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Paper Abstract and Keywords
Presentation 2023-03-17 16:45
Enhancement of functionality in Deep Echo State Network by optimizing leak rate
Shuichi Inoue, Sou Nobukawa (CIT), Haruhiko Nishimura (UOH), Eiji Watanabe (NIBB), Teijiro Isokawa (UOH) MSS2022-110 NLP2022-155
Abstract (in Japanese) (See Japanese page) 
(in English) Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, which can respond on layer-specific different time scales. This dynamical characteristic leads to improve performance. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leak rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay. These characteristics produce a high memory capacity, consequently, diverse responses leads to an enhancement of Deep-ESN functionality.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Reservoir computing / Echo State Network / Deep Echo State Network / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 436, NLP2022-155, pp. 231-236, March 2023.
Paper # NLP2022-155 
Date of Issue 2023-03-08 (MSS, NLP) 
ISSN Online edition: ISSN 2432-6380
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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 NLP MSS  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To NLP 
Conference Code 2023-03-NLP-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancement of functionality in Deep Echo State Network by optimizing leak rate 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Reservoir computing  
Keyword(3) Echo State Network  
Keyword(4) Deep Echo State Network  
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1st Author's Name Shuichi Inoue  
1st Author's Affiliation Chiba Institute of Technology (CIT)
2nd Author's Name Sou Nobukawa  
2nd Author's Affiliation Chiba Institute of Technology (CIT)
3rd Author's Name Haruhiko Nishimura  
3rd Author's Affiliation University of Hyogo (UOH)
4th Author's Name Eiji Watanabe  
4th Author's Affiliation National Institute for Basic Biology (NIBB)
5th Author's Name Teijiro Isokawa  
5th Author's Affiliation University of Hyogo (UOH)
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Speaker Author-1 
Date Time 2023-03-17 16:45:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2022-110, NLP2022-155 
Volume (vol) vol.122 
Number (no) no.435(MSS), no.436(NLP) 
Page pp.231-236 
#Pages
Date of Issue 2023-03-08 (MSS, NLP) 


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