63 citations · 79 across the 6 of their papers we have counts for
6 papers
OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models
Ali Aliev, Kamil Garifullin, Nikolay Yudin +5
In a rapidly growing field of model training there is a constant practical interest in parameter-efficient fine-tuning and various techniques that use a small amount of training da…
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds
Alexander Novikov, Maxim Rakhuba, Ivan Oseledets
In scientific computing and machine learning applications, matrices and more general multidimensional arrays (tensors) can often be approximated with the help of low-rank decomposi…
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians
Maxim Rakhuba, Alexander Novikov, Ivan Oseledets
Such problems as computation of spectra of spin chains and vibrational spectra of molecules can be written as high-dimensional eigenvalue problems, i.e., when the eigenvector can b…
Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations
Valentin Khrulkov, Maxim Rakhuba, Ivan Oseledets
Convolution with Green's function of a differential operator appears in a lot of applications e.g. Lippmann-Schwinger integral equation. Algorithms for computing such are usually n…
Robust discretization in quantized tensor train format for elliptic problems in two dimensions
A. V. Chertkov, I. V. Oseledets, M. V. Rakhuba
In this work we propose an efficient black-box solver for two-dimensional stationary diffusion equations, which is based on a new robust discretization scheme. The idea is to formu…
Calculating vibrational spectra of molecules using tensor train decomposition
Maxim Rakhuba, Ivan Oseledets
We propose a new algorithm for calculation of vibrational spectra of molecules using tensor train decomposition. Under the assumption that eigenfunctions lie on a low-parametric ma…