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| Presentation | 2022-01-21 11:45 Physical deep learning based on optimal control of dynamical systems Satoshi Sunada, Genki Furuhata, Tomoaki Niiyama (Kanazawa Univ.)  NLP2021-79 MICT2021-54 MBE2021-40 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | An underlying key factor of deep neural networks is the information propagation through the layers. This suggests a connection between deep neural networks and dynamical systems. In this presentation, we propose and demonstrate a pattern recognition approach based on optimal control of continuous-time dynamical systems. As a key example, we consider a delay system and show that it allows for information processing based on a virtual large-scale network in a physically single node with only a few control parameters. In addition, we discuss hardware implementation in an optoelectronic delay system. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Neural Network / Deep Learning / Dynamical System / Optimal Control / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 335, NLP2021-79, pp. 36-36, Jan. 2022. | |
| Paper # | NLP2021-79 | |
| Date of Issue | 2022-01-14 (NLP, MICT, MBE) | |
| ISSN | 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 | NLP2021-79 MICT2021-54 MBE2021-40 |
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| Conference Information | |
| Committee | NLP MICT MBE NC |
| Conference Date | 2022-01-21 - 2022-01-23 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2022-01-NLP-MICT-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Physical deep learning based on optimal control of dynamical systems |
| Sub Title (in English) | |
| Keyword(1) | Neural Network |
| Keyword(2) | Deep Learning |
| Keyword(3) | Dynamical System |
| Keyword(4) | Optimal Control |
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| 1st Author's Name | Satoshi Sunada |
| 1st Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
| 2nd Author's Name | Genki Furuhata |
| 2nd Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
| 3rd Author's Name | Tomoaki Niiyama |
| 3rd Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-01-21 11:45:00 |
| Presentation Time | 25 minutes |
| Registration for | NLP |
| Paper # | NLP2021-79, MICT2021-54, MBE2021-40 |
| Volume (vol) | vol.121 |
| Number (no) | no.335(NLP), no.336(MICT), no.337(MBE) |
| Page | p.36 |
| #Pages | 1 |
| Date of Issue | 2022-01-14 (NLP, MICT, MBE) |
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