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Paper Abstract and Keywords
Presentation 2018-01-18 18:05
Convolutional Neural Network based on Entanglement Entropy and Convolutional Neural Network based on Principal Component Analysis
Shu Eggchee, Masaru Tanaka (Fukuoka Univ.) PRMU2017-129 MVE2017-50
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we study the differences in the CNN performing processing in accordance with the data using the CNN and entanglement entropy for performing processing in accordance with the data using the principal component analysis. CNN performing processing in accordance with the data using the entanglement entropy eliminates pooling and dropouts used in general CNN, proper selection of unnecessary information for each data based on entanglement entropy Based on the amount of information to be learned. Specifically, unnecessary information reduction using singular value decomposition based on entanglement and entropy is performed. On the other hand, in the field of image processing, image compression using singular value decomposition is often performed based on the contribution ratio of principal component analysis. CNN performing processing in accordance with the data using the principal component analysis is a learning method using about contraction of dimensions based on the contribution rate of principal component analysis of the original data.
Keyword (in Japanese) (See Japanese page) 
(in English) quantum mechanics / entanglement entropy / principal component analysis / singular value decomposition / convolutional neural network / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 391, PRMU2017-129, pp. 141-146, Jan. 2018.
Paper # PRMU2017-129 
Date of Issue 2018-01-11 (PRMU, MVE) 
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 PRMU2017-129 MVE2017-50

Conference Information
Committee PRMU MVE IPSJ-CVIM  
Conference Date 2018-01-18 - 2018-01-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2018-01-PRMU-MVE-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Convolutional Neural Network based on Entanglement Entropy and Convolutional Neural Network based on Principal Component Analysis 
Sub Title (in English)  
Keyword(1) quantum mechanics  
Keyword(2) entanglement entropy  
Keyword(3) principal component analysis  
Keyword(4) singular value decomposition  
Keyword(5) convolutional neural network  
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1st Author's Name Shu Eggchee  
1st Author's Affiliation Fukuoka University (Fukuoka Univ.)
2nd Author's Name Masaru Tanaka  
2nd Author's Affiliation Fukuoka University (Fukuoka Univ.)
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Speaker Author-1 
Date Time 2018-01-18 18:05:00 
Presentation Time 25 minutes 
Registration for PRMU 
Paper # PRMU2017-129, MVE2017-50 
Volume (vol) vol.117 
Number (no) no.391(PRMU), no.392(MVE) 
Page pp.141-146 
#Pages 6 
Date of Issue 2018-01-11 (PRMU, MVE) 

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