| Project/Area Number |
17K19989
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Information science, computer engineering, and related fields
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Taki Masato 国立研究開発法人理化学研究所, 数理創造プログラム, 上級研究員 (70548221)
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| Project Period (FY) |
2017-06-30 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | 機械学習 / 数理物理 / 数理工学 / 深層学習 |
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| Outline of Final Research Achievements |
The origin of the high performance of deep learning (generalization performance) is a big mystery. The goal of this project was to tackle it with a mathematical and applied approach. Various computer experiments related to deep learning were able to be carried out by the computer environment that was prepared by this grant project. As a result, we have accumulated practical know-how that contributes to the performance improvement of deep learning. Utilizing that know-how, we were able to conduct applied research on experimental science data, etc., while taking advantage of the strengths of machine learning. In that case, the practical side by the computer experiment was important, but the improvement and adjustment of the machine learning model by the mathematical analysis also played a big role.
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| Academic Significance and Societal Importance of the Research Achievements |
深層学習技術は、数理的側面と計算機科学的側面、そして応用を通じた実務的側面を持つが、その間の橋渡しを少しでもできるよう取り組んだ。科学における深層学習の応用研究はまだ始まって日が浅いが、共同研究者などをへ深層学習技術を提供することで、深層学習を使った科学研究が本邦でより進むよう本研究課題を進めた。また応用研究を通じて科学に適用する際の難しい点なども浮き彫りになり、それは今後の検討課題である。
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