The research in the sixth generation of wireless networks needs to tackle new challenges in
order to meet the requirements of emerging applications in terms of high data rate, low …

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

ITensor is a system for programming tensor network calculations with an interface modeled
on tensor diagram notation, which allows users to focus on the connectivity of a tensor …

Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …

Generalized tensor algebra is a prime candidate for acceleration via customized ASICs.
Modern tensors feature a wide range of data sparsity, with the density of non-zero elements …

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 …

Tensor networks represent the state-of-the-art in computational methods across many
disciplines, including the classical simulation of quantum many-body systems and quantum …

Generative modeling, which learns joint probability distribution from data and generates
samples according to it, is an important task in machine learning and artificial intelligence …

Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

Neural net compression can be achieved by approximating each layer's weight matrix by a
low-rank matrix. The real difficulty in doing this is not in training the resulting neural net …