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Maxim Rakhuba
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V Lebedev, Y Ganin, M Rakhuba, I Oseledets, V Lempitsky
arXiv preprint arXiv:1412.6553, 2014
10452014
Calculating vibrational spectra of molecules using tensor train decomposition
M Rakhuba, I Oseledets
The Journal of chemical physics 145 (12), 2016
642016
Fast multidimensional convolution in low-rank tensor formats via cross approximation
MV Rakhuba, IV Oseledets
SIAM Journal on Scientific Computing 37 (2), A565-A582, 2015
582015
T-basis: a compact representation for neural networks
A Obukhov, M Rakhuba, S Georgoulis, M Kanakis, D Dai, L Van Gool
International Conference on Machine Learning, 7392-7404, 2020
282020
QTT-finite-element approximation for multiscale problems I: model problems in one dimension
V Kazeev, I Oseledets, M Rakhuba, C Schwab
Advances in Computational Mathematics 43, 411-442, 2017
28*2017
Grid-based electronic structure calculations: The tensor decomposition approach
MV Rakhuba, IV Oseledets
Journal of Computational Physics 312, 19-30, 2016
232016
Speeding-up convolutional neural networks using fine-tuned cp-decomposition. arXiv 2014
V Lebedev, Y Ganin, M Rakhuba, I Oseledets, V Lempitsky
arXiv preprint arXiv:1412.6553, 0
20
Alternating least squares as moving subspace correction
IV Oseledets, MV Rakhuba, A Uschmajew
SIAM Journal on Numerical Analysis 56 (6), 3459-3479, 2018
192018
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians
M Rakhuba, A Novikov, I Oseledets
Journal of Computational Physics 396, 718-737, 2019
142019
Spectral tensor train parameterization of deep learning layers
A Obukhov, M Rakhuba, A Liniger, Z Huang, S Georgoulis, D Dai, ...
International Conference on Artificial Intelligence and Statistics, 3547-3555, 2021
122021
Jacobi--Davidson method on low-rank matrix manifolds
MV Rakhuba, IV Oseledets
SIAM Journal on Scientific Computing 40 (2), A1149-A1170, 2018
122018
Tensor rank bounds for point singularities in ℝ3
C Marcati, M Rakhuba, C Schwab
Advances in Computational Mathematics 48 (3), 18, 2022
112022
Quantized tensor FEM for multiscale problems: diffusion problems in two and three dimensions
V Kazeev, I Oseledets, MV Rakhuba, C Schwab
Multiscale Modeling & Simulation 20 (3), 893-935, 2022
92022
Robust discretization in quantized tensor train format for elliptic problems in two dimensions
AV Chertkov, IV Oseledets, MV Rakhuba
arXiv preprint arXiv:1612.01166, 2016
82016
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds
A Novikov, M Rakhuba, I Oseledets
SIAM Journal on Scientific Computing 44 (2), A843-A869, 2022
72022
Black-box solver for multiscale modelling using the QTT format
IV Oseledets, MV Rakhuba, AV Chertkov
Proc. ECCOMAS. Crete Island, Greece, 2016
72016
Towards practical control of singular values of convolutional layers
A Senderovich, E Bulatova, A Obukhov, M Rakhuba
Advances in Neural Information Processing Systems 35, 10918-10930, 2022
62022
Cherry-picking gradients: Learning low-rank embeddings of visual data via differentiable cross-approximation
M Usvyatsov, A Makarova, R Ballester-Ripoll, M Rakhuba, A Krause, ...
Proceedings of the IEEE/CVF International Conference on Computer Visioná…, 2021
62021
Robust solver in a quantized tensor format for three-dimensional elliptic problems
M Rakhuba
SAM Research Report 2019, 2019
6*2019
Tensor rank bounds and explicit QTT representations for the inverses of circulant matrices
L Vysotsky, M Rakhuba
Numerical Linear Algebra with Applications 30 (3), e2461, 2023
42023
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Articles 1–20