| 研究課題/領域番号 |
23K16939
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 筑波大学 |
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研究代表者 |
NGUYEN DAIHAI 筑波大学, システム情報系, 助教 (50968401)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
2,730千円 (直接経費: 2,100千円、間接経費: 630千円)
2025年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
2024年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
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| キーワード | Generative models / Constrained domains / optimal transport / graph kernels / graph structured data |
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| 研究開始時の研究の概要 |
We design novel measures for graphs with the following advantages:
1) taking into account node features and global structures of graphs.
2) deriving valid kernels that can be used for kernel-based frameworks.
3) reducing the complexity of computing the pairwise distance or kernel matrices.
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| 研究実績の概要 |
The research aims to develop optimal transport-based learning models for structured data, along with their related extensions and applications.
This year, our main focus has been on learning to generate structured data. Generating structured data, such as graphs, poses a significant challenge due to the constraints imposed on the generated samples. To address it, we formulated the problem of generating structured data as a distribution optimization problem on constrained domains. Then we developed a general framework based on the optimal transport theory to tackle this issue. We conducted an analysis of theoretical properties and convergence of the proposed, as well as experiments on synthetic and real world data sets to demonstrate its effectiveness in generating constrained samples.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
We have achieved promising results in the topics of learning to generate structured data, with our research findings being published in top conferences and journals. Moving forward, we plan to further extend our research focus to include graph data and its applications.
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| 今後の研究の推進方策 |
This year, our plan is to continue our efforts in developing optimal transport-based learning models tailored for graph-structured data, with a specific focus on their applications in Bioinformatics. Our research will encompass various domains within Bioinformatics, including molecular graphs, biological network data, and more.
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