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| Paper Abstract and Keywords | ||
| Presentation | 2018-09-21 11:10 [Invited Talk] Reservoir Computing: Theory, Physical Implementations, and Applications Kohei Nakajima (Univ. Tokyo)  PRMU2018-60 IBISML2018-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Reservoir Computing (RC) has been proposed as a framework for training recurrent neural networks. In this framework, low-dimensional input is projected to a high-dimensional dynamical system, which is referred to as a reservoir. If the dynamics of the reservoir involve enough nonlinearity and if enough memory is available, emulating nonlinear dynamical systems requires only the addition of a linear, static readout from the high-dimensional state space of the reservoir. Due to its generic nature, RC is not limited to digital simulations of neural networks. In fact, any high-dimensional dynamical system, including a system utilizing physical dynamics, can serve as a reservoir if it has the appropriate properties. In this talk, we will introduce the recent advancement of RC by presenting a number of new applications in the field of soft robotics. This short paper aims to introduce the basics of the framework by explaining a benchmark platform called the echo state network. The use of this example should help to clarify the advanced content in the talk from its very foundation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Reservoir computing / Physical reservoir computing / Recurrent neural network / Soft robotics / Soft robots / Nonlinear dynamics / Morphological computation / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 220, IBISML2018-37, pp. 149-154, Sept. 2018. | |
| Paper # | IBISML2018-37 | |
| Date of Issue | 2018-09-13 (PRMU, IBISML) | |
| 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 | PRMU2018-60 IBISML2018-37 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2018-09-20 - 2018-09-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2018-09-PRMU-IBISML-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Reservoir Computing: Theory, Physical Implementations, and Applications |
| Sub Title (in English) | |
| Keyword(1) | Reservoir computing |
| Keyword(2) | Physical reservoir computing |
| Keyword(3) | Recurrent neural network |
| Keyword(4) | Soft robotics |
| Keyword(5) | Soft robots |
| Keyword(6) | Nonlinear dynamics |
| Keyword(7) | Morphological computation |
| Keyword(8) | |
| 1st Author's Name | Kohei Nakajima |
| 1st Author's Affiliation | The University of Tokyo (Univ. Tokyo) |
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| Speaker | Author-1 |
| Date Time | 2018-09-21 11:10:00 |
| Presentation Time | 50 minutes |
| Registration for | IBISML |
| Paper # | PRMU2018-60, IBISML2018-37 |
| Volume (vol) | vol.118 |
| Number (no) | no.219(PRMU), no.220(IBISML) |
| Page | pp.149-154 |
| #Pages | 6 |
| Date of Issue | 2018-09-13 (PRMU, IBISML) |
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