| Deep elastic strain engineering of bandgap through machine learning Z Shi, E Tsymbalov, M Dao, S Suresh, A Shapeev, J Li Proceedings of the National Academy of Sciences 116 (10), 4117-4122, 2019 | 96 | 2019 |
| Dropout-based active learning for regression E Tsymbalov, M Panov, A Shapeev Analysis of Images, Social Networks and Texts: 7th International Conference …, 2018 | 64 | 2018 |
| How certain is your Transformer? A Shelmanov, E Tsymbalov, D Puzyrev, K Fedyanin, A Panchenko, ... Proceedings of the 16th Conference of the European Chapter of the …, 2021 | 43 | 2021 |
| Metallization of diamond Z Shi, M Dao, E Tsymbalov, A Shapeev, J Li, S Suresh Proceedings of the National Academy of Sciences 117 (40), 24634-24639, 2020 | 38 | 2020 |
| Uncertainty estimation of transformer predictions for misclassification detection A Vazhentsev, G Kuzmin, A Shelmanov, A Tsvigun, E Tsymbalov, ... Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 29 | 2022 |
| Deeper connections between neural networks and Gaussian processes speed-up active learning E Tsymbalov, S Makarychev, A Shapeev, M Panov arXiv preprint arXiv:1902.10350, 2019 | 27 | 2019 |
| Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass E Tsymbalov, Z Shi, M Dao, S Suresh, J Li, A Shapeev npj Computational Materials 7 (1), 76, 2021 | 26 | 2021 |
| Active learning and uncertainty estimation A Shapeev, K Gubaev, E Tsymbalov, E Podryabinkin Machine Learning Meets Quantum Physics, 309-329, 2020 | 21 | 2020 |
| User-assisted log analysis for quality control of distributed fintech applications I Itkin, A Gromova, A Sitnikov, D Legchikov, E Tsymbalov, R Yavorskiy, ... 2019 IEEE International Conference On Artificial Intelligence Testing …, 2019 | 15 | 2019 |
| Compact difference scheme for parabolic and Schrödinger-type equations with variable coefficients VA Gordin, EA Tsymbalov Journal of Computational Physics 375, 1451-1468, 2018 | 15 | 2018 |
| Dropout strikes back: Improved uncertainty estimation via diversity sampled implicit ensembles E Tsymbalov, K Fedyanin, M Panov CoRR, abs, 2020 | 12* | 2020 |
| Compact difference schemes for the diffusion and Schrödinger equations. Approximation, stability, convergence, effectiveness, monotony VA Gordin, EA Tsymbalov Journal of Computational Mathematics, 348-370, 2014 | 12 | 2014 |
| Climategpt: Towards ai synthesizing interdisciplinary research on climate change D Thulke, Y Gao, P Pelser, R Brune, R Jalota, F Fok, M Ramos, I van Wyk, ... arXiv preprint arXiv:2401.09646, 2024 | 6 | 2024 |
| Churn prediction for game industry based on cohort classification ensemble E Tsymbalov | 5 | 2016 |
| Fact-checking the output of large language models via token-level uncertainty quantification E Fadeeva, A Rubashevskii, A Shelmanov, S Petrakov, H Li, H Mubarak, ... arXiv preprint arXiv:2403.04696, 2024 | 4 | 2024 |
| A fourth-order accurate difference scheme for a differential equation with variable coefficients VA Gordin, EA Tsymbalov Mathematical Models and Computer Simulations 10, 79-88, 2018 | 4 | 2018 |
| Compact difference scheme for the differential equation with piecewise-constant coefficient VA Gordin, EA Tsymbalov Matematicheskoe modelirovanie 29 (12), 16-28, 2017 | 4 | 2017 |
| Compact difference schemes for weakly-nonlinear parabolic and Schrodinger-type equations and systems V Gordin, E Tsymbalov arXiv preprint arXiv:1712.05185, 2017 | 3 | 2017 |
| Elastic strain engineering of materials M Dao, J Li, SHI Zhe, E Tsymbalov, A Shapeev, S Suresh US Patent App. 17/283,949, 2021 | 2 | 2021 |
| 4 order difference scheme for the differential equation with variable coefficients VA Gordin, EA Tsymbalov Matematicheskoe modelirovanie 29 (7), 3-14, 2017 | 2 | 2017 |
Alexander ShapeevSkolkovo Institute of Science and Technology在 shapeev.com 的电子邮件经过验证
