Summary of Tabm: Advancing Tabular Deep Learning with Parameter-efficient Ensembling, by Yury Gorishniy and Akim Kotelnikov and Artem Babenko
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
by Yury Gorishniy, Akim Kotelnikov, Artem Babenko
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 This study proposes a new deep learning architecture called TabM, which leverages efficient ensembling to improve performance and efficiency on tabular data. Unlike traditional deep ensembles, TabM trains multiple implicit MLPs simultaneously, sharing most parameters, resulting in better results and reduced computational costs. The authors evaluate TabM as a baseline against other architectures on public benchmarks, finding that it outperforms attention- and retrieval-based models while maintaining efficiency. They also analyze the ensemble-like nature of TabM, demonstrating that individual predictions are weak but collectively powerful. Overall, TabM is a simple yet effective model for tabular deep learning, suitable for both researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper talks about how to make better machines learn from tables of information. The authors create a new way to train these machines called TabM, which works really well and uses less energy than other methods. They compare TabM to other ways people have tried and find that it’s the best one so far. They also study why TabM is good at making predictions by looking at how it makes multiple guesses for each item. Overall, this new method called TabM is a great way to make machines learn from tables of information. |
Keywords
* Artificial intelligence * Attention * Deep learning
