| Reinforcement learning for combinatorial optimization: A survey N Mazyavkina, S Sviridov, S Ivanov, E Burnaev Computers & Operations Research 134, 105400, 2021 | 691 | 2021 |
| ABC: A Big CAD Model Dataset For Geometric Deep Learning S Koch, A Matveev, Z Jiang, F Williams, A Artemov, E Burnaev, M Alexa, ... CVPR Proc., 9601-9611, 2019 | 512 | 2019 |
| Steganographic generative adversarial networks D Volkhonskiy, I Nazarov, E Burnaev 12th Int. Conf. on Machine Vision (ICMV 2019) 11433, 114333M, 2020 | 261 | 2020 |
| Anonymous Walk Embeddings S Ivanov, E Burnaev ICML Proc.; arXiv:1805.11921, 2018 | 234 | 2018 |
| Driving digital rock towards machine learning: predicting permeability with gradient boosting and deep neural networks O Sudakov, E Burnaev, D Koroteev Computers & geosciences 127, 91-98, 2019 | 217 | 2019 |
| NAS-Bench-NLP: neural architecture search benchmark for natural language processing N Klyuchnikov, I Trofimov, E Artemova, M Salnikov, M Fedorov, A Filippov, ... IEEE Access 10, 45736-45747, 2022 | 132 | 2022 |
| Latent Video Transformer R Rakhimov, D Volkhonskiy, A Artemov, D Zorin, E Burnaev VISAPP 2020; arXiv:2006.10704, 2020 | 124 | 2020 |
| Boundary loss for remote sensing imagery semantic segmentation A Bokhovkin, E Burnaev Advances in Neural Networks–ISNN 2019: 16th International Symposium on …, 2019 | 123 | 2019 |
| Influence of resampling on accuracy of imbalanced classification E Burnaev, P Erofeev, A Papanov Eighth International Conference on Machine Vision, 987521-987521-5, 2015 | 120 | 2015 |
| A predictive model for steady-state multiphase pipe flow: Machine learning on lab data EA Kanin, AA Osiptsov, AL Vainshtein, EV Burnaev Journal of Petroleum Science and Engineering 180, 727-746, 2019 | 112 | 2019 |
| Wasserstein-2 Generative Networks A Korotin, V Egiazarian, A Asadulaev, A Safin, E Burnaev ICLR 2019; arXiv:1909.13082, 2019 | 103 | 2019 |
| One-class SVM with privileged information and its application to malware detection E Burnaev, D Smolyakov Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on …, 2016 | 97 | 2016 |
| Neural Optimal Transport A Korotin, D Selikhanovych, E Burnaev arXiv preprint arXiv:2201.12220, 2022 | 79 | 2022 |
| Large-scale wasserstein gradient flows P Mokrov, A Korotin, L Li, A Genevay, JM Solomon, E Burnaev Advances in Neural Information Processing Systems 34, 15243-15256, 2021 | 79 | 2021 |
| Data-driven model for the identification of the rock type at a drilling bit N Klyuchnikov, A Zaytsev, A Gruzdev, G Ovchinnikov, K Antipova, ... Journal of Petroleum science and Engineering 178, 506-516, 2019 | 79 | 2019 |
| Esports Athletes and Players: a Comparative Study N Khromov, A Korotin, A Lange, A Stepanov, E Burnaev, A Somov IEEE Pervasive Computing 18 (3), 31-39, 2019 | 77 | 2019 |
| Conformal -NN Anomaly Detector for Univariate Data Streams V Ishimtsev, A Bernstein, E Burnaev, I Nazarov Conformal and Probabilistic Prediction and Applications, 213-227, 2017 | 75 | 2017 |
| Do neural optimal transport solvers work? a continuous wasserstein-2 benchmark A Korotin, L Li, A Genevay, JM Solomon, A Filippov, E Burnaev Advances in Neural Information Processing Systems 34, 14593-14605, 2021 | 74 | 2021 |
| Monocular 3D Object Detection via Geometric Reasoning on Keypoints I Barabanau, A Artemov, E Burnaev, V Murashkin VISAPP 2019; arXiv:1905.05618, 2019 | 74 | 2019 |
| Generative Modeling with Optimal Transport Maps L Rout, A Korotin, E Burnaev arXiv preprint arXiv:2110.02999, 2021 | 73 | 2021 |
Denis ZorinProfessor of Computer Science and Mathematics, Courant Institute, NYUVerified email at cs.nyu.edu
