| Gtapprox: Surrogate modeling for industrial design M Belyaev, E Burnaev, E Kapushev, M Panov, P Prikhodko, D Vetrov, ... Advances in Engineering Software 102, 29-39, 2016 | 69 | 2016 |
| Quadrature-based features for kernel approximation M Munkhoeva, Y Kapushev, E Burnaev, I Oseledets Advances in neural information processing systems 31, 2018 | 66 | 2018 |
| Scaling transformer to 1m tokens and beyond with rmt A Bulatov, Y Kuratov, Y Kapushev, MS Burtsev arXiv preprint arXiv:2304.11062, 2023 | 62 | 2023 |
| Gaussian process regression for structured data sets M Belyaev, E Burnaev, Y Kapushev Statistical Learning and Data Sciences: Third International Symposium, SLDS …, 2015 | 36 | 2015 |
| Computationally efficient algorithm for Gaussian Process regression in case of structured samples M Belyaev, E Burnaev, Y Kapushev Computational Mathematics and Mathematical Physics 56, 499-513, 2016 | 26 | 2016 |
| Large-scale shape retrieval with sparse 3d convolutional neural networks A Notchenko, Y Kapushev, E Burnaev Analysis of Images, Social Networks and Texts: 6th International Conference …, 2018 | 25 | 2018 |
| Exact inference for gaussian process regression in case of big data with the cartesian product structure M Belyaev, E Burnaev, Y Kapushev arXiv preprint arXiv:1403.6573, 2014 | 20 | 2014 |
| Building data fusion surrogate models for spacecraft aerodynamic problems with incomplete factorial design of experiments M Belyaev, E Burnaev, E Kapushev, S Alestra, M Dormieux, A Cavailles, ... Advanced Materials Research 1016, 405-412, 2014 | 17 | 2014 |
| Sparse 3d convolutional neural networks for large-scale shape retrieval A Notchenko, E Kapushev, E Burnaev arXiv preprint arXiv:1611.09159, 2016 | 9 | 2016 |
| Tensor Completion via Gaussian Process--Based Initialization Y Kapushev, I Oseledets, E Burnaev SIAM Journal on Scientific Computing 42 (6), A3812-A3824, 2020 | 6 | 2020 |
| Beyond attention: Breaking the limits of transformer context length with recurrent memory A Bulatov, Y Kuratov, Y Kapushev, M Burtsev Proceedings of the AAAI Conference on Artificial Intelligence 38 (16), 17700 …, 2024 | 5 | 2024 |
| Accurate fetal variant calling in the presence of maternal cell contamination E Nabieva, SM Sharma, Y Kapushev, SK Garushyants, AV Fedotova, ... European Journal of Human Genetics 28 (11), 1615-1623, 2020 | 5 | 2020 |
| Random fourier features based slam Y Kapushev, A Kishkun, G Ferrer, E Burnaev 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 3 | 2021 |
| Вычислительно эффективный алгоритм построения регрессии на основе гауссовских процессов в случае структурированных выборок МГ Беляев, ЕВ Бурнаев, ЕР Капушев Журнал вычислительной математики и математической физики 56 (4), 507-522, 2016 | 3 | 2016 |
| Denoising Score Matching via Random Fourier Features O Tsymboi, Y Kapushev, E Burnaev, I Oseledets IEEE Access 10, 34154-34169, 2022 | 1 | 2022 |
| Denoising Score Matching with Random Fourier Features T Olga, Y Kapushev, E Burnaev, I Oseledets arXiv preprint arXiv:2101.05239, 2021 | | 2021 |
| Surrogate models for spacecraft aerodynamic problems M Belyaev, E Burnaev, E Kapushev, S Alestra, M Dormieux, A Cavailles, ... 11th World Congress on Computational Mechanics, WCCM 2014, 5th European …, 2014 | | 2014 |
| Lean4trace: Data augmentation for neural theorem proving in Lean V Nesterov, Y Kapushev, M Burtsev AI for Math Workshop@ ICML 2024, 0 | | |
| Quadrature-based features for kernel approximation. Supplementary materials. M Munkhoeva, Y Kapushev, E Burnaev, I Oseledets | | |
| Суррогатное моделирование и оптимизациÿ профилÿ крыла самолета на основе гауссовских процессов Е Бурнаев, П Ерофеев, А Зайцев, Д Кононенко, Е Капушев | | |
