Untrained graph neural networks for denoising
A fundamental problem in signal processing is to denoise a signal. While there are many
well-performing methods for denoising signals defined on regular domains, including …
well-performing methods for denoising signals defined on regular domains, including …
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 …
Exploiting the Structure of Two Graphs with Graph Neural Networks
Graph neural networks (GNNs) have emerged as a promising solution to deal with
unstructured data, outperforming traditional deep learning architectures. However, most of …
unstructured data, outperforming traditional deep learning architectures. However, most of …
Robust Graph Neural Network Based on Graph Denoising
Graph Neural Networks (GNNs) have emerged as a notorious alternative to address
learning problems dealing with non-Euclidean datasets. However, although most works …
learning problems dealing with non-Euclidean datasets. However, although most works …
Node-variant graph filters in graph neural networks
Graph neural networks (GNNs) have been successfully employed in a myriad of applications
involving graph signals. Theoretical findings establish that GNNs use nonlinear activation …
involving graph signals. Theoretical findings establish that GNNs use nonlinear activation …
A unified view between tensor hypergraph neural networks and signal denoising
Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising (HyperGSD)
are two fundamental topics in higher-order network modeling. Understanding the connection …
are two fundamental topics in higher-order network modeling. Understanding the connection …