| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2026年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2025年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
2024年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
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| 研究開始時の研究の概要 |
Learning from multi-relational and multi-modal data, often represented as high-order tensors, stands as one of the significant challenges within machine learning community. Tensor Network decomposition (TND) offers a promising solution to address the curse of dimensionality in these scenarios. However, the existing tensor network decomposition is limited by a specific topology structure, which makes it difficult to mine the potential data structure. This project intends to break through this limitation and develop adaptive TND-based machine learning methods, theory, and its applications.
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