| Automated evolutionary approach for the design of composite machine learning pipelines NO Nikitin, P Vychuzhanin, M Sarafanov, IS Polonskaia, I Revin, ... Future Generation Computer Systems 127, 109-125, 2022 | 51 | 2022 |
| Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea NO Nikitin, I Revin, A Hvatov, P Vychuzhanin, AV Kalyuzhnaya Computers & Geosciences 161, 105061, 2022 | 16 | 2022 |
| Structural evolutionary learning for composite classification models NO Nikitin, IS Polonskaia, P Vychuzhanin, IV Barabanova, ... Procedia computer science 178, 414-423, 2020 | 14 | 2020 |
| Multi-objective evolutionary design of composite data-driven models IS Polonskaia, NO Nikitin, I Revin, P Vychuzhanin, AV Kalyuzhnaya 2021 IEEE Congress on Evolutionary Computation (CEC), 926-933, 2021 | 11 | 2021 |
| Automatic evolutionary learning of composite models with knowledge enrichment AV Kalyuzhnaya, NO Nikitin, P Vychuzhanin, A Hvatov, A Boukhanovsky Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 11 | 2020 |
| Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration: SWAN wind wave model case study NO Nikitin, P Vychuzhanin, A Hvatov, I Deeva, AV Kalyuzhnaya, ... Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 10 | 2019 |
| Robust ensemble-based evolutionary calibration of the numerical wind wave model P Vychuzhanin, NO Nikitin, AV Kalyuzhnaya Computational Science–ICCS 2019: 19th International Conference, Faro …, 2019 | 5 | 2019 |
| Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks P Vychuzhanin, A Hvatov, AV Kalyuzhnaya Procedia Computer Science 136, 321-330, 2018 | 5 | 2018 |
| Sensitivity analysis of the composite data-driven pipelines in the automated machine learning IV Barabanova, P Vychuzhanin, NO Nikitin Procedia Computer Science 193, 484-493, 2021 | 4 | 2021 |
| ÒÅÕÍÎËÎÃÈÈ ÏÐÈÊËÀÄÍÎÃÎ ÈÑÊÓÑCÒÂÅÍÍÎÃÎ ÈÍÒÅËËÅÊÒÀ  ÇÀÄÀ×ÀÕ ×ÈÑËÅÍÍÎÃÎ ÌÎÄÅËÈÐÎÂÀÍÈß ÏÐÎÖÅÑÑΠ ÎÊÅÀÍÅ À Êàëþæíàÿ, ÍÎ Íèêèòèí, Ï Âû÷óæàíèí, ÀÀ Õâàòîâ Êîìïëåêñíûå èññëåäîâàíèÿ Ìèðîâîãî îêåàíà, 81-82, 2020 | | 2020 |
| REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Model P Vychuzhanin, NO Nikitin, AV Kalyuzhnaya arXiv preprint arXiv:1906.08587, 2019 | | 2019 |
| ÎÁÍÀÐÓÆÅÍÈÅ ÀÍÎÌÀËÈÉ Â ÐÅÇÓËÜÒÀÒÀÕ ÃÈÄÐÎÌÅÒÅÎÐÎËÎÃÈ×ÅÑÊÎÃÎ ÌÎÄÅËÈÐÎÂÀÍÈß Ñ ÈÑÏÎËÜÇÎÂÀÍÈÅÌ ÑÂÅÐÒÎ×ÍÛÕ ÍÅÉÐÎÍÍÛÕ ÑÅÒÅÉ Ï Âû÷óæàíèí, ÀÀ Õâàòîâ, À Êàëþæíàÿ Ñîâðåìåííûå ïðîáëåìû ãèäðîìåòåîðîëîãèè è óñòîé÷èâîãî ðàçâèòèÿ Ðîññèéñêîé …, 2019 | | 2019 |
Anna KalyuzhnayaITMO UniversityVerified email at niuitmo.ru
