no code implementations • 13 Dec 2023 • Ruituo Wu, Jiani Liu, Ce Zhu, Anh-Huy Phan, Ivan V. Oseledets, Yipeng Liu
However, a substantial number of potential tensor permutations can lead to a tensor network with the same structure but varying expressive capabilities.
no code implementations • 8 Aug 2023 • Daria Cherniuk, Stanislav Abukhovich, Anh-Huy Phan, Ivan Oseledets, Andrzej Cichocki, Julia Gusak
Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks.
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 • 5 Mar 2022 • Anh-Huy Phan, Konstantin Sobolev, Dmitry Ermilov, Igor Vorona, Nikolay Kozyrskiy, Petr Tichavsky, Andrzej Cichocki
This motivates using a hybrid model of CPD and TKD, a decomposition with multiple Tucker models with small core tensor, known as block term decomposition (BTD).
no code implementations • ECCV 2020 • Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Julia Gusak, Petr Tichavsky, Valeriy Glukhov, Ivan Oseledets, Andrzej Cichocki
Most state of the art deep neural networks are overparameterized and exhibit a high computational cost.
no code implementations • 16 Jun 2020 • Nikolay Kozyrskiy, Anh-Huy Phan
The modern convolutional neural networks although achieve great results in solving complex computer vision tasks still cannot be effectively used in mobile and embedded devices due to the strict requirements for computational complexity, memory and power consumption.
no code implementations • 13 Nov 2017 • Anh-Huy Phan, Masao Yamagishi, Danilo Mandic, Andrzej Cichocki
A novel algorithm to solve the quadratic programming problem over ellipsoids is proposed.
Optimization and Control