The University of Southampton
University of Southampton Institutional Repository

Symmetries and Discriminability in Feedforward Network Architectures

Symmetries and Discriminability in Feedforward Network Architectures
Symmetries and Discriminability in Feedforward Network Architectures
This paper investigates the effects of introducing symmetries into feedforward neural networks in what are termed symmetry networks. This technique allows more efficient training for problems in which we require the output of a network to be invariant under a set of transformations of the input. The particular problem of graph recognition is considered. In this case the network is designed to deliver the same output for isomorphic graphs. This leads to the question for which inputs can be distinguished by such architectures. A theorem characterizing when two inputs can be distinguished by a symmetry network is given. As a consequence, a particular network design is shown to be able to distinguish nonisomorphic graphs if and only if the graph reconstruction conjecture holds.
816-826
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db

Shawe-Taylor, J. (1993) Symmetries and Discriminability in Feedforward Network Architectures. IEEE Transactions on Neural Networks, 4 (5), 816-826.

Record type: Article

Abstract

This paper investigates the effects of introducing symmetries into feedforward neural networks in what are termed symmetry networks. This technique allows more efficient training for problems in which we require the output of a network to be invariant under a set of transformations of the input. The particular problem of graph recognition is considered. In this case the network is designed to deliver the same output for isomorphic graphs. This leads to the question for which inputs can be distinguished by such architectures. A theorem characterizing when two inputs can be distinguished by a symmetry network is given. As a consequence, a particular network design is shown to be able to distinguish nonisomorphic graphs if and only if the graph reconstruction conjecture holds.

Text
SymmetriesAndDiscInFeedforwardnetworkArch.pdf - Other
Download (1MB)

More information

Published date: September 1993
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259819
URI: http://eprints.soton.ac.uk/id/eprint/259819
PURE UUID: dda9aa0d-c40d-4fad-91d6-dcde52e09478

Catalogue record

Date deposited: 03 Sep 2004
Last modified: 14 Mar 2024 06:28

Export record

Contributors

Author: J. Shawe-Taylor

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×