Publication:
A comparison of architectural alternatives for recurrent networks
A comparison of architectural alternatives for recurrent networks
dc.contributor.author | Wilson, William Hulme | en_US |
dc.date.accessioned | 2021-11-25T13:11:02Z | |
dc.date.available | 2021-11-25T13:11:02Z | |
dc.date.issued | 1993 | en_US |
dc.description.abstract | This paper describes a class of recurrent neural networks related to Elman networks. The networks used herein differ from standard Elman networks in that they may have more than one state vector. Such networks have an explicit representation of the hidden unit activations from several steps back. In principle, a single-state-vector network is capable of learning any sequential task that a multi-state-vector network can learn. This paper describes experiments which show that, in practice, and for the learning task used, a multi-state-vector network can learn a task faster and better than a single-state-vector network. The task used involved learning the graphotactic structure of a sample of about 400 English words. | en_US |
dc.identifier.uri | http://hdl.handle.net/1959.4/11534 | |
dc.language | English | |
dc.language.iso | EN | en_US |
dc.publisher | Sydney University Electrical Engineering | en_US |
dc.rights | CC BY-NC-ND 3.0 | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/au/ | en_US |
dc.source | Legacy MARC | en_US |
dc.subject.other | Neural Networks, Genetic Algorithms and Fuzzy Logic (280212) | en_US |
dc.subject.other | Elman network | en_US |
dc.subject.other | simple recurrent network | en_US |
dc.subject.other | tower network | en_US |
dc.title | A comparison of architectural alternatives for recurrent networks | en_US |
dc.type | Conference Paper | en |
dcterms.accessRights | open access | |
dspace.entity.type | Publication | en_US |
unsw.accessRights.uri | https://purl.org/coar/access_right/c_abf2 | |
unsw.description.notePublic | Further publications of this author may be found at http://www.cse.unsw.edu.au/~billw/selpubs.html | en_US |
unsw.identifier.doi | https://doi.org/10.26190/unsworks/442 | |
unsw.publisher.place | Sydney, Australia | en_US |
unsw.relation.faculty | Engineering | |
unsw.relation.ispartofconferenceLocation | Sydney, Australia | en_US |
unsw.relation.ispartofconferenceName | Australian Conference on Neural Networks | en_US |
unsw.relation.ispartofconferenceProceedingsTitle | Proceedings of the Fourth Australian Conference on Neural Networks (ACNN’93) | en_US |
unsw.relation.ispartofconferenceYear | 1993 | en_US |
unsw.relation.ispartofpagefrompageto | 189-192 | en_US |
unsw.relation.originalPublicationAffiliation | Wilson, William Hulme, Computer Science & Engineering, Faculty of Engineering, UNSW | en_US |
unsw.relation.school | School of Computer Science and Engineering | * |
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