Advances in Neural Information Processing Systems 9 (NIPS 1996)

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• Hebb Learning of Features based on their Information Content Ferdinand Peper, Hideki Noda
• A Variational Principle for Model-based Morphing Lawrence Saul, Michael Jordan
• ARTEX: A Self-organizing Architecture for Classifying Image Regions Stephen Grossberg, James Williamson
• The CONDENSATION Algorithm - Conditional Density Propagation and Applications to Visual Tracking Andrew Blake, Michael Isard
• A Silicon Model of Amplitude Modulation Detection in the Auditory Brainstem André van Schaik, Eric Fragnière, Eric Vittoz
• Adaptive On-line Learning in Changing Environments Noboru Murata, Klaus-Robert Müller, Andreas Ziehe, Shun-ichi Amari
• Minimizing Statistical Bias with Queries David Cohn
• A Micropower Analog VLSI HMM State Decoder for Wordspotting John Lazzaro, John Wawrzynek, Richard P. Lippmann
• Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation Eric Wan, Alex Nelson
• Source Separation and Density Estimation by Faithful Equivariant SOM Juan Lin, Jack Cowan, David Grier
• Continuous Sigmoidal Belief Networks Trained using Slice Sampling Brendan J. Frey
• Compositionality, MDL Priors, and Object Recognition Elie Bienenstock, Stuart Geman, Daniel Potter
• A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks Manfred Opper, Ole Winther
• Ordered Classes and Incomplete Examples in Classification Mark Mathieson
• Unification of Information Maximization and Minimization Ryotaro Kamimura
• Multi-Task Learning for Stock Selection Joumana Ghosn, Yoshua Bengio
• Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input Akaysha Tang, Andreas Bartels, Terrence J. Sejnowski
• Analytical Mean Squared Error Curves in Temporal Difference Learning Satinder Singh, Peter Dayan
• Dynamics of Training Siegfried Bös, Manfred Opper
• A Constructive RBF Network for Writer Adaptation John Platt, Nada Matic
• MLP Can Provably Generalize Much Better than VC-bounds Indicate Adam Kowalczyk, Herman Ferrá
• 3D Object Recognition: A Model of View-Tuned Neurons Emanuela Bricolo, Tomaso Poggio, Nikos K. Logothetis
• Learning Bayesian Belief Networks with Neural Network Estimators Stefano Monti, Gregory Cooper
• Approximate Solutions to Optimal Stopping Problems John Tsitsiklis, Benjamin Van Roy
• An Orientation Selective Neural Network for Pattern Identification in Particle Detectors Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar-Pikielny
• Early Brain Damage Volker Tresp, Ralph Neuneier, Hans-Georg Zimmermann
• Text-Based Information Retrieval Using Exponentiated Gradient Descent Ron Papka, James Callan, Andrew Barto
• An Analog Implementation of the Constant Average Statistics Constraint For Sensor Calibration John Harris, Yu-Ming Chiang
• A Neural Model of Visual Contour Integration Zhaoping Li
• Unsupervised Learning by Convex and Conic Coding Daniel Lee, H. Sebastian Seung
• Reconstructing Stimulus Velocity from Neuronal Responses in Area MT Wyeth Bair, James Cavanaugh, J. Movshon
• Multidimensional Triangulation and Interpolation for Reinforcement Learning Scott Davies
• Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks Marian Bartlett, Terrence J. Sejnowski
• On the Effect of Analog Noise in Discrete-Time Analog Computations Wolfgang Maass, Pekka Orponen
• Combinations of Weak Classifiers Chuanyi Ji, Sheng Ma
• Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems Satinder Singh, Dimitri Bertsekas
• Neural Learning in Structured Parameter Spaces - Natural Riemannian Gradient Shun-ichi Amari
• Extraction of Temporal Features in the Electrosensory System of Weakly Electric Fish Fabrizio Gabbiani, Walter Metzner, Ralf Wessel, Christof Koch
