Summary of Multi-view Symbolic Regression, by Etienne Russeil et al.
Multi-View Symbolic Regression
by Etienne Russeil, Fabrício Olivetti de França, Konstantin Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Instrumentation and Methods for Astrophysics (astro-ph.IM); Applications (stat.AP)
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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 Multi-View Symbolic Regression (MvSR) method tackles the challenge of symbolic regression in the presence of multiple datasets generated from different experiments. By fitting analytical expressions to each dataset simultaneously, MvSR can capture underlying relationships and recover known expressions or identify new ones. The authors demonstrate MvSR’s effectiveness on both synthetic and real-world datasets from astronomy, chemistry, and economics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MvSR is a new way to find math equations that describe how different things are connected. Right now, we usually only have one set of data from an experiment. But what if we have many sets of data from different experiments? That’s where MvSR comes in! It looks at all the data together and finds a single equation that fits each dataset. This helps us discover patterns and relationships between things. |
Keywords
* Artificial intelligence * Regression
