2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024
TQCompressor: improving tensor decomposition methods in neural networks via permutations
V. Abronin, A. Naumov, D. Mazur +7
We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage…
quant-ph2023★ 2 cited
Optimization of chemical mixers design via tensor trains and quantum computing
Nikita Belokonev, Artem Melnikov, Maninadh Podapaka +3
Chemical component design is a computationally challenging procedure that often entails iterative numerical modeling and authentic experimental testing. We demonstrate a novel opti…
q-bio.BM2023★ 2 cited
Protein-protein docking using a tensor train black-box optimization method
Dmitry Morozov, Artem Melnikov, Vishal Shete +1
Black-box optimization methods play an important role in many fields of computational simulation. In particular, such methods are often used in the design and modelling of biologic…