9 citations · 22 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
PROTES: Probabilistic Optimization with Tensor Sampling
Anastasia Batsheva, Andrei Chertkov, Gleb Ryzhakov +1
We developed a new method PROTES for black-box optimization, which is based on the probabilistic sampling from a probability density function given in the low-parametric tensor tra…
Optimization of Functions Given in the Tensor Train Format
Andrei Chertkov, Gleb Ryzhakov, Georgii Novikov +1
Tensor train (TT) format is a common approach for computationally efficient work with multidimensional arrays, vectors, matrices, and discretized functions in a wide range of appli…
Black box approximation in the tensor train format initialized by ANOVA decomposition
Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets
Surrogate models can reduce computational costs for multivariable functions with an unknown internal structure (black boxes). In a discrete formulation, surrogate modeling is equiv…
Solution of the Fokker-Planck equation by cross approximation method in the tensor train format
Andrei Chertkov, Ivan Oseledets
We propose the novel numerical scheme for solution of the multidimensional Fokker-Planck equation, which is based on the Chebyshev interpolation and the spectral differentiation te…
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…