2016 Fiscal Year Final Research Report
Fast Optimal Transport and Applications to Inference and Simulation in Large Scale Statistical Machine Learning
Project/Area Number |
26700002
|
Research Category |
Grant-in-Aid for Young Scientists (A)
|
Allocation Type | Partial Multi-year Fund |
Research Field |
Statistical science
|
Research Institution | Kyoto University |
Principal Investigator |
CUTURI Marco 京都大学, 情報学研究科, 准教授 (80597344)
|
Project Period (FY) |
2014-04-01 – 2017-03-31
|
Keywords | 最適輸送理論 / 機械学習 / 最適化 / グラフィックス |
Outline of Final Research Achievements |
This funding was used to push forward the idea that optimal transport could be used numerically to solve real life problems using a regularization approach. We have demonstrated over the course of this project that these ideas were feasible, and have shown their applicability to a very wide range of applications, ranging from graphics and medical imaging to graphics and machine learning. These ideas were presented in top conferences and journals.
|
Free Research Field |
統計学
|