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 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 |
IBISML2021-34 |
Conference Information |
Committee |
IBISML |
Conference Date |
2022-03-08 - 2022-03-09 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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(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) |
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(See Japanese page) |
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geometric deep learning |
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geometric symmetry |
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dynamical system |
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conservation law |
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Takashi Matsubara |
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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 |
1 |
Date of Issue |
2022-03-01 (IBISML) |
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