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Paper Abstract and Keywords
Presentation 2017-10-12 14:00
Entanglement Entropic Convolutional Neural Network
Shu Eguchi, Masaru Tanaka (Fukuoka Univ.) PRMU2017-73
Abstract (in Japanese) (See Japanese page) 
(in English) The neural network used for machine learning is an extract that extracts information necessary for classification from enormous data so as not to undergo overlearning. However, when extracting information necessary for classification, it also extracts unnecessary information for classification. In this article, we report on the results of experiments conducted to determine whether an algorithm to maintain important information is possible while reducing unnecessary information for classification. The model discussed in this paper is a convolution neural network based on an entanglement entropy (EECNN: Entanglement Entropic Convolutional Neural Network), which uses a physical approach to the machine learning field. The calculation of the entangement entropy using the probability amplitude obtained from input to the layer or output from the layer. The entangement entropy is an example where the original image can be sufficiently approximated by reconstructing using several singular values after the singular value decomposition, if the probability amplitude is not random noise. Maintaining important information while reducing information by restoring the original probability amplitude from singular values based on the results of entropy.
Keyword (in Japanese) (See Japanese page) 
(in English) quantum mechanics / machine learning / entanglement entropy / singular value decomposition / convolutional neural network / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 238, PRMU2017-73, pp. 61-66, Oct. 2017.
Paper # PRMU2017-73 
Date of Issue 2017-10-05 (PRMU) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee PRMU  
Conference Date 2017-10-12 - 2017-10-13 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2017-10-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Entanglement Entropic Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) quantum mechanics  
Keyword(2) machine learning  
Keyword(3) entanglement entropy  
Keyword(4) singular value decomposition  
Keyword(5) convolutional neural network  
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1st Author's Name Shu Eguchi  
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 2017-10-12 14:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2017-73 
Volume (vol) vol.117 
Number (no) no.238 
Page pp.61-66 
#Pages
Date of Issue 2017-10-05 (PRMU) 


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