• A Model of Recurrent Interactions in Primary Visual Cortex Emanuel Todorov, Athanassios Siapas, David Somers
• On a Modification to the Mean Field EM Algorithm in Factorial Learning A. Dunmur, D. Titterington
• VLSI Implementation of Cortical Visual Motion Detection Using an Analog Neural Computer Ralph Etienne-Cummings, Jan Van der Spiegel, Naomi Takahashi, Alyssa Apsel, Paul Mueller
• Local Bandit Approximation for Optimal Learning Problems Michael Duff, Andrew Barto
• Learning Appearance Based Models: Mixtures of Second Moment Experts Christoph Bregler, Jitendra Malik
• Interpreting Images by Propagating Bayesian Beliefs Yair Weiss
• Why did TD-Gammon Work? Jordan Pollack, Alan Blair
• NeuroScale: Novel Topographic Feature Extraction using RBF Networks David Lowe, Michael Tipping
• Time Series Prediction using Mixtures of Experts Assaf Zeevi, Ron Meir, Robert Adler
• MIMIC: Finding Optima by Estimating Probability Densities Jeremy De Bonet, Charles Isbell, Paul Viola
• Neural Network Models of Chemotaxis in the Nematode Caenorhabditis Elegans Thomas Ferrée, Ben Marcotte, Shawn Lockery
• GTM: A Principled Alternative to the Self-Organizing Map Christopher Bishop, Markus Svensén, Christopher Williams
• Support Vector Method for Function Approximation, Regression Estimation and Signal Processing Vladimir Vapnik, Steven Golowich, Alex Smola
• Smoothing Regularizers for Projective Basis Function Networks John Moody, Thorsteinn Rögnvaldsson
• Bangs, Clicks, Snaps, Thuds and Whacks: An Architecture for Acoustic Transient Processing Fernando Pineda, Gert Cauwenberghs, R. Edwards
• Salient Contour Extraction by Temporal Binding in a Cortically-based Network Shih-Cheng Yen, Leif Finkel
• Learning Decision Theoretic Utilities through Reinforcement Learning Magnus Stensmo, Terrence J. Sejnowski
• Spatial Decorrelation in Orientation Tuned Cortical Cells Alexander Dimitrov, Jack Cowan
• Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches Mahesan Niranjan
• Are Hopfield Networks Faster than Conventional Computers? Ian Parberry, Hung-Li Tseng
• Learning from Demonstration Stefan Schaal
• Clustering Sequences with Hidden Markov Models Padhraic Smyth
• Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs Rich Caruana, Virginia de Sa
• Hidden Markov Decision Trees Michael Jordan, Zoubin Ghahramani, Lawrence Saul
• Representing Face Images for Emotion Classification Curtis Padgett, Garrison Cottrell
• Separating Style and Content Joshua Tenenbaum, William Freeman
• Contour Organisation with the EM Algorithm José Leite, Edwin Hancock
• Combining Neural Network Regression Estimates with Regularized Linear Weights Christopher Merz, Michael Pazzani
• Triangulation by Continuous Embedding Marina Meila, Michael Jordan
• Bayesian Model Comparison by Monte Carlo Chaining David Barber, Christopher Bishop
• Practical Confidence and Prediction Intervals Tom Heskes
• Consistent Classification, Firm and Soft Yoram Baram
• Neural Models for Part-Whole Hierarchies Maximilian Riesenhuber, Peter Dayan
• Bayesian Unsupervised Learning of Higher Order Structure Michael Lewicki, Terrence J. Sejnowski
• An Architectural Mechanism for Direction-tuned Cortical Simple Cells: The Role of Mutual Inhibition Silvio Sabatini, Fabio Solari, Giacomo Bisio
• Complex-Cell Responses Derived from Center-Surround Inputs: The Surprising Power of Intradendritic Computation Bartlett Mel, Daniel Ruderman, Kevin Archie
• Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions Yoram Singer, Manfred K. K. Warmuth
• A Constructive Learning Algorithm for Discriminant Tangent Models Diego Sona, Alessandro Sperduti, Antonina Starita
