Book
Advances in Neural Information Processing Systems 12 (NIPS 1999)
Edited by:
S. Solla and T. Leen and K. Müller
- 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
Do not remove: This comment is monitored to verify that the site is working properly