Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition. - Vadim Lebedev; Yaroslav Ganin; Maksim Rakhuba; Ivan V. Oseledets; Victor S. Lempitsky (ICLR (Poster), 2015)

Fast Multidimensional Convolution in Low-Rank Tensor Formats via Cross Approximation. - M. V. Rakhuba; Ivan V. Oseledets (SIAM J. Sci. Comput., 2015)

Grid-based electronic structure calculations: The tensor decomposition approach. - M. V. Rakhuba; Ivan V. Oseledets (J. Comput. Phys., 2016)

QTT-finite-element approximation for multiscale problems I: model problems in one dimension. - Vladimir A. Kazeev; Ivan V. Oseledets; Maksim Rakhuba; Christoph Schwab (Adv. Comput. Math., 2017)

Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations. - Valentin Khrulkov; M. V. Rakhuba; Ivan V. Oseledets (CoRR, 2017)

Alternating Least Squares as Moving Subspace Correction. - Ivan V. Oseledets; Maxim V. Rakhuba; André Uschmajew (SIAM J. Numer. Anal., 2018)

Jacobi-Davidson Method on Low-Rank Matrix Manifolds. - M. V. Rakhuba; Ivan V. Oseledets (SIAM J. Sci. Comput., 2018)

Low-rank Riemannian eigensolver for high-dimensional Hamiltonians. - Maxim V. Rakhuba; Alexander Novikov 001; Ivan V. Oseledets (J. Comput. Phys., 2019)

T-Basis: a Compact Representation for Neural Networks. - Anton Obukhov; Maxim V. Rakhuba; Stamatios Georgoulis; Menelaos Kanakis; Dengxin Dai; Luc Van Gool (ICML, 2020)

Low rank tensor approximation of singularly perturbed partial differential equations in one dimension. - Carlo Marcati; Maxim V. Rakhuba; Johan E. M. Ulander (CoRR, 2020)

Spectral Tensor Train Parameterization of Deep Learning Layers. - Anton Obukhov; Maxim V. Rakhuba; Alexander Liniger; Zhiwu Huang; Stamatios Georgoulis; Dengxin Dai; Luc Van Gool (AISTATS, 2021)

Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. - Mikhail Usvyatsov; Anastasia Makarova; Rafael Ballester-Ripoll; Maxim V. Rakhuba; Andreas Krause 001; Konrad Schindler (ICCV, 2021)

Robust Alternating Direction Implicit Solver in Quantized Tensor Formats for a Three-Dimensional Elliptic PDE. - Maxim V. Rakhuba (SIAM J. Sci. Comput., 2021)

Towards Practical Control of Singular Values of Convolutional Layers. - Alexandra Senderovich; Ekaterina Bulatova; Anton Obukhov; Maxim V. Rakhuba (NeurIPS, 2022)

Tensor rank bounds for point singularities in ℝ3. - Carlo Marcati; Maksim Rakhuba; Christoph Schwab (Adv. Comput. Math., 2022)

Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds. - Alexander Novikov 001; Maxim V. Rakhuba; Ivan V. Oseledets (SIAM J. Sci. Comput., 2022)

Quantized Tensor FEM for Multiscale Problems: Diffusion Problems in Two and Three Dimensions. - Vladimir A. Kazeev; Ivan V. Oseledets; Maxim V. Rakhuba; Christoph Schwab (Multiscale Model. Simul., 2022)

Local convergence of alternating low-rank optimization methods with overrelaxation. - Ivan V. Oseledets; Maxim V. Rakhuba; André Uschmajew (Numer. Linear Algebra Appl., 2023)

Tensor rank bounds and explicit QTT representations for the inverses of circulant matrices. - Lev I. Vysotsky; Maxim V. Rakhuba (Numer. Linear Algebra Appl., 2023)

Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach. - Ivan Peshekhonov; Aleksey Arzhantsev; Maxim V. Rakhuba (AISTATS, 2024)

Dimension-free Structured Covariance Estimation. - Nikita Puchkin; Maxim V. Rakhuba (COLT, 2024)

Tight and Efficient Upper Bound on Spectral Norm of Convolutional Layers. - Ekaterina Grishina; Mikhail Gorbunov; Maxim V. Rakhuba (ECCV (89), 2024)