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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.16755 |
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| _version_ | 1866909938954010624 |
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| author | Zheng, Shuhan Li, Ziqiang Fujiwara, Kantaro Tanaka, Gouhei |
| author_facet | Zheng, Shuhan Li, Ziqiang Fujiwara, Kantaro Tanaka, Gouhei |
| contents | Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components. The issue of uncovering interaction relations in such systems using observable dynamics is called relational inference. In this study, we propose a Diffusion model for Relational Inference (DiffRI), inspired by a self-supervised method for probabilistic time series imputation. DiffRI learns to infer the probability of the presence of connections between components through conditional diffusion modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion model for relational inference Zheng, Shuhan Li, Ziqiang Fujiwara, Kantaro Tanaka, Gouhei Machine Learning Artificial Intelligence Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components. The issue of uncovering interaction relations in such systems using observable dynamics is called relational inference. In this study, we propose a Diffusion model for Relational Inference (DiffRI), inspired by a self-supervised method for probabilistic time series imputation. DiffRI learns to infer the probability of the presence of connections between components through conditional diffusion modeling. |
| title | Diffusion model for relational inference |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16755 |