Unsupervised Learning: Foundations of Neural Computation / Edition 1

by Geoffrey Hinton, Terrence J. Sejnowski

Overview

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

Product Details

ISBN-13:	9780262581684
Publisher:	MIT Press
Publication date:	05/24/1999
Series:	Computational Neuroscience Series
Edition description:	First Edition
Pages:	418
Product dimensions:	6.00(w) x 9.00(h) x 0.90(d)
Age Range:	18 Years

About the Author

Geoffrey Hinton is Professor of Computer Science at the University of Toronto.

Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is President of the Neural Information Processing (NIPS) Foundation. He is the author of The Deep Learning Revolution (MIT Press) and other books.

Table of Contents

Introduction
1 Unsupervised Learning
H.B. Barlow
2 Local Synaptic Learning Rules Suffice to MaXimize Mutual
Information in a Linear Network
Ralph Linsker
3 Convergent Algorithm for Sensory Receptive Field
Development
Joseph J. Atick and A. Norman Redlich
4 Emergence of PositionIndependent Detectors of Sense of
Rotation and Dilation with Hebbian Learning: An Analysis
Kechen Zhang, Martin I. Sereno, and Margaret E. Sereno
5 Learning Invariance from Transformation Sequences
Peter Földiák
6 Learning Perceptually Salient Visual Parameters Using
Spatiotemporal Smoothness Constraints
James V. Stone
7 What Is the Goal of Sensory Coding?
David J. Field
8 An InformationMaXimization Approach to Blind Separation
and Blind Deconvolution
Anthony J. Bell and Terrence J. Sejnowski
9 Natural Gradient Works Efficiently in Learning
Shunichi Amari
10 A Fast FiXedPoint Algorithm for Independent Components
Analysis
Aapo Hyvärinen and Erkki Oja
11 Feature EXtraction Using an Unsupervised Neural Network
Nathan Intrator
12 Learning MiXture Models of Spatial Coherence
Suzanna Becker and Geoffrey E. Hinton
13 Bayesian SelfOrganization Driven by Prior Probability
Distributions
Alan L. Yuille, Stelios M. Smirnakis, and Lei Xu
14 Finding Minimum Entropy Codes
H.B. Barlow, T.P. Kaushal, and G.J. Mitchison
15 Learning Population Codes by Minimizing Description
Length
Richard S. Zemel and Geoffrey E. Hinton
16 The Helmholtz Machine
Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, andRichard
S. Zemel
17 Factor Analysis Using DeltaRule WakeSleep Learning
Radford M. Neal and Peter Dayan
18 Dimension Reduction by Local Principal Component
Analysis
Nandakishore Kambhatla and Todd K. Leen
19 A ResourceAllocating Network for Function Interpolation
John Platt
20 Learning with Preknowledge: Clustering with Point and
Graph Matching Distance Measures
Steven Gold, Anand Rangarajan, and Eric Mjolsness
21 Learning to Generalize from Single EXamples in the
Dynamic Link Architecture
Wolfgang Konen and Christoph von der Malsburg
IndeX