Summary of Why Tabular Foundation Models Should Be a Research Priority, by Boris Van Breugel et al.
Why Tabular Foundation Models Should Be a Research Priority
by Boris van Breugel, Mihaela van der Schaar
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 abstract proposes a shift in the machine learning research community’s priorities towards developing foundation models for tabular data, which is the dominant modality in many fields but has received little attention. The authors suggest that Large Tabular Models (LTMs) could revolutionize how science and ML use tabular data by contextualizing it with respect to related datasets. This could have far-reaching impacts such as few-shot tabular models, automating data science, out-of-distribution synthetic data, and empowering multidisciplinary scientific discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper suggests that the current focus on text and image foundation models is not enough, and it’s time to prioritize tabular data instead. The authors propose developing “Large Tabular Models” (LTMs) which could change how we use tabular data in science and ML. This could be very useful for many fields where tabular data is common. |
Keywords
» Artificial intelligence » Attention » Few shot » Machine learning » Synthetic data
