Untrained neural network priors for inverse imaging problems: A survey
A Qayyum, I Ilahi, F Shamshad… - … on Pattern Analysis …, 2022 - ieeexplore.ieee.org
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 …
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …
Towards data-centric graph machine learning: Review and outlook
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 …
to drive AI models and applications, has attracted increasing attention in recent years. In this …
Learning to denoise unreliable interactions for graph collaborative filtering
Recently, graph neural networks (GNN) have been successfully applied to recommender
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …
Madm: A model-agnostic denoising module for graph-based social recommendation
Graph-based social recommendation improves the prediction accuracy of recommendation
by leveraging high-order neighboring information contained in social relations. However …
by leveraging high-order neighboring information contained in social relations. However …
Theoretical perspectives on deep learning methods in inverse problems
J Scarlett, R Heckel, MRD Rodrigues… - IEEE journal on …, 2022 - ieeexplore.ieee.org
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 …
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …
Radar‐based human activity recognition using denoising techniques to enhance classification accuracy
R Yu, Y Du, J Li, A Napolitano… - IET Radar, Sonar & …, 2024 - Wiley Online Library
Radar‐based human activity recognition is considered as a competitive solution for the
elderly care health monitoring problem, compared to alternative techniques such as …
elderly care health monitoring problem, compared to alternative techniques such as …
Convolutional learning on directed acyclic graphs
We develop a novel convolutional architecture tailored for learning from data defined over
directed acyclic graphs (DAGs). DAGs can be used to model causal relationships among …
directed acyclic graphs (DAGs). DAGs can be used to model causal relationships among …
Redesigning graph filter-based GNNs to relax the homophily assumption
S Rey, M Navarro, VM Tenorio, S Segarra… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …
defined over irregular domains, typically by implicitly assuming that the data structure is …
FedPnP: Personalized graph-structured federated learning
A Rasti-Meymandi, A Sajedi, KN Plataniotis - Pattern Recognition, 2025 - Elsevier
Abstract In Personalized Federated Learning (PFL), current methods often fail to consider
the fine-grained relationships between clients and their local datasets, hindering effective …
the fine-grained relationships between clients and their local datasets, hindering effective …
MRI reconstruction with enhanced self-similarity using graph convolutional network
Q Ma, Z Lai, Z Wang, Y Qiu, H Zhang, X Qu - BMC Medical Imaging, 2024 - Springer
Abstract Background Recent Convolutional Neural Networks (CNNs) perform low-error
reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image …
reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image …