Summary of Portal: Scalable Tabular Foundation Models Via Content-specific Tokenization, by Marco Spinaci et al.
PORTAL: Scalable Tabular Foundation Models via Content-Specific Tokenization
by Marco Spinaci, Marek Polewczyk, Johannes Hoffart, Markus C. Kohler, Sam Thelin, Tassilo Klein
First submitted to arxiv on: 17 Oct 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 proposed PORTAL framework enables self-supervised learning on tabular data without requiring cleaning or preprocessing, making it scalable for pre-training datasets. By handling various data modalities, PORTAL can be effectively fine-tuned to match state-of-the-art methods on complex classification and regression tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PORTAL is a new way of learning from tables that doesn’t need special cleaning or preparation. It’s simple yet powerful and can be used with large datasets collected online. This helps with big tasks like classification and regression, making it a practical step forward for learning from tables. |
Keywords
* Artificial intelligence * Classification * Regression * Self supervised
