Follow
Maxim Rakhuba
Title
Cited by
Cited by
Year
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
8412014
Calculating vibrational spectra of molecules using tensor train decomposition
M Rakhuba, I Oseledets
The Journal of chemical physics 145 (12), 124101, 2016
512016
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
512015
Grid-based electronic structure calculations: The tensor decomposition approach
MV Rakhuba, IV Oseledets
Journal of Computational Physics 312, 19-30, 2016
222016
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
212020
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 (2), 411-442, 2017
21*2017
Alternating least squares as moving subspace correction
IV Oseledets, MV Rakhuba, A Uschmajew
SIAM Journal on Numerical Analysis 56 (6), 3459-3479, 2018
142018
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians
M Rakhuba, A Novikov, I Oseledets
Journal of Computational Physics 396, 718-737, 2019
122019
Jacobi--Davidson method on low-rank matrix manifolds
MV Rakhuba, IV Oseledets
SIAM Journal on Scientific Computing 40 (2), A1149-A1170, 2018
102018
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
9
Tensor Rank bounds for Point Singularities in
C Marcati, M Rakhuba, C Schwab
arXiv preprint arXiv:1912.07996, 2019
82019
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
72021
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
62016
Black-box solver for multiscale modelling using the QTT format
IV Oseledets, MV Rakhuba, AV Chertkov
Proc. ECCOMAS. Crete Island, Greece, 2016
62016
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
42022
Tensor rank bounds for point singularities in ℝ3
C Marcati, M Rakhuba, C Schwab
Advances in Computational Mathematics 48 (3), 1-57, 2022
42022
Robust solver in a quantized tensor format for three-dimensional elliptic problems
M Rakhuba
SAM Research Report 2019, 2019
4*2019
Low-rank tensor approximation of singularly perturbed boundary value problems in one dimension
C Marcati, M Rakhuba, JEM Ulander
Calcolo 59 (1), 1-32, 2022
22022
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
22021
Towards practical control of singular values of convolutional layers
A Senderovich, E Bulatova, A Obukhov, M Rakhuba
arXiv preprint arXiv:2211.13771, 2022
12022
The system can't perform the operation now. Try again later.
Articles 1–20