Advances in Neural Information Processing Systems 12 (NIPS 1999)

• Regular and Irregular Gallager-zype Error-Correcting Codes Yoshiyuki Kabashima, Tatsuto Murayama, David Saad, Renato Vicente
• An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task Akaysha Tang, Barak Pearlmutter, Tim Hely, Michael Zibulevsky, Michael Weisend
• The Entropy Regularization Information Criterion Alex Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson
• Invariant Feature Extraction and Classification in Kernel Spaces Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex Smola, Klaus-Robert Müller
• Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology Yair Weiss, William Freeman
• Population Decoding Based on an Unfaithful Model Si Wu, Hiroyuki Nakahara, Noboru Murata, Shun-ichi Amari
• Broadband Direction-Of-Arrival Estimation Based on Second Order Statistics Justinian Rosca, Joseph Ruanaidh, Alexander Jourjine, Scott Rickard
• Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA Aapo Hyvärinen, Patrik Hoyer
• Reinforcement Learning Using Approximate Belief States Andres Rodriguez, Ronald Parr, Daphne Koller
• Building Predictive Models from Fractal Representations of Symbolic Sequences Peter Tiño, Georg Dorffner
• Neural Computation with Winner-Take-All as the Only Nonlinear Operation Wolfgang Maass
• Support Vector Method for Multivariate Density Estimation Vladimir Vapnik, Sayan Mukherjee
• Leveraged Vector Machines Yoram Singer
• Learning Factored Representations for Partially Observable Markov Decision Processes Brian Sallans
• Variational Inference for Bayesian Mixtures of Factor Analysers Zoubin Ghahramani, Matthew Beal
• Topographic Transformation as a Discrete Latent Variable Nebojsa Jojic, Brendan J. Frey
• Channel Noise in Excitable Neural Membranes Amit Manwani, Peter Steinmetz, Christof Koch
• Efficient Approaches to Gaussian Process Classification Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther
• Optimal Sizes of Dendritic and Axonal Arbors Dmitri Chklovskii
• v-Arc: Ensemble Learning in the Presence of Outliers Gunnar Rätsch, Bernhard Schölkopf, Alex Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika
• Monte Carlo POMDPs Sebastian Thrun
• A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay Tasks Alfonso Renart, Néstor Parga, Edmund Rolls
• Information Factorization in Connectionist Models of Perception Javier Movellan, James McClelland
• Hierarchical Image Probability (H1P) Models Clay Spence, Lucas Parra
• Reinforcement Learning for Spoken Dialogue Systems Satinder Singh, Michael Kearns, Diane Litman, Marilyn Walker
• Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly David Horn, Nir Levy, Isaac Meilijson, Eytan Ruppin
• Image Representations for Facial Expression Coding Marian Bartlett, Gianluca Donato, Javier Movellan, Joseph Hager, Paul Ekman, Terrence J. Sejnowski
• Algorithms for Independent Components Analysis and Higher Order Statistics Daniel Lee, Uri Rokni, Haim Sompolinsky
• A SNoW-Based Face Detector Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
• A Winner-Take-All Circuit with Controllable Soft Max Property Shih-Chii Liu
• Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers Matthias Seeger
• Kirchoff Law Markov Fields for Analog Circuit Design Richard Golden
• Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects? Zhaoping Li
• Policy Gradient Methods for Reinforcement Learning with Function Approximation Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour
• Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks Michael Schmitt
• Evolving Learnable Languages Bradley Tonkes, Alan Blair, Janet Wiles
• Large Margin DAGs for Multiclass Classification John Platt, Nello Cristianini, John Shawe-Taylor
• Approximate Planning in Large POMDPs via Reusable Trajectories Michael Kearns, Yishay Mansour, Andrew Ng
• Maximum Entropy Discrimination Tommi Jaakkola, Marina Meila, Tony Jebara
• The Relaxed Online Maximum Margin Algorithm Yi Li, Philip Long
• Bayesian Modelling of fMRI lime Series Pedro Højen-Sørensen, Lars Hansen, Carl Rasmussen
• Bayesian Averaging is Well-Temperated Lars Hansen
• Policy Search via Density Estimation Andrew Ng, Ronald Parr, Daphne Koller
• Low Power Wireless Communication via Reinforcement Learning Timothy Brown
• Learning to Parse Images Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh
• Robust Recognition of Noisy and Superimposed Patterns via Selective Attention Soo-Young Lee, Michael C. Mozer
• Bayesian Network Induction via Local Neighborhoods Dimitris Margaritis, Sebastian Thrun
• Spiking Boltzmann Machines Geoffrey E. Hinton, Andrew Brown
