| Project/Area Number |
18K19793
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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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| Review Section |
Medium-sized Section 60:Information science, computer engineering, and related fields
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
Fukumizu Kenji 統計数理研究所, 数理・推論研究系, 教授 (60311362)
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| Co-Investigator(Kenkyū-buntansha) |
鈴木 大慈 東京大学, 大学院情報理工学系研究科, 准教授 (60551372)
今泉 允聡 東京大学, 大学院総合文化研究科, 准教授 (90814088)
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| Project Period (FY) |
2018-06-29 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2020: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2019: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2018: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 人工知能 / 深層学習 / 機械学習 / ニューラルネットワーク / 理論解析 |
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| Outline of Final Research Achievements |
This study theoretically analyzes the learning dynamics of deep neural networks with mathematical approaches. We have obtained the following results; (1) In estimating non-smooth functions, the deep neural networks have advantages over conventional models with fixed bases, (2) The sdalle point structure has been revelaed in the case that a network has surplus hidden units (3) Sufficient conditions have been obtained for the stability of deep generative models. Also, we have developed a meta-learing method for estimating causal directions with a small number of data.
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| Academic Significance and Societal Importance of the Research Achievements |
深層学習は応用面からの成功により現在の人工知能の基盤技術となっているが，モデルが強い非線形性を持つことから理論的な解析を行うことは容易ではなくブラックボックスとして使われる場合が多い．本研究は数理的手法で深層学習の性質を理論的に明らかにするものであり，ブラックボックスを超えた深層学習の理論，特に学習によって得られたネットワークの信頼性や，学習アルゴリズムの安定性に関して重要な知見が得られた．
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