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
|
Project Status |
Completed (Fiscal Year 2016)
|
Budget Amount *help |
¥25,090,000 (Direct Cost: ¥19,300,000、Indirect Cost: ¥5,790,000)
Fiscal Year 2016: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2015: ¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2014: ¥13,780,000 (Direct Cost: ¥10,600,000、Indirect Cost: ¥3,180,000)
|
Keywords | 最適輸送理論 / 機械学習 / 最適化 / グラフィックス / 統計学 / 統計的機械学習 / optimal transport / medical imaging / graphics / optimization |
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.
|
Report
(4 results)
Research Products
(23 results)