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Attribution
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
arXiv 2024
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
arXiv 2023
TabDDPM: Modelling Tabular Data with Diffusion Models
arXiv 2022
from 3 papers
Artem Babenko
Ivan Rubachev
Yury Gorishniy
Daniil Shlenskii
Dmitry Baranchuk
Nikolay Kartashev