Algebraic Geometry
---> Gelfand Zeta Function
---> Schwartz Distribution
---> Empirical Process
---> Statistical Learning Theory,
which shall take you clear understanding deep learning process and further exploration.
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Dr. Sumio Watanabe is a professor emeritus at Institute of Science Tokyo (Former Tokyo Institute of Technology), a visiting senior researcher of RIKEN, and an IEICE fellow.
Recent Articles :
(1) Sumio Watanabe, Review and prospect of algebraic research in equivalent framework between statistical mechanics and machine learning theory, Reviews in Mathematical Physics. arxiv.org/abs/arxiv2406.10234v3
(2) S. Watanabe, Mathematical theory of Bayesian statistics for unknown information source. Philosophical Transactions of Royal Society A, doi.org/10.1098/rsta.2022.0151, 2023. https://arxiv.org/abs/2206.05630
(3) S. Watanabe, Recent advances in algebraic geometry and Bayesian statistics. Information Geometry, doi.org/10.1007/s41884-022-00083, 2022. https://arxiv.org/abs/2211.10049
(4) S. Watanabe, Information criteria and cross validation for Bayesian inference in regular and singular cases. Japanese Journal of Statistics and Data Science, vol.4, pp.1-19, 2021.
In the paper (1), algebraic research in learning theory is discussed on the equivalence between machine learning and statistical mechanics. In the paper (2), unknown uncertainty and statistical model in Bayesian statistics is studied. In the paper (3). algebro-geometric study of Bayesian statistics is introduced. In the paper (4), difference between information criteria and cross validation is explained.
Author : Sumio Watanabe