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Attribution
TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
arXiv 2024
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
arXiv 2023
from 3 papers
Artem Babenko
Akim Kotelnikov
Ivan Rubachev
Nikolay Kartashev
Daniil Shlenskii