| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
In this research, we theoretically investigate a question why neural networks of large depth obtained by a machine learning method show significant performance for various tasks. We consider a particular type of neural networks, called threshold circuits, and then provide mathematical proofs which suggest that large depth contributes to the performance of threshold circuits that carry out (somewhat artificial) information processing. As part of the proofs, we also show detailed constructions (that is, placement of neural computational elements and their connections) of threshold circuits that are guaranteed to be achieve good performance for the information processing.
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