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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 …
Robust graph filter identification and graph denoising from signal observations
S Rey, VM Tenorio, AG Marqués - IEEE Transactions on Signal …, 2023 - ieeexplore.ieee.org
When facing graph signal processing tasks, it is typically assumed that the graph describing
the support of the signals is known. However, in many relevant applications the available …
the support of the signals is known. However, in many relevant applications the available …
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 …
Blind deconvolution of sparse graph signals in the presence of perturbations
VM Tenorio, S Rey, AG Marques - ICASSP 2024-2024 IEEE …, 2024 - ieeexplore.ieee.org
Blind deconvolution over graphs involves using (observed) output graph signals to obtain
both the inputs (sources) as well as the filter that drives (models) the graph diffusion process …
both the inputs (sources) as well as the filter that drives (models) the graph diffusion process …
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 …
Exploiting the Structure of Two Graphs with Graph Neural Networks
VM Tenorio, AG Marques - arxiv preprint arxiv:2411.05119, 2024 - arxiv.org
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
VM Tenorio, S Rey, AG Marques - 2023 57th Asilomar …, 2023 - ieeexplore.ieee.org
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 …
Neighborhood Graph Filters Based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning
Multi-agent deep reinforcement learning (MADRL), where a group of agents inside multi-
agent systems cooperate to achieve a common goal, has been shown useful in many …
agent systems cooperate to achieve a common goal, has been shown useful in many …
Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors
Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the
deep learning community to learn complex hidden relationships of data generated from non …
deep learning community to learn complex hidden relationships of data generated from non …