| Revisiting deep learning models for tabular data Y Gorishniy, I Rubachev, V Khrulkov, A Babenko Advances in Neural Information Processing Systems 34, 18932-18943, 2021 | 826 | 2021 |
| Label-efficient semantic segmentation with diffusion models D Baranchuk, I Rubachev, A Voynov, V Khrulkov, A Babenko arXiv preprint arXiv:2112.03126, 2021 | 563 | 2021 |
| Tabddpm: Modelling tabular data with diffusion models A Kotelnikov, D Baranchuk, I Rubachev, A Babenko International Conference on Machine Learning, 17564-17579, 2023 | 264 | 2023 |
| On embeddings for numerical features in tabular deep learning Y Gorishniy, I Rubachev, A Babenko Advances in Neural Information Processing Systems 35, 24991-25004, 2022 | 171 | 2022 |
| Revisiting pretraining objectives for tabular deep learning I Rubachev, A Alekberov, Y Gorishniy, A Babenko arXiv preprint arXiv:2207.03208, 2022 | 50 | 2022 |
| Tabr: Unlocking the power of retrieval-augmented tabular deep learning Y Gorishniy, I Rubachev, N Kartashev, D Shlenskii, A Kotelnikov, ... arXiv preprint arXiv:2307.14338, 2023 | 41* | 2023 |
| TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks I Rubachev, N Kartashev, Y Gorishniy, A Babenko arXiv preprint arXiv:2406.19380, 2024 | 3* | 2024 |
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