Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference

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MIT Press, 2001 - Computers - 1106 pages
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The annual conference on Neural Information Processing Systems (NIPS) is the flagshipconference on neural computation. The conference is interdisciplinary, with contributions inalgorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing,reinforcement learning and control, implementations, and diverse applications. Only about 30 percentof the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high.These proceedings contain all of the papers that were presented at the 2000 conference.

 

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Contents

Ranit AharonovBarki Isaac Meilijson and Eytan Ruppin
10
HippocampallyDependent Consolidation in a Hierarchical Model ofNeocortex
24
The Use of MDL to Select among Computational Models of Cognition
38
The Early Word Catches the Weights
52
Adaptive Object Representation with HierarchicallyDistributed Memory Sites
66
Dendritic Compartmentalization Could Underlie Competition and Attentional
82
Modelling Spatial Recall Mental Imagery and Neglect
96
Stability and Noise in Biochemical Switches William Bialek
103
Algorithms for Nonnegative Matrix Factorization
556
Constrained Independent Component Analysis Wei Lu and Jagath C Rajapakse
570
The Unscented Particle Filter
584
Automatic Choice of Dimensionality for PCA Thomas P Minka
598
An Information Maximization Approach to Overcomplete and Recurrent
612
Kernel Expansions with Unlabeled Examples
626
Data Clustering by Markovian Relaxation and the Information Bottleneck
640
Mixtures of Gaussian Processes Volker Tresp
654

A New Model of Spatial Representation in Multimodal Brain Areas
117
Dopamine Bonuses Sham Kakade and Peter Dayan
131
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic
145
Universality and Individuality in a Neural Code Elad Schneidman
159
Interfacing a Silicon Neuron to a Leech Heart
173
Theory
186
Competition and Arbors in Ocular Dominance Peter Dayan
203
Permitted and Forbidden Sets in Symmetric ThresholdLinear Networks
217
On Reversing Jensen s Inequality Tony Jebara and Alex Pentland
231
Some New Bounds on the Generalization Error of Combined Classifiers
245
Foundations for a Circuit Complexity Theory of Sensory Processing
259
A Framework for Good
273
Simulations With Field Theoretic Priors
287
The Kernel Trick for Distances Bernhard Scholkopf
301
Analysis of Bit Error Probability of DirectSequence CDMA Multiuser
315
Algebraic Information Geometry for Learning Machines with Singularities
329
Stagewise Processing in Errorcorrecting Codes and Image Restoration
343
Convergence of Large Margin Separable Linear Classification Tong Zhang
357
A Variational MeanField Theory for Sigmoidal Belief Networks
374
Model Complexity Goodness of Fit and Diminishing Returns
388
A Linear Programming Approach to Novelty Detection
395
Incremental and Decremental Support Vector Machine Learning
409
Gaussianization Scott Saobing Chen and Ramesh A Gopinath
423
Improved Output Coding for Classification Using Continuous Relaxation
437
Explaining Away in Weight Space Peter Dayan and Sham Kakade
451
Hightemperature Expansions for Learning Models of Nonnegative Data
465
A StructureBased Approach
479
Sequentially Fitting Inclusive Trees for Inference in NoisyOR Networks
493
Propagation Algorithms for Variational Bayesian Learning
507
NBody Problems in Statistical Learning
521
A SampleBased Criterion
535
Ensemble Learning and Linear Response Theory for ICA
542
Feature Selection for SVMs Jason Weston Sayan Mukherjee Olivier Chapelle
668
Christopher K I Williams
682
A GradientBased Boosting Algorithm for Regression Problems
696
A Silicon Primitive for Competitive Learning
713
Homeostasis in a Silicon Integrate and Fire Neuron
727
Fourlegged Walking Gait Control Using a Neuromorphic Chip Interfaced to
741
Speech Denoising and Dereverberation Using Probabilistic Models
758
Learning Joint Statistical Models for Audio Visual Fusion and Segregation
772
HigherOrder Statistical Properties Arising from the NonStationarity of Natural
786
Minimum Bayes Error Feature Selection for Continuous Speech Recognition
800
A Linear Operator for Measuring Synchronization of Video Facial
814
Noise Suppression Based on Neurophysiologicallymotivated SNR Estimation
821
Emergence of Movement Sensitive Neurons Properties by Learning a Sparse
838
A Markov Chain Monte Carlo Approach
852
Color Opponency Constitutes a Sparse Representation for the Chromatic
866
Partially Observable SDE Models for Image Sequence Recognition Tasks
880
Learning and Tracking Cyclic Human Motion
894
Ratecoded Restricted Boltzmann Machines for Face Recognition
908
From Mixtures of Mixtures to Adaptive Transform Coding
925
A Comparison of Image Processing Techniques for Visual Speech Recognition
939
Recognizing Handwritten Digits Using Hierarchical Products of Experts
953
Probabilistic Semantic Video Indexing
967
Learning Switching Linear Models of Human Motion
981
The Use of Classifiers in Sequential Inference Vasin Punyakanok and Dan Roth
995
Bayesian Video Shot Segmentation Nuno Vasconcelos and Andrew Lippman
1009
Exact Solutions to TimeDependent MDPs Justin A Boyan and Michael L Littman
1026
Reinforcement Learning with Function Approximation Converges to a Region
1040
Automated State Abstraction for Options using the UTree Algorithm
1054
Balancing Multiple Sources of Reward in Reinforcement Learning
1082
Approximate Policy Construction Using Decision Diagrams
1089
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About the author (2001)

Thomas G. Dietterich is Professor of Computer Science at Oregon State University.

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