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cs.LG2024
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…
cs.LG2024
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy +1
Advances in machine learning research drive progress in real-world applications. To ensure this progress, it is important to understand the potential pitfalls on the way from a nov…