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

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

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 …

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 …

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-
world applications. In order to obtain performance gains, these networks have grown larger …

Tensor network methods are taking a central role in modern quantum physics and beyond.
They can provide an efficient approximation to certain classes of quantum states, and the …

Tensor-network techniques have recently proven useful in machine learning, both as a tool
for the formulation of new learning algorithms and for enhancing the mathematical …

This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …

Hyperspectral image (HSI) super-resolution is a hot topic in remote sensing and computer
vision. Recently, tensor analysis has been proven to be an efficient technology for HSI …