| Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria A Katrutsa, V Strijov Expert Systems with Applications 76, 1-11, 2017 | 144 | 2017 |
| Black-box learning of multigrid parameters A Katrutsa, T Daulbaev, I Oseledets Journal of Computational and Applied Mathematics 368, 112524, 2020 | 77* | 2020 |
| Interpolation technique to speed up gradients propagation in neural ODEs T Daulbaev, A Katrutsa, L Markeeva, J Gusak, A Cichocki, I Oseledets Advances in Neural Information Processing Systems 33, 16689-16700, 2020 | 45* | 2020 |
| Stress test procedure for feature selection algorithms AM Katrutsa, VV Strijov Chemometrics and Intelligent Laboratory Systems 142, 172-183, 2015 | 28 | 2015 |
| Towards understanding normalization in neural ODEs J Gusak, L Markeeva, T Daulbaev, A Katrutsa, A Cichocki, I Oseledets ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 19 | 2020 |
| Machine learning methods for estimation the indicators of phosphogypsum influence in soil MA Pukalchik, AM Katrutsa, D Shadrin, VA Terekhova, IV Oseledets Journal of Soils and Sediments 19, 2265-2276, 2019 | 18 | 2019 |
| Survey on Efficient Training of Large Neural Networks. J Gusak, D Cherniuk, A Shilova, A Katrutsa, D Bershatsky, X Zhao, ... IJCAI, 5494-5501, 2022 | 17* | 2022 |
| Follow the bisector: a simple method for multi-objective optimization A Katrutsa, D Merkulov, N Tursynbek, I Oseledets arXiv preprint arXiv:2007.06937, 2020 | 8 | 2020 |
| Fast, memory-efficient low-rank approximation of SimRank IV Oseledets, GV Ovchinnikov, AM Katrutsa Journal of Complex Networks 5 (1), 111-126, 2017 | 7 | 2017 |
| Robust selection of multicollinear features in forecasting RG Neichev, AM Katrutsa, VV Strizhov Industrial laboratory. Diagnostics of materials 82 (3), 68-74, 2016 | 6* | 2016 |
| Multiparticle Kalman filter for object localization in symmetric environments R Korkin, I Oseledets, A Katrutsa Expert Systems with Applications 237, 121408, 2024 | 5 | 2024 |
| Dynamic mode decomposition and deep learning for postharvest decay prediction in apples N Stasenko, D Shadrin, A Katrutsa, A Somov IEEE Transactions on Instrumentation and Measurement 72, 1-11, 2023 | 4 | 2023 |
| Extension of dynamic mode decomposition for dynamic systems with incomplete information based on t-model of optimal prediction A Katrutsa, S Utyuzhnikov, I Oseledets Journal of Computational Physics 476, 111913, 2023 | 4 | 2023 |
| Memory-Efficient Backpropagation through Large Linear Layers D Bershatsky, A Mikhalev, A Katrutsa, J Gusak, D Merkulov, I Oseledets arXiv preprint arXiv:2201.13195, 2022 | 4 | 2022 |
| NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer V Leplat, D Merkulov, A Katrutsa, D Bershatsky, O Tsymboi, I Oseledets arXiv preprint arXiv:2209.14937, 2022 | 3 | 2022 |
| Проблема мультиколлинеарности при выборе признаков в регрессионных задачах АМ Катруца, ВВ Стрижов Информационные технологии 21 (1), 8-18, 2015 | 3 | 2015 |
| Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise Y Dorn, A Katrutsa, I Latypov, A Pudovikov arXiv preprint arXiv:2402.07062, 2024 | 2 | 2024 |
| Meta-solver for neural ordinary differential equations J Gusak, A Katrutsa, T Daulbaev, A Cichocki, I Oseledets arXiv preprint arXiv:2103.08561, 2021 | 2 | 2021 |
| Practical shift choice in the shift-and-invert Krylov subspace evaluations of the matrix exponential A Katrutsa, M Botchev, I Oseledets arXiv preprint arXiv:1909.13059, 2019 | 2 | 2019 |
| How to optimize preconditioners for the conjugate gradient method: a stochastic approach A Katrutsa, M Botchev, G Ovchinnikov, I Oseledets arXiv preprint arXiv:1806.06045, 2018 | 2 | 2018 |
Ivan OseledetsAIRI; Skolkovo Institute of Science and TechnologyПодтвержден адрес электронной почты в домене skoltech.ru
