115 citations · 220 across the 11 of their papers we have counts for
3 papers · 2 filters
TabDDPM: Modelling Tabular Data with Diffusion Models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev +1
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the comp…
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…
On Embeddings for Numerical Features in Tabular Deep Learning
Yury Gorishniy, Ivan Rubachev, Artem Babenko
Recently, Transformer-like deep architectures have shown strong performance on tabular data problems. Unlike traditional models, e.g., MLP, these architectures map scalar values of…