• Actor-Critic Algorithms Vijay Konda, John Tsitsiklis
• Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks Masashi Sugiyama, Hidemitsu Ogawa
• Image Recognition in Context: Application to Microscopic Urinalysis Xubo Song, Joseph Sill, Yaser Abu-Mostafa, Harvey Kasdan
• Bayesian Map Learning in Dynamic Environments Kevin P. Murphy
• Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks Mike Schuster
• Statistical Dynamics of Batch Learning Song Li, K. Y. Michael Wong
• Scale Mixtures of Gaussians and the Statistics of Natural Images Martin J. Wainwright, Eero Simoncelli
• Independent Factor Analysis with Temporally Structured Sources Hagai Attias
• Managing Uncertainty in Cue Combination Zhiyong Yang, Richard Zemel
• Potential Boosters? Nigel Duffy, David Helmbold
• Resonance in a Stochastic Neuron Model with Delayed Interaction Toru Ohira, Yuzuru Sato, Jack Cowan
• Wiring Optimization in the Brain Dmitri Chklovskii, Charles Stevens
• Learning from User Feedback in Image Retrieval Systems Nuno Vasconcelos, Andrew Lippman
• Online Independent Component Analysis with Local Learning Rate Adaptation Nicol Schraudolph, Xavier Giannakopoulos
• A Variational Baysian Framework for Graphical Models Hagai Attias
• Algebraic Analysis for Non-regular Learning Machines Sumio Watanabe
• Model Selection in Clustering by Uniform Convergence Bounds Joachim Buhmann, Marcus Held
• Unmixing Hyperspectral Data Lucas Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus-Robert Müller
• Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions Tong Zhang
• Inference for the Generalization Error Claude Nadeau, Yoshua Bengio
• LTD Facilitates Learning in a Noisy Environment Paul Munro, Gerardina Hernández
• An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control Oliver Landolt, Steve Gyger
• Understanding Stepwise Generalization of Support Vector Machines: a Toy Model Sebastian Risau-Gusman, Mirta Gordon
• A Geometric Interpretation of v-SVM Classifiers David Crisp, Christopher J. C. Burges
• Robust Learning of Chaotic Attractors Rembrandt Bakker, Jaap Schouten, Marc-Olivier Coppens, Floris Takens, C. Giles, Cor van den Bleek
• Greedy Importance Sampling Dale Schuurmans
• Recurrent Cortical Competition: Strengthen or Weaken? Péter Adorján, Lars Schwabe, Christian Piepenbrock, Klaus Obermayer
• Constrained Hidden Markov Models Sam Roweis
• Approximate Inference A lgorithms for Two-Layer Bayesian Networks Andrew Ng, Michael Jordan
• Perceptual Organization Based on Temporal Dynamics Xiuwen Liu, DeLiang Wang
• Learning Informative Statistics: A Nonparametnic Approach John W. Fisher III, Alexander Ihler, Paul Viola
• Rules and Similarity in Concept Learning Joshua Tenenbaum
• Support Vector Method for Novelty Detection Bernhard Schölkopf, Robert C. Williamson, Alex Smola, John Shawe-Taylor, John Platt
• Generalized Model Selection for Unsupervised Learning in High Dimensions Shivakumar Vaithyanathan, Byron Dom
• An Improved Decomposition Algorithm for Regression Support Vector Machines Pavel Laskov
• An Analog VLSI Model of Periodicity Extraction André van Schaik
• From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data Eric Mjolsness, Tobias Mann, Rebecca Castaño, Barbara Wold
• Data Visualization and Feature Selection: New Algorithms for Nongaussian Data Howard Yang, John Moody
• An Information-Theoretic Framework for Understanding Saccadic Eye Movements Tai Sing Lee, Stella Yu
• Noisy Neural Networks and Generalizations Hava Siegelmann, Alexander Roitershtein, Asa Ben-Hur
• The Nonnegative Boltzmann Machine Oliver Downs, David MacKay, Daniel Lee
• Boosting Algorithms as Gradient Descent Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean
• Local Probability Propagation for Factor Analysis Brendan J. Frey
• A MCMC Approach to Hierarchical Mixture Modelling Christopher Williams
• The Infinite Gaussian Mixture Model Carl Rasmussen
• Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints Miguel Carreira-Perpiñán
• Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization Thomas Hofmann
• Bayesian Reconstruction of 3D Human Motion from Single-Camera Video Nicholas Howe, Michael Leventon, William Freeman
• Mixture Density Estimation Jonathan Li, Andrew Barron
• Information Capacity and Robustness of Stochastic Neuron Models Elad Schneidman, Idan Segev, Naftali Tishby
• Bayesian Transduction Thore Graepel, Ralf Herbrich, Klaus Obermayer
• Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting Yuansong Liao, John Moody
• An Analysis of Turbo Decoding with Gaussian Densities Paat Rusmevichientong, Benjamin Van Roy
