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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 …
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 …
A robust alternative for graph convolutional neural networks via graph neighborhood filters
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that,
upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular …
upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular …
Robust graph-filter identification with graph denoising regularization
When approaching graph signal processing tasks, graphs are usually assumed to be
perfectly known. However, in many practical applications, the observed (inferred) network is …
perfectly known. However, in many practical applications, the observed (inferred) network is …
Overparametrized deep encoder-decoder schemes for inputs and outputs defined over graphs
There is a growing interest in the joint application of graph signal processing and neural
networks (NNs) for learning problems involving complex, non-linear and/or non-Euclidean …
networks (NNs) for learning problems involving complex, non-linear and/or non-Euclidean …
On architecture selection for linear inverse problems with untrained neural networks
In recent years, neural network based image priors have been shown to be highly effective
for linear inverse problems, often significantly outperforming conventional methods that are …
for linear inverse problems, often significantly outperforming conventional methods that are …
Deep encoder-decoder neural network architectures for graph output signals
Neural networks (NNs) and graph signal processing have emerged as important actors in
data-science applications dealing with complex (non-linear, non-Euclidean) datasets. In this …
data-science applications dealing with complex (non-linear, non-Euclidean) datasets. In this …
Generative adversarial networks for graph data imputation from signed observations
We study the problem of missing data imputation for graph signals from signed one-bit
quantized observations. More precisely, we consider that the true graph data is drawn from a …
quantized observations. More precisely, we consider that the true graph data is drawn from a …
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 …