DocumentCode
423535
Title
Simple algorithm for recurrent neural networks that can learn sequence completion
Author
Szita, István ; Lõrincz, András
Author_Institution
Dept. of Information Syst., Eotvos Lorand Univ., Budapest, Hungary
Volume
1
fYear
2004
fDate
25-29 July 2004
Lastpage
188
Abstract
We can memorize long sequences like melodies or poems and it is intriguing to develop efficient connectionist representations for this problem. Recurrent neural networks have been proved to offer a reasonable approach here. We start from a few axiomatic assumptions and provide a simple mathematical framework that encapsulates the problem. A gradient-descent based algorithm is derived in this framework. Demonstrations on a benchmark problem show the applicability of our approach.
Keywords
gradient methods; learning (artificial intelligence); mathematical analysis; recurrent neural nets; axiomatic assumptions; connectionist representations; gradient-descent based algorithm; mathematical framework; recurrent neural network; sequence completion learning; Backpropagation algorithms; Chaos; Electronic mail; Humans; Information systems; Mathematical model; Neural networks; Prediction methods; Recurrent neural networks; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-8359-1
Type
conf
DOI
10.1109/IJCNN.2004.1379895
Filename
1379895
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