| List of publications: |
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Citations (Crossref Cited-By Service + Math-Net.Ru) |
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| 1. |
S. A. Budennyy, V. D. Lazarev, N. N. Zakharenko, A. N. Korovin, O. A. Plosskaya, D. V. Dimitrov, V. S. Akhripkin, I. V. Pavlov, I. V. Oseledets, I. S. Barsola, I. V. Egorov, A. A. Kosterina, L. E. Zhukov, “eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI”, Dokl. Math., 106:suppl. 1 (2022), S118–S128    |
| 2. |
I. V. Oseledets, E. E. Tyrtyshnikov, “Approximate inversion of matrices in the process of solving a hypersingular integral equation”, Comput. Math. Math. Phys., 45:2 (2005), 302–313   |
| 3. |
V. S. Fanaskov, I. V. Oseledets, “Spectral neural operators”, Dokl. Math., 108:suppl. 2 (2023), S226–S232 |
| 4. |
I. V. Oseledets, D. V. Savostyanov, “Minimization methods for approximating tensors and their comparison”, Comput. Math. Math. Phys., 46:10 (2006), 1641–1650  |
| 5. |
I. V. Oseledets, “Lower bounds for separable approximations of the Hilbert kernel”, Sb. Math., 198:3 (2007), 425–432  |
| 6. |
S. A. Matveev, I. V. Oseledets, E. S. Ponomarev, A. V. Chertkov, “Overview of visualization methods for artificial neural networks”, Comput. Math. Math. Phys., 61:5 (2021), 887–899 |
| 7. |
J. V. Gusak, T. K. Daulbaev, I. V. Oseledets, E. S. Ponomarev, A. S. Cichocki, “Reduced-order modeling of deep neural networks”, Comput. Math. Math. Phys., 61:5 (2021), 774–785 |
| 8. |
I. V. Oseledets, “Use of Divided Differences and $B$ Splines for Constructing Fast Discrete Transforms of Wavelet Type on Nonuniform Grids”, Math. Notes, 77:5 (2005), 686–694 |
| 9. |
D. Yu. Turdakov, A. I. Avetisyan, K. V. Arkhipenko, A. V. Antsiferova, D. S. Vatolin, S. S. Volkov, A. V. Gasnikov, D. A. Devyatkin, M. D. Drobyshevskiy, A. P. Kovalenko, M. I. Krivonosov, N. V. Lukashevich, V. A. Malykh, S. I. Nikolenko, I. V. Oseledets, A. I. Perminov, I. V. Sochenkov, M. M. Tihomirov, A. N. Fedotov, M. Yu. Khachay, “Trusted artificial intelligence: challenges and promising solutions”, Dokl. Math., 106:suppl. 1 (2022), S9–S13 |
| 10. |
E. V. Burnaev, A. V. Bernshtein, V. V. Vanovskiy, A. A. Zaytsev, A. M. Bulkin, V. Yu. Ignatiev, D. G. Shadrin, S. V. Illarionova, I. V. Oseledets, A. Yu. Mikhalev, A. A. Osiptsov, A. A. Artemov, M. G. Sharaev, I. E. Trofimov, “Fundamental research and developments in the field of applied artificial intelligence”, Dokl. Math., 106:suppl. 1 (2022), S14–S22 |
| 11. |
Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew, “Local convergence of alternating low-rank optimizationmethods with overrelaxation”, Numer. Linear Algebra Appl., 30:3 (2023), 2459 , 15 pp.
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| 12. |
J. Math. Sci. (N. Y.), 285:2 (2024), 221–233 |
| 13. |
A. I. Boyko, I. V. Oseledets, G. Ferrer, “TT-QI: Faster value iteration in tensor train format for stochastic optimal control”, Comput. Math. Math. Phys., 61:5 (2021), 836–846 |
| 14. |
N. L. Zamarashkin, I. V. Oseledets, E. E. Tyrtyshnikov, “New applications of matrix methods”, Comput. Math. Math. Phys., 61:5 (2021), 669–673 |
| 15. |
A. V. Chashchin, M. A. Botchev, I. V. Oseledets, G. V. Ovchinnikov, “Predicting dynamical system evolution with residual neural networks”, Keldysh Institute preprints, 2019 |
| 16. |
I. V. Oseledets, S. L. Stavtsev, E. E. Tyrtyshnikov, “Integration of oscillating functions in a quasi-three-dimensional electrodynamic problem”, Comput. Math. Math. Phys., 49:2 (2009), 292–303 |
| 17. |
Daniil Merkulov, Daria Cherniuk, Alexander Rudikov, Ivan Oseledets, Ekaterina Muravleva, Aleksandr Mikhalev, Boris Kashin, “Quantization of large language models with an overdetermined basis”, Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Proc. Mach. Learn. Res. (PMLR), 244, 2024, 2527–2536 , arXiv: 2404.09737 |
| 18. |
I. V. Oseledets, P. V. Kharyuk, “Structuring data with block term decomposition: decomposition of joint tensors and variational block term decomposition as a parametrized mixture distribution model”, Comput. Math. Math. Phys., 61:5 (2021), 816–835 |
| 19. |
I. V. Oseledets, M. A. Botchev, A. M. Katrutsa, G. V. Ovchinnikov, “How to optimize preconditioners for the conjugate gradient method: a stochastic approach”, Keldysh Institute preprints, 2018 |
| 20. |
P. V. Kharyuk, I. V. Oseledets, V. L. Ushakov, “Compression of fMRI data using wavelet tensor train decomposition”, Num. Meth. Prog., 15:4 (2014), 669–676 |
| 21. |
P. V. Kharyuk, I. V. Oseledets, “WTT decomposition for the compression of array's families and its application to image processing”, Num. Meth. Prog., 15:2 (2014), 229–238 |
| 22. |
A. Yu. Mikhalev, I. V. Oferkin, I. V. Oseledets, A. V. Sulimov, E. E. Tyrtyshnikov, V. B. Sulimov, “Application of the multicharge approximation for large dense matrices in the framework of the polarized continuum solvent model”, Num. Meth. Prog., 15:1 (2014), 9–21 |
| 23. |
T. G. Saluev, I. V. Oseledets, R. Yu. Fadeev, “Web-framework for creation of interactive training courses on computational methods”, Artificial Intelligence and Decision Making, 2014, no. 1, 46–51 |
| 24. |
Comput. Math. Math. Phys., 65:3 (2025), 503–521 |
| 25. |
I. V. Oseledets, E. A. Muravleva, “K-optimal preconditioners based on approximations of inverse matrices”, Comput. Math. Math. Phys., 65:7 (2025), 1535–1547 |
| 26. |
B. S. Kashin, I. V. Oseledets, A. Rudikov, “Accelerated algorithm for splitting a vector into two vectors with small uniform norm”, Math. Notes, 118:3 (2025), 564–570 |
| 27. |
Comput. Math. Math. Phys., 64:4 (2024), 788–805 |
| 28. |
A. Zhavoronkin, M. Pautov, N. Kalmykov, E. Sevriugov, D. Kovalev, O. Y. Rogov, I. Oseledets, “UnGAN: machine unlearning strategies through membership inference”, Issledovaniya po prikladnoi matematike i informatike. IV, Zap. nauchn. sem. POMI, 540, POMI, SPb., 2024, 46–60 ; |
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