Learning on tabular data



Learning for tabular data, i.e. the stuff you generally store in spreadsheets and relational databases.

Popular in many areas, notably recommender systems.

Not much here for now.

(Cheng et al. 2016): jrzaurin/pytorch-widedeep: A flexible package to combine tabular data with text and images using Wide and Deep models in Pytorch. pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM | infinitoml

Note that the author of that package advises using gradient boosting machines to get this job done.

References

Arik, Sercan O., and Tomas Pfister. 2019. “TabNet: Attentive Interpretable Tabular Learning,” August. https://arxiv.org/abs/1908.07442v5.
Cheng, Heng-Tze, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, et al. 2016. “Wide & Deep Learning for Recommender Systems.” arXiv:1606.07792 [cs, Stat], June. http://arxiv.org/abs/1606.07792.
Shwartz-Ziv, Ravid, and Amitai Armon. 2021. “Tabular Data: Deep Learning Is Not All You Need.” arXiv:2106.03253 [cs], June. http://arxiv.org/abs/2106.03253.

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