| Neural codes for image retrieval A Babenko, A Slesarev, A Chigorin, V Lempitsky European conference on computer vision, 584-599, 2014 | 1632 | 2014 |
| 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 | 1473 | 2021 |
| Aggregating local deep features for image retrieval A Babenko, V Lempitsky Proceedings of the IEEE international conference on computer vision, 1269-1277, 2015 | 1268* | 2015 |
| Label-efficient semantic segmentation with diffusion models D Baranchuk, I Rubachev, A Voynov, V Khrulkov, A Babenko ICLR'2022, 2022 | 818 | 2022 |
| The inverted multi-index A Babenko, V Lempitsky IEEE transactions on pattern analysis and machine intelligence 37 (6), 1247-1260, 2014 | 625 | 2014 |
| TabDDPM: Modelling Tabular Data with Diffusion Models A Kotelnikov, D Baranchuk, I Rubachev, A Babenko ICML'2023, 2023 | 596 | 2023 |
| Unsupervised discovery of interpretable directions in the gan latent space A Voynov, A Babenko International conference on machine learning, 9786-9796, 2020 | 536 | 2020 |
| Neural oblivious decision ensembles for deep learning on tabular data S Popov, S Morozov, A Babenko ICLR'2020, 2020 | 505 | 2020 |
| A critical look at the evaluation of GNNs under heterophily: are we really making progress? O Platonov, D Kuznedelev, M Diskin, A Babenko, L Prokhorenkova ICLR'2023, 2023 | 438 | 2023 |
| Additive quantization for extreme vector compression A Babenko, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 368 | 2014 |
| Efficient indexing of billion-scale datasets of deep descriptors A Babenko, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 351 | 2016 |
| On Embeddings for Numerical Features in Tabular Deep Learning Y Gorishniy, I Rubachev, A Babenko NeurIPS'2022, 2022 | 310 | 2022 |
| Editable neural networks A Sinitsin, V Plokhotnyuk, D Pyrkin, S Popov, A Babenko ICLR'2020, 2020 | 222 | 2020 |
| Extreme Compression of Large Language Models via Additive Quantization V Egiazarian, A Panferov, D Kuznedelev, E Frantar, A Babenko, D Alistarh ICML'2024, 2024 | 161 | 2024 |
| Revisiting the inverted indices for billion-scale approximate nearest neighbors D Baranchuk, A Babenko, Y Malkov Proceedings of the European Conference on Computer Vision (ECCV), 202-216, 2018 | 139 | 2018 |
| Tree quantization for large-scale similarity search and classification A Babenko, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 132 | 2015 |
| Characterizing graph datasets for node classification: Beyond homophily-heterophily dichotomy O Platonov, D Kuznedelev, A Babenko, L Prokhorenkova NeurIPS'2023, 2023 | 118* | 2023 |
| Results of the NeurIPS’21 challenge on billion-scale approximate nearest neighbor search HV Simhadri, G Williams, M Aumüller, M Douze, A Babenko, D Baranchuk, ... NeurIPS 2021 Competitions and Demonstrations Track, 177-189, 2022 | 118 | 2022 |
| TABR: TABULAR DEEP LEARNING MEETS NEAREST NEIGHBORS IN 2023 Y Gorishniy, I Rubachev, N Kartashev, D Shlenskii, A Kotelnikov, ... ICLR'2024, 2024 | 109* | 2024 |
| Non-metric similarity graphs for maximum inner product search S Morozov, A Babenko Advances in Neural Information Processing Systems 31, 2018 | 106 | 2018 |
