|Budget Amount *help
¥17,160,000 (Direct Cost: ¥13,200,000、Indirect Cost: ¥3,960,000)
Fiscal Year 2021: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2019: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
|Outline of Final Research Achievements
Deep learning currently plays a central role in machine learning and has shown high performance in many tasks. On the other hand, theoretical understanding of its principles has not progressed. Indeed, at the beginning of this research project, it was almost a black box. In order to change this situation, we have obtained the following research results on the principles of deep learning. (1) Compression based generalization error analysis of deep learning from the kernel method perspective, (2) Proposing a new method to obtain the optimal model structure based on statistical degrees of freedom and its application to model compression, (3) Proposal of new stochastic optimization methods, and (4) Theoretical proof of the superiority of deep learning over the kernel method and other classical methods. Through these studies, we have obtained many insights into the question of why deep learning is better than other methods.