Introduction |
Unsupervised Learning / H. B. Barlow1: |
Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network / Ralph Linsker2: |
Convergent Algorithm for Sensory Receptive Field Development / Joseph J. Atick ; A. Norman Redlich3: |
Emergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis / Kechen Zhang ; Martin I. Sereno ; Margaret E. Sereno4: |
Learning Invariance from Transformation Sequences / Peter Foldiak5: |
Learning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints / James V. Stone6: |
What Is the Goal of Sensory Coding? / David J. Field7: |
An Information-Maximization Approach to Blind Separation and Blind Deconvolution / Anthony J. Bell ; Terrence J. Sejnowski8: |
Natural Gradient Works Efficiently in Learning / Shun-ichi Amari9: |
A Fast Fixed-Point Algorithm for Independent Component Analysis / Aapo Hyvarinen ; Erkki Oja10: |
Feature Extraction Using an Unsupervised Neural Network / Nathan Intrator11: |
Learning Mixture Models of Spatial Coherence / Suzanna Becker ; Geoffrey E. Hinton12: |
Bayesian Self-Organization Driven by Prior Probability Distributions / Alan L. Yuille ; Stelios M. Smirnaki ; Lei Xu13: |
Finding Minimum Entropy Codes / T.P. Kaushal ; G. J. Mitchison14: |
Learning Population Codes by Minimizing Description Length / Richard S. Zemel15: |
The Helmholtz Machine / Peter Dayan ; Radford M. Neal16: |
Factor Analysis Using Delta-Rule Wake-Sleep Learning / 17: |
Dimension Reduction by Local Principal Component Analysis / Nandakishore Kambhatla ; Todd K. Leen18: |
A Resource-Allocating Network for Function Interpolation / John Platt19: |
Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures / Steven Gold ; Anand Rangarajan ; Eric Mjolsness20: |
Learning to Generalize from Single Examples in the Dynamic Link Architecture / Wolfgang Konen ; Christoph von der Malsburg21: |
Index |
Introduction |
Unsupervised Learning / H. B. Barlow1: |
Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network / Ralph Linsker2: |