Gait recognition has emerged as an attractive biometric technology for the identification of
people by analysing the way they walk. However, one of the main challenges of the …

The advent of noisy-intermediate scale quantum computers has introduced the exciting
possibility of achieving quantum speedups in machine learning tasks. These devices …

The widespread use of multisensor technology and the emergence of big data sets have
brought the necessity to develop more versatile tools to represent higher order data with …

We introduce an efficient algorithm for reducing bond dimensions in an arbitrary tensor
network without changing its geometry. The method is based on a quantitative …

Low-rank tensor completion plays an important role in many applications such as image
processing, computer vision, and machine learning. A widely used convex relaxation of this …

Tensor-ring (TR) decomposition is a powerful tool for exploiting the low-rank property of
multiway data and has been demonstrated great potential in a variety of important …

We argue and demonstrate that projected entangled-pair states (PEPS) outperform matrix
product states significantly for the task of generative modeling of datasets with an intrinsic …

Exploiting the latent low-rankness of tensors is crucial in tensor denoising. Classically, many
methods use the Tucker model to find the low-rank structure of a tensor. Recently, the tensor …

Machine learning tasks are an exciting application for quantum computers, as it has been
proven that they can learn certain problems more efficiently than classical ones. Applying …

Tensor based methods have gained popularity for being able to represent multi-aspect real
world data in a lower dimensional space. Among them, methods with orthogonal factors …