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Paper Abstract and Keywords
Presentation 2026-01-29 12:45
Introducing Dimension-Wise Hysteresis in Neural Network Weight Updates
Hidetoshi Nagai (KIT) NLP2025-103 MBE2025-43 NC2025-65
Abstract (in Japanese) (See Japanese page) 
(in English) In neural network training, weight updates are typically performed based on gradients; however, the magnitude of a gradient does not necessarily correspond to the distance to the local optimal solution.
Consider each dimension independently. When a sign reversal occurs in the update amount, reduce the reflected update amount as if the learning rate had decreased. Futhermore, while updates of the same sign continue, increase the reflected update amount step by step as if the learning rate had increased. Applying this adjustment can be expected to accelerate convergence to a local optimal solution. Nevertheless, even with similar operations, applying strong suppression of the update amount at sign flip and small incremental expansions during same-sign updates to a dimension can instead suppress learning in that dimension.
Therefore, in this paper, we propose a mechanism that introduces per-dimension hysteresis into the weight update process. This mechanism can independently accelerate or decelerate learning progress for each dimension. We implemented a model incorporating this mechanism and conducted a simple experiment to evaluate its ability to accelerate learning. The results confirmed the effectiveness of the proposed approach.
Keyword (in Japanese) (See Japanese page) 
(in English) neural network / machine learning / weight update / hysteresis / Hysteresis Synapse / learning speed control / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 346, NC2025-65, pp. 80-85, Jan. 2026.
Paper # NC2025-65 
Date of Issue 2026-01-21 (NLP, MBE, NC) 
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 NLP2025-103 MBE2025-43 NC2025-65

Conference Information
Committee NC MBE NLP IEE-MBE  
Conference Date 2026-01-28 - 2026-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology, Wakamatsu Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) NC, NLP, ME, etc. 
Paper Information
Registration To NC 
Conference Code 2026-01-NC-MBE-NLP-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Introducing Dimension-Wise Hysteresis in Neural Network Weight Updates 
Sub Title (in English)  
Keyword(1) neural network  
Keyword(2) machine learning  
Keyword(3) weight update  
Keyword(4) hysteresis  
Keyword(5) Hysteresis Synapse  
Keyword(6) learning speed control  
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1st Author's Name Hidetoshi Nagai  
1st Author's Affiliation Kyushu Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2026-01-29 12:45:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NLP2025-103, MBE2025-43, NC2025-65 
Volume (vol) vol.125 
Number (no) no.344(NLP), no.345(MBE), no.346(NC) 
Page pp.80-85 
#Pages 6 
Date of Issue 2026-01-21 (NLP, MBE, NC) 

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