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
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
Calculating vibrational spectra of molecules using tensor train decomposition
M Rakhuba, I Oseledets
The Journal of chemical physics 145 (12), 124101, 2016
Grid-based electronic structure calculations: The tensor decomposition approach
MV Rakhuba, IV Oseledets
Journal of Computational Physics 312, 19-30, 2016
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
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians
M Rakhuba, A Novikov, I Oseledets
Journal of Computational Physics 396, 718-737, 2019
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
Jacobi--Davidson method on low-rank matrix manifolds
MV Rakhuba, IV Oseledets
SIAM Journal on Scientific Computing 40 (2), A1149-A1170, 2018
Alternating least squares as moving subspace correction
IV Oseledets, MV Rakhuba, A Uschmajew
SIAM Journal on Numerical Analysis 56 (6), 3459-3479, 2018
Tensor Rank bounds for Point Singularities in
C Marcati, M Rakhuba, C Schwab
arXiv preprint arXiv:1912.07996, 2019
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
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
Quantized tensor FEM for multiscale problems: diffusion problems in two and three dimensions
V Kazeev, I Oseledets, M Rakhuba, C Schwab
arXiv preprint arXiv:2006.01455, 2020
Robust solver in a quantized tensor format for three-dimensional elliptic problems
M Rakhuba
SAM Research Report 2019, 2019
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, ...
arXiv preprint arXiv:2105.14250, 2021
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds
A Novikov, M Rakhuba, I Oseledets
arXiv preprint arXiv:2103.14974, 2021
Low rank tensor approximation of singularly perturbed partial differential equations in one dimension
C Marcati, M Rakhuba, JEM Ulander
arXiv preprint arXiv:2010.06919, 2020
Tensor methods for high-dimensional eigenvalue problems
M Rakhuba
Numerical Analysis of Complex PDE Models in the Sciences, Workshop 1 …, 2018
Low-rank Riemannian optimization for high-dimensional eigenvalue problems
M Rakhuba
Swiss Numerics Day 2018, 2018
Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations
V Khrulkov, M Rakhuba, I Oseledets
2017 Progress In Electromagnetics Research Symposium-Spring (PIERS), 2334-2339, 2017
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