Summary of Univariate Skeleton Prediction in Multivariate Systems Using Transformers, by Giorgio Morales et al.
Univariate Skeleton Prediction in Multivariate Systems Using Transformers
by Giorgio Morales, John W. Sheppard
First submitted to arxiv on: 25 Jun 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 A novel approach to explainable symbolic regression is proposed in this paper, which generates univariate symbolic skeletons that describe how each input variable influences a system’s response. The method uses artificial data sets with varying input variables to model relationships separately for each input variable, and then employs a pre-trained Multi-Set Transformer to solve the Multi-Set Skeleton Prediction problem. This approach is shown to outperform several existing methods in terms of learning skeleton expressions that match underlying functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve symbolic regression methods by allowing them to explain how different variables affect a system’s response. By using artificial data sets and a special type of neural network, the method can identify patterns and relationships between variables that previously were difficult to understand. The results show that this approach is effective in learning expressions that match real-world functions. |
Keywords
* Artificial intelligence * Neural network * Regression * Transformer
