Neural Comp. NEW Faster Access
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Edwards, P. J.
Right arrow Articles by Murray, A. F.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Edwards, P. J.
Right arrow Articles by Murray, A. F.

Neural Computation, Vol 10, 987-1005, Copyright © 1998 by The MIT Press

LETTERS

Towards Optimally Distributed Computation

Peter J. Edwards and Alan F. Murray

This article introduces the concept of optimally distributed computation in feedforward neural networks via regularization of weight saliency. By constraining the relative importance of the parameters, computation can be distributed thinly and evenly throughout the network. We propose that this will have beneficial effects on fault-tolerance performance and generalization ability in large network architectures. These theoretical predictions are verified by simulation experiments on two problems: one artificial and the other a real-world task. In summary, this article presents regularization terms for distributing neural computation optimally.




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1998 by The MIT Press.