| Continuous-time convolutions model of event sequences V Zhuzhel, V Grabar, G Boeva, A Zabolotnyi, A Stepikin, V Zholobov, ... arXiv preprint arXiv:2302.06247, 2023 | 9 | 2023 |
| COHORTNEY: Non-parametric clustering of event sequences V Zhuzhel, R Rivera-Castro, N Kaploukhaya, L Mironova, A Zaytsev, ... arXiv preprint arXiv:2104.01440, 2021 | 5 | 2021 |
| Cotode: Continuous trajectory neural ordinary differential equations for modelling event sequences I Kuleshov, G Boeva, V Zhuzhel, E Romanenkova, E Vorsin, A Zaytsev arXiv preprint arXiv:2408.08055, 2024 | 3 | 2024 |
| No two users are alike: Generating audiences with neural clustering for temporal point processes V Zhuzhel, V Grabar, N Kaploukhaya, R Rivera-Castro, L Mironova, ... Doklady Mathematics 108 (Suppl 2), S511-S528, 2023 | 3 | 2023 |
| COTIC: Embracing Non-Uniformity in Event Sequence Data via Multilayer Continuous Convolution V Zhuzhel, V Grabar, G Boeva, A Stepikin, A Zabolotnyi, V Zholobov, ... IEEE Access 13, 210741-210757, 2025 | | 2025 |
| DeNOTS: Stable Deep Neural ODEs for Time Series I Kuleshov, E Romanenkova, V Zhuzhel, G Boeva, E Vorsin, A Zaytsev arXiv preprint arXiv:2408.08055, 2024 | | 2024 |
