TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling

Yury Gorishniy, Akim Kotelnikov, Artem Babenko

Deep learning architectures for supervised learning on tabular data range from simple multilayer perceptrons (MLP) to sophisticated Transformers and retrieval-augmented methods. Th…