Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Convolutional neural networks typically consist of many convolutional layers followed by
one or more fully connected layers. While convolutional layers map between high-order …

Abstract Graph Neural Networks (GNNs) are attracting growing attention due to their
effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN …

Motivated by applications in neuroimaging analysis, we propose a new regression model,
Sparse TensOr REsponse regression (STORE), with a tensor response and a vector …

Tensor-on-tensor regression: Riemannian optimization, over-parameterization, statistical-computational
gap and their interplay Page 1 The Annals of Statistics 2024, Vol. 52, No. 6, 2583–2612 …

The aim of this article is to develop a low-rank linear regression model to correlate a high-
dimensional response matrix with a high-dimensional vector of covariates when coefficient …

The tensor-on-tensor regression can predict a tensor from a tensor, which generalizes most
previous multilinear regression approaches, including methods to predict a scalar from a …

We propose and analyse a reduced-rank method for solving least-squares regression
problems with infinite dimensional output. We derive learning bounds for our method, and …

A major interest in longitudinal neuroimaging studies involves investigating voxel‐level
neuroplasticity due to treatment and other factors across visits. However, traditional voxel …