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Paper Abstract and Keywords
Presentation 2022-03-08 13:05
[Invited Talk] ---
Takashi Matsubara (Osaka Univ.) IBISML2021-34
Abstract (in Japanese) (See Japanese page) 
(in English) Deep learning is being considered as the most promising approach to building an artificial intelligence (AI) system; it sometimes recognizes and edits images and natural languages at a superhuman level. Given a sufficient amount of data and computational resources, deep learning can approximate an arbitrary function. However, deep learning makes decisions difficult to understand and control, and it is often described as ``unreliable''. The phrase ``AI is unreliable'' implies that ``non-AI approaches are reliable.'' In contrast to regular deep learning, mathematical models are designed to guarantee properties of targets, such as a dependency between factors, a geometric symmetry, and laws of physics. If deep learning guaranteed these properties, it would provide the same level of reliability as mathematical models. When replacing operations that compose deep learning appropriately, the function space to be approximated is restricted to a certain subset with desired properties, and the deep learning after training is guaranteed to have those properties. In fact, convolutional and graph neural networks have the translation and permutation equivariance, respectively. Geometric deep learning is a generalization these approaches, which guarantees various properties described using geometric concepts. Conservation laws of physical systems are associated with certain geometric symmetries and are included as objects of geometric deep learning. In this talk, the author will introduce geometric deep learning that guarantees various properties of dynamical systems, with a focus on recent publications by the author or his collaborators.
Keyword (in Japanese) (See Japanese page) 
(in English) geometric deep learning / geometric symmetry / dynamical system / conservation law / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 419, IBISML2021-34, pp. 27-27, March 2022.
Paper # IBISML2021-34 
Date of Issue 2022-03-01 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee IBISML  
Conference Date 2022-03-08 - 2022-03-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2022-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) --- 
Sub Title (in English)  
Keyword(1) geometric deep learning  
Keyword(2) geometric symmetry  
Keyword(3) dynamical system  
Keyword(4) conservation law  
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1st Author's Name Takashi Matsubara  
1st Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2022-03-08 13:05:00 
Presentation Time 40 minutes 
Registration for IBISML 
Paper # IBISML2021-34 
Volume (vol) vol.121 
Number (no) no.419 
Page p.27 
#Pages
Date of Issue 2022-03-01 (IBISML) 


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