Updated on 2025/04/17

写真b

 
TAKI Masato
 
*Items subject to periodic update by Rikkyo University (The rest are reprinted from information registered on researchmap.)
Affiliation*
Graduate School of Artificial Intelligence and Science Master's Program in Artificial Intelligence and Science
College of Science Department of Physics
Graduate School of Artificial Intelligence and Science Doctoral Program in Artificial Intelligence and Science
Title*
Associate Professor
Campus Career*
  • 4 2022 - Present 
    Graduate School of Artificial Intelligence and Science   Master's Program in Artificial Intelligence and Science   Associate Professor
  • 4 2022 - Present 
    Graduate School of Artificial Intelligence and Science   Doctoral Program in Artificial Intelligence and Science   Associate Professor
  • 4 2021 - Present 
    College of Science   Department of Physics   Associate Professor
  • 4 2021 - 3 2022 
    Graduate School of Artificial Intelligence and Science   Artificial Intelligence and Science   Associate Professor
  • 4 2020 - 3 2021 
    Graduate School of Artificial Intelligence and Science   Artificial Intelligence and Science   Specially Appointed Associate Professor
 

Research Projects

  • Mathematics and application of deep learning

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research 

    ▶ More details

    6 2022 - 3 2027

    Grant number：22H05116

    Grant amount：\101010000 （ Direct Cost: \77700000 、 Indirect Cost：\23310000 ）

    researchmap

  • Research on performance of deep learning performance based on random matrix theory

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research 

    Taki Masato

    ▶ More details

    6 2017 - 3 2020

    Grant number：17K19989

    Grant amount：\6240000 （ Direct Cost: \4800000 、 Indirect Cost：\1440000 ）

    The origin of the high performance of deep learning (generalization performance) is a big mystery. The goal of this project was to tackle it with a mathematical and applied approach. Various computer experiments related to deep learning were able to be carried out by the computer environment that was prepared by this grant project. As a result, we have accumulated practical know-how that contributes to the performance improvement of deep learning. Utilizing that know-how, we were able to conduct applied research on experimental science data, etc., while taking advantage of the strengths of machine learning. In that case, the practical side by the computer experiment was important, but the improvement and adjustment of the machine learning model by the mathematical analysis also played a big role.

    researchmap