Presentation 2017-11-09
Investigation of Hebbian-Like Learning Algorithm Based on Feedback Alignment for Autoencoder
Takashi Matsubara,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Multilayer perceptrons have been trained by the back-propagation (BP) algorithm. Recent studies on feedback alignment (FA) have demonstrated that the BP does not necessarily require an accurate propagation of error signal in a layer-by-layer fashion, and attract attention as a bridge between the BP and a learning mechanism in biological neural networks. This study proposes an extension of FA for autoencoders and demonstrates that the proposed algorithm mimics the formation of Hebbian learning and can train an autoencoder.
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Keyword(in English)
Paper # CCS2017-24
Date of Issue 2017-11-02 (CCS)

Conference Information
Committee CCS
Conference Date 2017/11/9(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Osaka Univ.
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Naoki Wakamiya(Osaka Univ.)
Vice Chair Mikio Hasegawa(Tokyo Univ. of Science) / Makoto Naruse(NICT)
Secretary Mikio Hasegawa(Osaka Univ.) / Makoto Naruse(Tokyo City Univ.)
Assistant Chisa Takano(Hirishima City Univ.) / Takashi Shimada(Univ. of Tokyo) / Tomoya Suzuki(Ibaraki Univ.) / Ryo Takahashi(AUT)

Paper Information
Registration To Technical Committee on Complex Communication Sciences
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Investigation of Hebbian-Like Learning Algorithm Based on Feedback Alignment for Autoencoder
Sub Title (in English)
Keyword(1)
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1st Author's Name Takashi Matsubara
1st Author's Affiliation Kobe University(Kobe Univ.)
Date 2017-11-09
Paper # CCS2017-24
Volume (vol) vol.117
Number (no) CCS-288
Page pp.pp.21-24(CCS),
#Pages 4
Date of Issue 2017-11-02 (CCS)