no code implementations • 5 Feb 2024 • Gleb Ryzhakov, Andrei Chertkov, Artem Basharin, Ivan Oseledets
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 the use of the MaxVol indices selection procedure.
1 code implementation • 28 Dec 2023 • Nikita Pospelov, Andrei Chertkov, Maxim Beketov, Ivan Oseledets, Konstantin Anokhin
Neural networks (NNs), both living and artificial, work due to being complex systems of neurons, each having its own specialization.
1 code implementation • 20 Mar 2023 • Andrei Chertkov, Olga Tsymboi, Mikhail Pautov, Ivan Oseledets
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 translation systems.
1 code implementation • 9 May 2022 • Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll, Ivan Oseledets, Evgeny Frolov
The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave.
1 code implementation • 30 Apr 2022 • Konstantin Sozykin, Andrei Chertkov, Roman Schutski, Anh-Huy Phan, Andrzej Cichocki, Ivan Oseledets
We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle.
no code implementations • 14 Feb 2022 • Valentin Khrulkov, Gleb Ryzhakov, Andrei Chertkov, Ivan Oseledets
Diffusion models have recently outperformed alternative approaches to model the distribution of natural images, such as GANs.