• Probabilistic Methods for Support Vector Machines Peter Sollich
• Neural System Model of Human Sound Localization Craig Jin, Simon Carlile
• A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion Girish Patel, Edgar Brown, Stephen DeWeerth
• Graded Grammaticality in Prediction Fractal Machines Shan Parfitt, Peter Tiño, Georg Dorffner
• Learning Sparse Codes with a Mixture-of-Gaussians Prior Bruno Olshausen, K. Millman
• Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems Liqing Zhang, Shun-ichi Amari, Andrzej Cichocki
• Application of Blind Separation of Sources to Optical Recording of Brain Activity Holger Schoner, Martin Stetter, Ingo Schießl, John Mayhew, Jennifer Lund, Niall McLoughlin, Klaus Obermayer
• Spectral Cues in Human Sound Localization Craig Jin, Anna Corderoy, Simon Carlile, André van Schaik
• Robust Full Bayesian Methods for Neural Networks Christophe Andrieu, João de Freitas, Arnaud Doucet
• A Neurodynamical Approach to Visual Attention Gustavo Deco, Josef Zihl
• Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers Anthony Coolen, C. Mace
• Audio Vision: Using Audio-Visual Synchrony to Locate Sounds John Hershey, Javier Movellan
• Predictive Sequence Learning in Recurrent Neocortical Circuits Rajesh Rao, Terrence J. Sejnowski
• Differentiating Functions of the Jacobian with Respect to the Weights Gary Flake, Barak Pearlmutter
• Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information Thea Ghiselli-Crippa, Paul Munro
• Agglomerative Information Bottleneck Noam Slonim, Naftali Tishby
• Spike-based Learning Rules and Stabilization of Persistent Neural Activity Xiaohui Xie, H. Sebastian Seung
• Nonlinear Discriminant Analysis Using Kernel Functions Volker Roth, Volker Steinhage
• On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling Peter Sykacek
• Uniqueness of the SVM Solution Christopher J. C. Burges, David Crisp
• The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning Charles Isbell, Parry Husbands
• Search for Information Bearing Components in Speech Howard Yang, Hynek Hermansky
• An Oscillatory Correlation Frame work for Computational Auditory Scene Analysis Guy Brown, DeLiang Wang
• Manifold Stochastic Dynamics for Bayesian Learning Mark Zlochin, Yoram Baram
• Bifurcation Analysis of a Silicon Neuron Girish Patel, Gennady Cymbalyuk, Ronald Calabrese, Stephen DeWeerth
• Speech Modelling Using Subspace and EM Techniques Gavin Smith, João de Freitas, Tony Robinson, Mahesan Niranjan
• Optimal Kernel Shapes for Local Linear Regression Dirk Ormoneit, Trevor Hastie
• Robust Neural Network Regression for Offline and Online Learning Thomas Briegel, Volker Tresp
• Neural Network Based Model Predictive Control Stephen Piche, James Keeler, Greg Martin, Gene Boe, Doug Johnson, Mark Gerules
• Coastal Navigation with Mobile Robots Nicholas Roy, Sebastian Thrun
• Boosting with Multi-Way Branching in Decision Trees Yishay Mansour, David McAllester
• Neural Representation of Multi-Dimensional Stimuli Christian Eurich, Stefan Wilke, Helmut Schwegler
• Model Selection for Support Vector Machines Olivier Chapelle, Vladimir Vapnik
• Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration Panayiota Poirazi, Bartlett Mel
• State Abstraction in MAXQ Hierarchical Reinforcement Learning Thomas Dietterich
• Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks Yoshua Bengio, Samy Bengio
• A Generative Model for Attractor Dynamics Richard Zemel, Michael C. Mozer
• An Environment Model for Nonstationary Reinforcement Learning Samuel Choi, Dit-Yan Yeung, Nevin Zhang
• Predictive App roaches for Choosing Hyperparameters in Gaussian Processes S. Sundararajan, S. Keerthi
• Acquisition in Autoshaping Sham Kakade, Peter Dayan
• Transductive Inference for Estimating Values of Functions Olivier Chapelle, Vladimir Vapnik, Jason Weston
• Effective Learning Requires Neuronal Remodeling of Hebbian Synapses Gal Chechik, Isaac Meilijson, Eytan Ruppin
• The Relevance Vector Machine Michael Tipping
• Dual Estimation and the Unscented Transformation Eric Wan, Rudolph van der Merwe, Alex Nelson
• Learning Statistically Neutral Tasks without Expert Guidance Ton Weijters, Antal van den Bosch, Eric Postma
• Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks David Barber, Peter Sollich
• A Multi-class Linear Learning Algorithm Related to Winnow Chris Mesterharm
• Churn Reduction in the Wireless Industry Michael C. Mozer, Richard Wolniewicz, David Grimes, Eric Johnson, Howard Kaushansky
• Recognizing Evoked Potentials in a Virtual Environment Jessica Bayliss, Dana Ballard