Tensor network (TN) is a powerful framework in machine learning, but selecting a good TN
model, known as TN structure search (TN-SS), is a challenging and computationally …

This paper introduces a novel hyperspectral image super-resolution algorithm based on
graph-regularized tensor ring decomposition aimed at resolving the challenges of …

Compressing big data and model parameters via tensor decomposition such as the tensor
train (TT) format has gained great success in recent years. The application of tensor …

The fusion of hyperspectral and multispectral images presents a challenge as it involves
blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral …

In computational science, the preservation of characteristic data properties and the
computational efficiency for processing practices amidst growing complexity is crucial …

Deep neural networks have demonstrated significant achievements across various fields,
yet their memory and time complexities present obstacles for implementing them on …