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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
9872014
Calculating vibrational spectra of molecules using tensor train decomposition
M Rakhuba, I Oseledets
The Journal of chemical physics 145 (12), 2016
602016
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
572015
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
252020
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
23*2017
Grid-based electronic structure calculations: The tensor decomposition approach
MV Rakhuba, IV Oseledets
Journal of Computational Physics 312, 19-30, 2016
232016
Alternating least squares as moving subspace correction
IV Oseledets, MV Rakhuba, A Uschmajew
SIAM Journal on Numerical Analysis 56 (6), 3459-3479, 2018
182018
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians
M Rakhuba, A Novikov, I Oseledets
Journal of Computational Physics 396, 718-737, 2019
142019
Jacobi--Davidson method on low-rank matrix manifolds
MV Rakhuba, IV Oseledets
SIAM Journal on Scientific Computing 40 (2), A1149-A1170, 2018
122018
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
12
Tensor rank bounds for point singularities in ℝ3
C Marcati, M Rakhuba, C Schwab
Advances in Computational Mathematics 48 (3), 18, 2022
112022
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
112021
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
82022
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
72016
Black-box solver for multiscale modelling using the QTT format
IV Oseledets, MV Rakhuba, AV Chertkov
Proc. ECCOMAS. Crete Island, Greece, 2016
62016
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
52021
Robust solver in a quantized tensor format for three-dimensional elliptic problems
M Rakhuba
SAM Research Report 2019, 2019
5*2019
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
42022
Low-rank tensor approximation of singularly perturbed boundary value problems in one dimension
C Marcati, M Rakhuba, JEM Ulander
Calcolo 59 (1), 2, 2022
32022
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
22023
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