2 citations · 2 across the 3 of their papers we have counts for
6 papers
How to Train Unstable Looped Tensor Network
Anh-Huy Phan, Konstantin Sobolev, Dmitry Ermilov +4
A rising problem in the compression of Deep Neural Networks is how to reduce the number of parameters in convolutional kernels and the complexity of these layers by low-rank tensor…
Characterization of Decomposition of Matrix Multiplication Tensors
Petr Tichavsky
In this paper, the canonical polyadic (CP) decomposition of tensors that corresponds to matrix multiplications is studied. Finding the rank of these tensors and computing the decom…
Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network
Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin +6
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 kerne…
Cramér-Rao Bounds for Complex-Valued Independent Component Extraction: Determined and Piecewise Determined Mixing Models
Václav Kautský, Zbyněk Koldovský, Petr Tichavský +1
This paper presents Cramér-Rao Lower Bound (CRLB) for the complex-valued Blind Source Extraction (BSE) problem based on the assumption that the target signal is independent of the…
Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence
Zbyněk Koldovský, Petr Tichavský
We revise the problem of extracting one independent component from an instantaneous linear mixture of signals. The mixing matrix is parameterized by two vectors, one column of the…
Numerical CP Decomposition of Some Difficult Tensors
Petr Tichavsky, Anh Huy Phan, Andrzej Cichocki
In this paper, a numerical method is proposed for canonical polyadic (CP) decomposition of small size tensors. The focus is primarily on decomposition of tensors that correspond to…