Presentation | 1996/6/21 Annealed RNN Learning of Finite State Automata Ken-ichi Arai, Ryohei Nakano, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In recurrent neural network (RNN) learning of finite state automata (FSA), we discuss how a neuro gain (J) influences the stability of the state representation and the performance of the learning. We formally show that the existence of the critical neuro gain (β_0) : any β larger than β_0 makes an RNN maintain the stable representation of states of an acquired FSA. Considering the existence of β_0 and avoidance of local minima, we propose a new RNN learning method with the scheduling of β, called an annealed RNN learning. Our experiments show that the annealed RNN learning went beyond a constant β learning. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Recurrent Neural Network / Finite State Automata / Stability / annealing |
Paper # | NC96-12 |
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Committee | NC |
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Conference Date | 1996/6/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Annealed RNN Learning of Finite State Automata |
Sub Title (in English) | |
Keyword(1) | Recurrent Neural Network |
Keyword(2) | Finite State Automata |
Keyword(3) | Stability |
Keyword(4) | annealing |
1st Author's Name | Ken-ichi Arai |
1st Author's Affiliation | NTT Communication Science Laboratories() |
2nd Author's Name | Ryohei Nakano |
2nd Author's Affiliation | NTT Communication Science Laboratories |
Date | 1996/6/21 |
Paper # | NC96-12 |
Volume (vol) | vol.96 |
Number (no) | 117 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |