9 citations · 15 across the 4 of their papers we have counts for
5 papers
TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
Yury Gorishniy, Akim Kotelnikov, Ivan Rubachev +1
In deep learning for tabular data, efficient ensembles of multilayer perceptrons (MLPs) have recently emerged as effective and practical architectures. Existing methods of this kin…
On Finetuning Tabular Foundation Models
Ivan Rubachev, Akim Kotelnikov, Nikolay Kartashev +1
Foundation models are an emerging research direction in tabular deep learning. Notably, TabPFNv2 recently claimed superior performance over traditional GBDT-based methods on small-…
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…
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…
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…