815 citations · 3.2k across the 142 of their papers we have counts for
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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…
Adversarial point set registration
Sergei Divakov, Ivan Oseledets
We present a novel approach to point set registration which is based on one-shot adversarial learning. The idea of the algorithm is inspired by recent successes of generative adver…
Application of machine learning to viscoplastic flow modeling
E. Muravleva, I. Oseledets, D. Koroteev
We present a method to construct reduced-order models for duct flows of Bingham media. Our method is based on proper orthogonal decomposition (POD) to find a low-dimensional approx…
Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition
Anh-Huy Phan, Andrzej Cichocki, Ivan Oseledets +3
The Canonical Polyadic decomposition (CPD) is a convenient and intuitive tool for tensor factorization; however, for higher-order tensors, it often exhibits high computational cost…
Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions
Pavel Kharyuk, Ivan Oseledets
This work is devoted to elaboration on the idea to use block term decomposition for group data analysis and to raise the possibility of modelling group activity with (Lr, 1) and Tu…
Revealing the Unobserved by Linking Collaborative Behavior and Side Knowledge
Evgeny Frolov, Ivan Oseledets
We propose a tensor-based model that fuses a more granular representation of user preferences with the ability to take additional side information into account. The model relies on…