Summary of Diffusion Model For Relational Inference, by Shuhan Zheng et al.
Diffusion model for relational inference
by Shuhan Zheng, Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new method called Diffusion model for Relational Inference (DiffRI) to uncover interaction relations in complex systems. By conditioning on observable dynamics, DiffRI learns to infer the probability of connection presence between components. This approach is inspired by self-supervised probabilistic time series imputation methods and has potential applications in fields such as brain activity analysis, financial market modeling, and physical collective behavior study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how complex systems work together. It’s like trying to figure out what makes a group of people move together or why the stock market goes up and down. The scientists developed a new way called DiffRI that can look at what’s happening in the system and guess which parts are connected to each other. This is important because it can help us understand things like how our brains work or why animals behave in certain ways. |
Keywords
* Artificial intelligence * Diffusion model * Inference * Probability * Self supervised * Time series
