Dmitry Ermilov — accepted AI-conference papers

2021

Canonical Polyadic Tensor Decomposition With Low-Rank Factor Matrices

Anh-Huy Phan, Petr Tichavský, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Andrzej Cichocki

ICASSP 2021accepted

This paper proposes a constrained canonical polyadic (CP) tensor decomposition method with low-rank factor matrices. In this way, we allow the CP decomposition with high rank while keeping the number of the model parameters small. First, we propose an algorithm to decompose the tensors into factor m…

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2020

Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network

Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Julia Gusak, Petr Tichavský +3

ECCV 2020poster

Most state-of-the-art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kernels with its low-rank tensor approximations, whereas the Canonical Polyadic tensor Decomposition is one of the most suite…

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