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

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Todd K. Leen, Thomas G. Dietterich, Volker Tresp
MIT Press, 2001 - Computers - 1106 pages
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The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of 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

Who Does What? A Novel Algorithm to Determine Function Localization
3
A Productive Systematic Framework for the Representation of Visual Structure
10
The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem
17
HippocampallyDependent Consolidation in a Hierarchical Model ofNeocortex
24
Position Variance Recurrence and Perceptual Learning
31
The Use ofMDL to Select among Computational Models of Cognition
39
In J Myung Mark A Pitt Shaobo Zhang and Vijay Balasubramanian
45
The Early Word Catches the Weights
52
Feature Selection for SVMs Jason Weston Sayan Mukherjee Olivier Chapelle
668
On a Connection between Kernel PCA and Metric Multidimensional Scaling
675
Using the Nystrom Method to Speed Up Kernel Machines
682
Generalized Belief Propagation
689
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

Structure Learning in Human Causal Induction
59
Adaptive Object Representation with HierarchicallyDistributed Memory Sites
66
What Can a Single Neuron Compute?
75
Stability and Noise in Biochemical Switches William Bialek
103
A New Model of Spatial Representation in Multimodal Brain Areas
119
Finding the Key to a Synapse Thomas Natschlager and Wolfgang Maass
138
SpikeTimingDependent Learning for Oscillatory Networks
152
Natural Sound Statistics and Divisive Normalization in the Auditory System
166
Whence Sparseness? Carl van Vreeswijk
180
Algorithmic Stability and Generalization Performance
196
From Margin to Sparsity Thore Graepel Ralf Herbrich and Robert C Williamson
210
Why SVMs work
224
Second Order Approximations for Probability Models
238
A Support Vector Method for Clustering
367
A Variational MeanField Theory for Sigmoidal Belief Networks
374
Direct Classification with Indirect Data Timothy X Brown
381
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
523
Sparse Kernel Principal Component Analysis Michael E Tipping
633
Data Clustering by Markovian Relaxation and the Information Bottleneck
640
Active Learning for Parameter Estimation in Bayesian Networks
647
Mixtures of Gaussian Processes VolkerTresp
654
TreeBased Modeling and Estimation of Gaussian Processes on Graphs with
661
Speech Denoising and Dereverberation Using Probabilistic Models
758
Learning Joint Statistical Models for AudioVisual 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
A New Descriptor for Shape Matching and Object Recognition
831
Emergence of Movement Sensitive Neurons Properties by Learning a Sparse
838
Regularities in Scene Statistics which Enable
845
A Markov Chain Monte Carlo Approach
852
Keeping Flexible Active Contours on Track using Metropolis Updates
859
Color Opponency Constitutes a Sparse Representation for the Chromatic
866
Learning Segmentation by Random Walks Marina MeilS and Jianbo Shi
873
Partially Observable SDE Models for Image Sequence Recognition Tasks
880
Learning Sparse Image Codes using a Wavelet Pyramid Architecture
887
Learning and Tracking Cyclic Human Motion
894
Redundancy and Dimensionality Reduction in SparseDistributed
901
Ratecoded Restricted Boltzmann Machines for Face Recognition
908
Linking Psychophysics and Biophysics
915
From Mixtures of Mixtures to Adaptive Transform Coding
925
A Neural Probabilistic Language Model
932
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
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About the author (2001)

Volker Tresp heads a research group at Siemens Corporate Technology in Munich.

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