Summary of Tabularfm: An Open Framework For Tabular Foundational Models, by Quan M. Tran et al.
TabularFM: An Open Framework For Tabular Foundational Models
by Quan M. Tran, Suong N. Hoang, Lam M. Nguyen, Dzung Phan, Hoang Thanh Lam
First submitted to arxiv on: 14 Jun 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 Foundational models (FMs) are trained on vast datasets using self-supervised techniques, allowing them to learn general patterns and reduce the need for labeled data. This research primarily focused on unstructured or semi-structured data, but structured data like tabular data remains understudied due to a lack of clean datasets. To address this gap, we introduce TabularFM, incorporating state-of-the-art methods for developing FMs specifically for tabular data. We’ve curated a million datasets and released cleaned versions to facilitate development. Our framework pretrains models on these datasets, benchmarks various learning methods, and releases leaderboards for future studies. By releasing datasets, pretrained models, and leaderboards, we aim to enhance the validity and usability of tabular FMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TabularFM is a new way to train machines to learn from tables. Usually, people focus on things like words or pictures, but not tables! To fix this, we created a special tool that uses really cool math (like GANs, VAEs, and Transformers) to teach machines about tables. We even made a huge collection of table datasets and cleaned them up so others can use them too. Then, we trained our machine learning models on these datasets and compared how well they worked. Finally, we shared all this information with the world so that other people can learn from it too! |
Keywords
* Artificial intelligence * Machine learning * Self supervised
