| Presentation | 2022-03-08 [Invited Talk] --- Takashi Matsubara, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | geometric deep learning / geometric symmetry / dynamical system / conservation law |
| Paper # | IBISML2021-34 |
| Date of Issue | 2022-03-01 (IBISML) |
| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2022/3/8(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning, etc. |
| Chair | Ichiro Takeuchi(Nagoya Inst. of Tech.) |
| Vice Chair | Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Masashi Sugiyama(Univ. of Tokyo) |
| Assistant | Tomoharu Iwata(NTT) / Atsuyoshi Nakamura(Hokkaido Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Invited Talk] --- |
| Sub Title (in English) | |
| Keyword(1) | geometric deep learning |
| Keyword(2) | geometric symmetry |
| Keyword(3) | dynamical system |
| Keyword(4) | conservation law |
| 1st Author's Name | Takashi Matsubara |
| 1st Author's Affiliation | Osaka University(Osaka Univ.) |
| Date | 2022-03-08 |
| Paper # | IBISML2021-34 |
| Volume (vol) | vol.121 |
| Number (no) | IBISML-419 |
| Page | pp.pp.27-27(IBISML), |
| #Pages | 1 |
| Date of Issue | 2022-03-01 (IBISML) |