, James S. Schwaber
• Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning Timothy X. Brown, Hui Tong, Satinder P. Singh
• Viewing Classifier Systems as Model Free Learning in POMDPs Akira Hayashi, Nobuo Suematsu
• Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms Michael J. Kearns, Satinder P. Singh
• Exploring Unknown Environments with Real-Time Search or Reinforcement Learning Sven Koenig
• The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision Processes John Loch
• Learning Instance-Independent Value Functions to Enhance Local Search Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton
• Barycentric Interpolators for Continuous Space and Time Reinforcement Learning Rémi Munos, Andrew W. Moore
• Risk Sensitive Reinforcement Learning Ralph Neuneier, Oliver Mihatsch
• Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm Eimei Oyama, Susumu Tachi
• Learning Macro-Actions in Reinforcement Learning Jette Randlov
• Reinforcement Learning Based on On-Line EM Algorithm Masa-aki Sato, Shin Ishii
• A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory Nobuo Suematsu, Akira Hayashi
• Improved Switching among Temporally Abstract Actions Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran
• Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes John K. Williams, Satinder P. Singh