activity
20102024
most citedUnifying time evolution and optimization with matrix product states

815 citations · 3.2k across the 142 of their papers we have counts for

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Showing 2023Show all

20 papers · 1 filter

cs.IR2023

Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems

Albert Saiapin, Ivan Oseledets, Evgeny Frolov

In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to ad…

math.NA2023

Tensor Train Decomposition for Adversarial Attacks on Computer Vision Models

Andrei Chertkov, Ivan Oseledets

Deep neural networks (DNNs) are widely used today, but they are vulnerable to adversarial attacks. To develop effective methods of defense, it is important to understand the potent…

math.OC2023

Another approach to build Lyapunov functions for the first order methods in the quadratic case

Daniil Merkulov, Ivan Oseledets

Lyapunov functions play a fundamental role in analyzing the stability and convergence properties of optimization methods. In this paper, we propose a novel and straightforward appr…

math.NA2023

On the structure of the Schur complement matrix for the Stokes equation

Vladislav Pimanov, Ekaterina Muravleva, Ivan Oseledets +1

In this paper, we investigate the structure of the Schur complement matrix for the fully-staggered finite-difference discretization of the stationary Stokes equation. Specifically,…

cs.CV2023

General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing

Dmitrii Korzh, Mikhail Pautov, Olga Tsymboi +1

Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude. Howev…

cs.CV2023

Unsupervised evaluation of GAN sample quality: Introducing the TTJac Score

Egor Sevriugov, Ivan Oseledets

Evaluation metrics are essential for assessing the performance of generative models in image synthesis. However, existing metrics often involve high memory and time consumption as…