Summary of Visymre: Vision-guided Multimodal Symbolic Regression, by Da Li et al.
ViSymRe: Vision-guided Multimodal Symbolic Regression
by Da Li, Junping Yin, Jin Xu, Xinxin Li, Juan Zhang
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
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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 The proposed ViSymRe model integrates vision, symbol, and numeric modalities to enhance symbolic regression, allowing it to leverage the strengths of each. This multimodal approach improves various metrics of symbolic regression, including fitting effect, simplicity, and structural accuracy. The model also establishes a meta-learning framework that learns from historical experiences to efficiently solve new problems. Compared to traditional models, ViSymRe generates simpler and more structurally rational equations that facilitate accurate mechanism analysis and theoretical model development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach called ViSymRe that uses visual information to help find mathematical equations in data. This makes it easier to understand how things work together. The model combines three types of information: what we see, symbols, and numbers. It also learns from past experiences to solve problems quickly. The results show that this method is better than others at finding the right equation and being simple enough to understand. |
Keywords
* Artificial intelligence * Meta learning * Regression
