| Budget Amount *help |
¥25,090,000 (Direct Cost: ¥19,300,000、Indirect Cost: ¥5,790,000)
Fiscal Year 2019: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2017: ¥10,660,000 (Direct Cost: ¥8,200,000、Indirect Cost: ¥2,460,000)
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| Outline of Final Research Achievements |
In recent years, the development of generative models in the field of machine learning has been remarkable, reaching a level where images and videos of people who do not exist in reality are indistinguishable to the human eye. However, learning such generative models differs from learning ordinary discriminative models in that it often requires many heuristics in the learning method due to the instability of learning caused by the difficulty of optimizing the objective function and the non-triviality of formulation caused by the difficulty of evaluating the products. In this research, we have worked on elucidating and mitigating the learning instability, developing a generative model for tabular data, which has not been dealt with in existing research, and applying it to minority data analysis.
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