330 citations · 360 across the 5 of their papers we have counts for
5 papers
Quantized tensor FEM for multiscale problems: diffusion problems in two and three dimensions
V. Kazeev, I. Oseledets, M. Rakhuba +1
Homogenization in terms of multiscale limits transforms a multiscale problem with asymptotically separated microscales posed on a physical domain int…
Jacobi-Davidson method on low-rank matrix manifolds
Maxim Rakhuba, Ivan Oseledets
In this work we generalize the Jacobi-Davidson method to the case when eigenvector can be reshaped into a low-rank matrix. In this setting the proposed method inherits advantages o…
Grid-based electronic structure calculations: the tensor decomposition approach
Maxim Rakhuba, Ivan Oseledets
We present a fully grid-based approach for solving Hartree-Fock and all-electron Kohn-Sham equations based on low-rank approximation of three-dimensional electron orbitals. Due to…
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba +2
We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Gi…
Fast multidimensional convolution in low-rank formats via cross approximation
M. V. Rakhuba, I. V. Oseledets
We propose a new cross-conv algorithm for approximate computation of convolution in different low-rank tensor formats (tensor train, Tucker, Hierarchical Tucker). It has better com…