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 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
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.
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.
Richetti, Jonathan, Foivos I. Diakogianis, Asher Bender, André F. Colaço, and Roger A. Lawes. 2023. “A Methods Guideline for Deep Learning for Tabular Data in Agriculture with a Case Study to Forecast Cereal Yield.” Computers and Electronics in Agriculture 205 (February): 107642.
Shwartz-Ziv, Ravid, and Amitai Armon. 2021. “Tabular Data: Deep Learning Is Not All You Need.” arXiv:2106.03253 [Cs], June.
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