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 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) |
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PRMU2017-73 |
Conference Information |
Committee |
PRMU |
Conference Date |
2017-10-12 - 2017-10-13 |
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(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) |
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quantum mechanics |
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machine learning |
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entanglement entropy |
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singular value decomposition |
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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 |
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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 |
6 |
Date of Issue |
2017-10-05 (PRMU) |
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