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Citations (Crossref Cited-By Service + Math-Net.Ru) |
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Articles
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| 1. |
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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| 2. |
A. Chertkov, O. Tsymboi, M. Pautov, I. Oseledets, “Translate your gibberish: black-box adversarial attack on machine translation systems”, Issledovaniya po prikladnoi matematike i informatike. II–2, Zap. nauchn. sem. POMI, 530, POMI, Spb., 2023, 96–112 |
| 3. |
V. S. Fanaskov, I. V. Oseledets, “Spectral neural operators”, Dokl. Math., 108:suppl. 2 (2023), S226–S232  |
| 4. |
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 |
| 5. |
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 |
| 6. |
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 |
| 7. |
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   |
| 8. |
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 |
| 9. |
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 |
| 10. |
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 |
| 11. |
N. L. Zamarashkin, I. V. Oseledets, E. E. Tyrtyshnikov, “New applications of matrix methods”, Comput. Math. Math. Phys., 61:5 (2021), 669–673 |
| 12. |
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 |
| 13. |
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 |
| 14. |
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 |
| 15. |
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 |
| 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. |
I. V. Oseledets, “Lower bounds for separable approximations of the Hilbert kernel”, Sb. Math., 198:3 (2007), 425–432 |
| 18. |
I. V. Oseledets, D. V. Savostyanov, “Minimization methods for approximating tensors and their comparison”, Comput. Math. Math. Phys., 46:10 (2006), 1641–1650 |
| 19. |
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 |
| 20. |
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 |
Preprints
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| 21. |
A. V. Chashchin, M. A. Botchev, I. V. Oseledets, G. V. Ovchinnikov, “Predicting dynamical system evolution with residual neural networks”, Keldysh Institute preprints, 2019 |
| 22. |
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 ; ; |
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