1 code implementation • 26 Jul 2023 • Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev, Daniil Shlenskii, Akim Kotelnikov, Artem Babenko
Deep learning (DL) models for tabular data problems (e. g. classification, regression) are currently receiving increasingly more attention from researchers.
2 code implementations • 7 Jul 2022 • Ivan Rubachev, Artem Alekberov, Yury Gorishniy, Artem Babenko
Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT).
4 code implementations • 10 Mar 2022 • Yury Gorishniy, Ivan Rubachev, Artem Babenko
We start by describing two conceptually different approaches to building embedding modules: the first one is based on a piecewise linear encoding of scalar values, and the second one utilizes periodic activations.
11 code implementations • NeurIPS 2021 • Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets.