An experimental comparison of recurrent neural networks

Bill G. Home; C. Lee Giles

An experimental comparison of recurrent neural networks

Bill G. Home, C. Lee Giles

Research output: Contribution to conference › Paper › peer-review

Abstract

Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these architectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification.

Original language	English (US)
Pages	697-704
Number of pages	8
State	Published - 1994
Event	7th International Conference on Neural Information Processing Systems, NIPS 1994 - Denver, United States Duration: Jan 1 1994 → Jan 1 1994

Conference

Conference	7th International Conference on Neural Information Processing Systems, NIPS 1994
Country/Territory	United States
City	Denver
Period	1/1/94 → 1/1/94

All Science Journal Classification (ASJC) codes

Information Systems
Signal Processing
Computer Networks and Communications

Cite this

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title = "An experimental comparison of recurrent neural networks",

abstract = "Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these architectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification.",

author = "Home, {Bill G.} and {Lee Giles}, C.",

note = "Publisher Copyright: {\textcopyright} NIPS 1994.All rights reserved.; 7th International Conference on Neural Information Processing Systems, NIPS 1994 ; Conference date: 01-01-1994 Through 01-01-1994",

year = "1994",

language = "English (US)",

pages = "697--704",

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T1 - An experimental comparison of recurrent neural networks

AU - Home, Bill G.

AU - Lee Giles, C.

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AB - Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these architectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification.

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M3 - Paper

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T2 - 7th International Conference on Neural Information Processing Systems, NIPS 1994

Y2 - 1 January 1994 through 1 January 1994

ER -

An experimental comparison of recurrent neural networks

Abstract

Conference

All Science Journal Classification (ASJC) codes

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