| Budget Amount *help |
¥12,870,000 (Direct Cost: ¥9,900,000、Indirect Cost: ¥2,970,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2019: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2018: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2017: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
|
|---|
| Outline of Final Research Achievements |
This project focuses on the feature representation problems for graph machine learning. By extending our previous work on sparse linear learning over the subgraph-feature search space, we developed novel related methods such as decision tree ensemble learning over subgraph search space, decision tree learning based on regarding the subgraph search space as a trie, efficient learning by stochastic search over subgraph space, graph learning by subgraph co-occurrences, compressing the subgraph search space by decision diagrams, dual graph convolutions for a graph of graphs, self-attentive graph learning for molecular property prediction, and user-edit aware generative graph autocompletion.
|
