Tensor ring decompositions provide a powerful framework for analyzing multidimensional data. These decompositions represent tensors as a sum of rank-1 or low-rank matrices, leading to significant analytical advantages over traditional tensor representations. By exploiting the inherent structure of multiway data, tensor ring decompositions enable ef

Tensor rings provide a novel and powerful framework for effective representation learning. By decomposing high-order tensors into a sum of lower-rank tensors, tensor ring models capture complex data structures in a more compact manner. This decomposition of dimensionality leads to significant improvements in terms of memory efficiency and inference