• Effective Training of a Neural Network Character Classifier for Word Recognition Larry Yaeger, Richard Lyon, Brandyn Webb
• Second-order Learning Algorithm with Squared Penalty Term Kazumi Saito, Ryohei Nakano
• Multi-effect Decompositions for Financial Data Modeling Lizhong Wu, John Moody
• Microscopic Equations in Rough Energy Landscape for Neural Networks K. Y. Michael Wong
• Dynamic Features for Visual Speechreading: A Systematic Comparison Michael Gray, Javier Movellan, Terrence J. Sejnowski
• The Effect of Correlated Input Data on the Dynamics of Learning Søren Halkjær, Ole Winther
• Learning Temporally Persistent Hierarchical Representations Suzanna Becker
• Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning Jeff Schneider
• Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks Kagan Tumer, Nirmala Ramanujam, Rebecca Richards-Kortum, Joydeep Ghosh
• Radial Basis Function Networks and Complexity Regularization in Function Learning Adam Krzyzak, Tamás Linder
• Adaptively Growing Hierarchical Mixtures of Experts Jürgen Fritsch, Michael Finke, Alex Waibel
• On-line Policy Improvement using Monte-Carlo Search Gerald Tesauro, Gregory Galperin
• Blind Separation of Delayed and Convolved Sources Te-Won Lee, Anthony Bell, Russell Lambert
• A New Approach to Hybrid HMM/ANN Speech Recognition using Mutual Information Neural Networks Gerhard Rigoll, Christoph Neukirchen
• Competition Among Networks Improves Committee Performance Paul Munro, Bambang Parmanto
• Selective Integration: A Model for Disparity Estimation Michael Gray, Alexandre Pouget, Richard Zemel, Steven Nowlan, Terrence J. Sejnowski
• The Neurothermostat: Predictive Optimal Control of Residential Heating Systems Michael C. Mozer, Lucky Vidmar, Robert Dodier
• Softening Discrete Relaxation Andrew Finch, Richard Wilson, Edwin Hancock
• A Comparison between Neural Networks and other Statistical Techniques for Modeling the Relationship between Tobacco and Alcohol and Cancer Tony Plate, Pierre Band, Joel Bert, John Grace
• An Apobayesian Relative of Winnow Nick Littlestone, Chris Mesterharm
• LSTM can Solve Hard Long Time Lag Problems Sepp Hochreiter, Jürgen Schmidhuber
• 488 Solutions to the XOR Problem Frans Coetzee, Virginia Stonick
• A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data David J. Miller, Hasan Uyar
• Statistical Mechanics of the Mixture of Experts Kukjin Kang, Jong-Hoon Oh
• Predicting Lifetimes in Dynamically Allocated Memory David Cohn, Satinder Singh
• Reinforcement Learning for Mixed Open-loop and Closed-loop Control Eric Hansen, Andrew Barto, Shlomo Zilberstein
• Computing with Infinite Networks Christopher Williams
• Regression with Input-Dependent Noise: A Bayesian Treatment Christopher Bishop, Cazhaow Quazaz
• The Generalisation Cost of RAMnets Richard Rohwer, Michal Morciniec
• Multilayer Neural Networks: One or Two Hidden Layers? Graham Brightwell, Claire Kenyon, Hélène Paugam-Moisy
• Improving the Accuracy and Speed of Support Vector Machines Christopher J. C. Burges, Bernhard Schölkopf
• Adaptive Access Control Applied to Ethernet Data Timothy Brown
• An Adaptive WTA using Floating Gate Technology W. Kruger, Paul Hasler, Bradley Minch, Christof Koch
• Representation and Induction of Finite State Machines using Time-Delay Neural Networks Daniel Clouse, C. Giles, Bill Horne, Garrison Cottrell
• Probabilistic Interpretation of Population Codes Richard Zemel, Peter Dayan, Alexandre Pouget
• Analog VLSI Circuits for Attention-Based, Visual Tracking Timothy Horiuchi, Tonia Morris, Christof Koch, Stephen DeWeerth
• Online Learning from Finite Training Sets: An Analytical Case Study Peter Sollich, David Barber
