Presentation | 2002/1/22 Neural Network Pruning using MV Regularizer Takashi NAGATA, Atsushi KAWATA, Ken-ichi YAMADA, Ryohei NAKANO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The paper proposes a new method of neural network pruning by using a strong regularizer called MV regularizer. The MV regularizer learns a distinct penalty factor attached to each weight by solving a minimization problem over the validation error. After the learning, penalty factors are used to prune network weights one by one by monitoring generalization performance. The experiments using artificial data and real data showed the proposed method worked very well to remove a number of insignificant weights without the loss of generalization. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | neural network / network pruning / MV regularizer / generalization |
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Committee | NC |
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Conference Date | 2002/1/22(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) | Neural Network Pruning using MV Regularizer |
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Keyword(1) | neural network |
Keyword(2) | network pruning |
Keyword(3) | MV regularizer |
Keyword(4) | generalization |
1st Author's Name | Takashi NAGATA |
1st Author's Affiliation | Department of Intelligence and Computer Science, Nagoya Institute of Technology() |
2nd Author's Name | Atsushi KAWATA |
2nd Author's Affiliation | Department of Intelligence and Computer Science, Nagoya Institute of Technology |
3rd Author's Name | Ken-ichi YAMADA |
3rd Author's Affiliation | Department of Intelligence and Computer Science, Nagoya Institute of Technology |
4th Author's Name | Ryohei NAKANO |
4th Author's Affiliation | Department of Intelligence and Computer Science, Nagoya Institute of Technology |
Date | 2002/1/22 |
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Volume (vol) | vol.101 |
Number (no) | 616 |
Page | pp.pp.- |
#Pages | 6 |
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