115 citations · 136 across the 6 of their papers we have counts for
6 papers
Benchmarking Optimizers for MLPs in Tabular Deep Learning
Yury Gorishniy, Ivan Rubachev, Dmitrii Feoktistov +1
MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models.…
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…
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…
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev +3
Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the rec…
Revisiting Pretraining Objectives for Tabular Deep Learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy +1
Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT). Unlike GBDT, deep models can additionally benefit from…
Revisiting Deep Learning Models for Tabular Data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov +1
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed mo…