Abstract
We present a measure of representation in neural networks that we call ‘R’, which is based on information theory. We show how R relates to an analysis of distributed representation, viz. a principal components analysis of activation space. Finally, we argue that R is well suited to measure representation in neural networks.
| Original language | English |
|---|---|
| Title of host publication | ASCS09 |
| Subtitle of host publication | proceedings of the 9th Conference of the Australasian Society for Cognitive Science |
| Editors | Wayne Christensen, Elizabeth Schier, John Sutton |
| Place of Publication | North Ryde, NSW |
| Publisher | Macquarie Centre for Cognitive Science |
| Pages | 232-237 |
| Number of pages | 6 |
| ISBN (Print) | 9780646529189 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | Conference of the Australasian Society for Cognitive Science (9th : 2009) - Sydney Duration: 30 Sept 2009 → 2 Oct 2009 |
Conference
| Conference | Conference of the Australasian Society for Cognitive Science (9th : 2009) |
|---|---|
| City | Sydney |
| Period | 30/09/09 → 2/10/09 |
Bibliographical note
Copyright 2009 by the Australasian Society for Cognitive Science. Publisher version archived with the permission of the Editor, ASCS09 : Proceedings of the 9th Conference of the Australasian Society for Cognitive Science. This copy is available for individual, non-commercial use. Permission to reprint/republish this version for other uses must be obtained from the publisher.Keywords
- artificial neural networks
- (distributed) representation
- information theory
- principal components analysis
- measure