| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
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)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
The aim of this research is to find a mathematical and information theoretical background of deep learning systems and to obtain effective algorithms for them in terms of the techniques in the probabilistic information processing and in the information statistical mechanics. We obtained the following results during this research period. (1) clarifying a mathematical background of the pre-training in deep Boltzmann machines, (2) a novel general theorem for restricted Boltzmann machines (RBMs) that states that, when mean-field methods are employed, inference results obtained from marginalized models are more accurate than those obtained from original models, and (3) a very fast test method for noise robustness of deep neural networks.
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