Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

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

Depthwise separable convolution has shown great efficiency in network design, but requires
time-consuming training procedure with full training-set available. This paper first analyzes …

Group convolution, which divides the channels of ConvNets into groups, has achieved
impressive improvement over the regular convolution operation. However, existing models …

Tensor Kronecker products, the natural generalization of the matrix Kronecker product, are
independently emerging in multiple research communities. Like their matrix counterpart, the …

This article introduces a Tensor Network Kalman filter, which can estimate state vectors that
are exponentially large without ever having to explicitly construct them. The Tensor Network …

The segmentation of brain tumors in medical images is a crucial step of clinical treatment.
Manual segmentation is time consuming and labor intensive, and existing automatic …

For the first time, this position paper introduces a fundamental link between tensor networks
(TNs) and Green AI, highlighting their synergistic potential to enhance both the inclusivity …

For a long time, the idea of fusing synthetic radar imagery (SAR) and higher-resolution
panchromatic imagery, with the aim of increasing the spatial resolution of radar imagery, has …