28 citations · 35 across the 3 of their papers we have counts for
3 papers
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-…
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…
Label-Efficient Semantic Segmentation with Diffusion Models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov +2
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-t…