9 citations · 22 across the 14 of their papers we have counts for
15 papers
Global Optimization of Atomic Clusters via Physically-Constrained Tensor Train Decomposition
Konstantin Sozykin, Nikita Rybin, Andrei Chertkov +5
The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with sy…
Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
Andrei Chertkov, Artem Basharin, Mikhail Saygin +3
The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photonic, neuromorphic) due to their p…
High-dimensional Optimization with Low Rank Tensor Sampling and Local Search
Konstantin Sozykin, Andrei Chertkov, Anh-Huy Phan +2
We present a novel method called TESALOCS (TEnsor SAmpling and LOCal Search) for multidimensional optimization, combining the strengths of gradient-free discrete methods and gradie…
Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
Artem Basharin, Andrei Chertkov, Ivan Oseledets
We propose a new model for multi-token prediction in transformers, aiming to enhance sampling efficiency without compromising accuracy. Motivated by recent work that predicts the p…
Black-Box Approximation and Optimization with Hierarchical Tucker Decomposition
Gleb Ryzhakov, Andrei Chertkov, Artem Basharin +1
We develop a new method HTBB for the multidimensional black-box approximation and gradient-free optimization, which is based on the low-rank hierarchical Tucker decomposition with…
Fast gradient-free activation maximization for neurons in spiking neural networks
Nikita Pospelov, Andrei Chertkov, Maxim Beketov +2
Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for un…
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…
Translate your gibberish: black-box adversarial attack on machine translation systems
Andrei Chertkov, Olga Tsymboi, Mikhail Pautov +1
Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine transla…
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…
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll +2
Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario coll…
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
Konstantin Sozykin, Andrei Chertkov, Roman Schutski +3
We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demo…
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…