Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Recently, graph neural networks (GNN) have been successfully applied to recommender
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …

In recent years, there have been significant advances in the use of deep learning methods in
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …

Graph-based social recommendation improves the prediction accuracy of recommendation
by leveraging high-order neighboring information contained in social relations. However …

Graph neural networks (GNNs) have become a workhorse approach for learning from data
defined over irregular domains, typically by implicitly assuming that the data structure is …

Radar‐based human activity recognition is considered as a competitive solution for the
elderly care health monitoring problem, compared to alternative techniques such as …

The reactor control rod drive mechanism (CRDM) controls the startup, shutdown and power
of the reactor; it is one of the key pieces of equipment to ensure the normal operation of the …

Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising (HyperGSD)
are two fundamental topics in higher-order network modeling. Understanding the connection …

This paper introduces a novel nonparametric framework for data imputation, coined
multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM) …