Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

The popular tensor train (TT) and tensor ring (TR) decompositions have achieved promising
results in science and engineering. However, TT and TR decompositions only establish an …

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from
the laborious model selection problem due to their high model sensitivity. In particular, for …

Inverse problems in multi-dimensional imaging, eg, completion, denoising, and compressive
sensing, are challenging owing to the big volume of the data and the inherent ill-posedness …

As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …

The tensor train (TT) format enjoys appealing advantages in handling structural high-order
tensors. The recent decade has witnessed the wide applications of TT-format tensors from …

Flexible characterization techniques that provide a detailed picture of the experimental
imperfections under realistic assumptions are crucial to gain actionable advice in the …

Tensor decomposition is an effective approach to compress over-parameterized neural
networks and to enable their deployment on resource-constrained hardware platforms …

We propose a new tensor completion method based on tensor trains. The to-be-completed
tensor is modeled as a low-rank tensor train, where we use the known tensor entries and …

Multiway data-related learning tasks pose a huge challenge to the traditional regression
analysis techniques due to the existence of multidirectional relatedness. Simply vectorizing …