The notion of a tensor captures three great ideas: equivariance, multilinearity, separability.
But trying to be three things at once makes the notion difficult to understand. We will explain …

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to
predict missing entries from partial observations. However, existing methods often make the …

Tensor decomposition methods are widely used for model compression and fast inference in
convolutional neural networks (CNNs). Although many decompositions are conceivable …

Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in
the self-representation tensor. Current tensor decompositions for MSC suffer from highly …

Tensor network (TN) decomposition is a promising framework to represent extremely high-
dimensional problems with few parameters. However, it is challenging to search the (near-) …

Recent works put much effort into tensor network structure search (TN-SS), aiming to select
suitable tensor network (TN) structures, involving the TN-ranks, formats, and so on, for the …

We propose a sampling-based method for computing the tensor ring (TR) decomposition of
a data tensor. The method uses leverage score sampled alternating least squares to fit the …

Low-rank tensor completion recovers missing entries based on different tensor
decompositions. Due to its outstanding performance in exploiting some higher-order data …

Tensor networks provide succinct representations of quantum many-body states and are an
important computational tool for strongly correlated quantum systems. Their expressive and …

The exploration of hybrid quantum-classical algorithms and programming models on noisy
near-term quantum hardware has begun. As hybrid programs scale towards classical …