• Spatiotemporal Coupling and Scaling of Natural Images and Human Visual Sensitivities Dawei Dong
• Using Curvature Information for Fast Stochastic Search Genevieve Orr, Todd Leen
• ARC-LH: A New Adaptive Resampling Algorithm for Improving ANN Classifiers Friedrich Leisch, Kurt Hornik
• Estimating Equivalent Kernels for Neural Networks: A Data Perturbation Approach A. Burgess
• Monotonicity Hints Joseph Sill, Yaser Abu-Mostafa
• A Convergence Proof for the Softassign Quadratic Assignment Algorithm Anand Rangarajan, Alan L. Yuille, Steven Gold, Eric Mjolsness
• Removing Noise in On-Line Search using Adaptive Batch Sizes Genevieve Orr
• Size of Multilayer Networks for Exact Learning: Analytic Approach André Elisseeff, Hélène Paugam-Moisy
• Support Vector Regression Machines Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex Smola, Vladimir Vapnik
• A Hierarchical Model of Visual Rivalry Peter Dayan
• Dynamically Adaptable CMOS Winner-Take-All Neural Network Kunihiko Iizuka, Masayuki Miyamoto, Hirofumi Matsui
• Ensemble Methods for Phoneme Classification Steve Waterhouse, Gary Cook
• Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA Barak Pearlmutter, Lucas Parra
• Temporal Low-Order Statistics of Natural Sounds Hagai Attias, Christoph Schreiner
• Limitations of Self-organizing Maps for Vector Quantization and Multidimensional Scaling Arthur Flexer
• A Spike Based Learning Neuron in Analog VLSI Philipp Häfliger, Misha Mahowald, Lloyd Watts
• One-unit Learning Rules for Independent Component Analysis Aapo Hyvärinen, Erkki Oja
• Analysis of Temporal-Diffference Learning with Function Approximation John Tsitsiklis, Benjamin Van Roy
• Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm Achim Stahlberger, Martin Riedmiller
• Learning with Noise and Regularizers in Multilayer Neural Networks David Saad, Sara Solla
• The Learning Dynamcis of a Universal Approximator Ansgar West, David Saad, Ian Nabney
• Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo David Barber, Christopher Williams
• Genetic Algorithms and Explicit Search Statistics Shumeet Baluja
• Learning Exact Patterns of Quasi-synchronization among Spiking Neurons from Data on Multi-unit Recordings Laura Martignon, Kathryn Laskey, Gustavo Deco, Eilon Vaadia
• Efficient Nonlinear Control with Actor-Tutor Architecture Kenji Doya
• Interpolating Earth-science Data using RBF Networks and Mixtures of Experts Ernest Wan, Don Bone
• Statistically Efficient Estimations Using Cortical Lateral Connections Alexandre Pouget, Kechen Zhang
• Recursive Algorithms for Approximating Probabilities in Graphical Models Tommi Jaakkola, Michael Jordan
• Clustering via Concave Minimization Paul Bradley, Olvi Mangasarian, W. Street
• Balancing Between Bagging and Bumping Tom Heskes
• Self-Organizing and Adaptive Algorithms for Generalized Eigen-Decomposition Chanchal Chatterjee, Vwani Roychowdhury
• Multi-Grid Methods for Reinforcement Learning in Controlled Diffusion Processes Stephan Pareigis
• Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons Wolfgang Maass
• Edges are the 'Independent Components' of Natural Scenes. Anthony Bell, Terrence J. Sejnowski
• For Valid Generalization the Size of the Weights is More Important than the Size of the Network Peter Bartlett
• Neural Network Modeling of Speech and Music Signals Alex Röbel
• Rapid Visual Processing using Spike Asynchrony Simon Thorpe, Jacques Gautrais
• Visual Cortex Circuitry and Orientation Tuning Trevor Mundel, Alexander Dimitrov, Jack Cowan
• Orientation Contrast Sensitivity from Long-range Interactions in Visual Cortex Klaus Pawelzik, Udo Ernst, Fred Wolf, Theo Geisel