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Paper Abstract and Keywords
Presentation 2014-07-01 10:00
Learning Restricted Boltzmann Machine with discrete learning parameter
Seitaro Shinagawa (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-27
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, the method of Deep Neural Network (DNN) with hierarchical learning has been remarkable for performance to solve various complex tasks in machine learning, called “Deep Learning”. Pre-training for every two layers by using Restricted Boltzmann Machine (RBM) or auto-encoder (AE) is efficient and frequently used for constructing DNN. However, it takes longer time for learning. To solve this problem, we can select parallel computation by developing LSI chips. LSI chips have however limited area for Memory, it is not easy to apply them to big data processing. We show that learning of Deterministic RBM (DRBM) with discrete value of learning parameter (weight, bias) is efficient to solve this problem, and this method still has capability to create good DNN.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural network / Hierarchical / Deep learning / Pre-training / Restricted Boltzmann Machine / Discretization / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 113, NLP2014-27, pp. 37-40, June 2014.
Paper # NLP2014-27 
Date of Issue 2014-06-23 (NLP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLP2014-27

Conference Information
Committee NLP  
Conference Date 2014-06-30 - 2014-07-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2014-06-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Learning Restricted Boltzmann Machine with discrete learning parameter 
Sub Title (in English)  
Keyword(1) Neural network  
Keyword(2) Hierarchical  
Keyword(3) Deep learning  
Keyword(4) Pre-training  
Keyword(5) Restricted Boltzmann Machine  
Keyword(6) Discretization  
Keyword(7)  
Keyword(8)  
1st Author's Name Seitaro Shinagawa  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Yoshihiro Hayakawa  
2nd Author's Affiliation Sendai National College of Technology (SNCT)
3rd Author's Name Shigeo Sato  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
4th Author's Name Takeshi Onomi  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
5th Author's Name Koji Nakajima  
5th Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2014-07-01 10:00:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2014-27 
Volume (vol) vol.114 
Number (no) no.113 
Page pp.37-40 
#Pages 4 
Date of Issue 2014-06-23 (NLP) 

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