Search Results for author: Samuel Rey

Found 13 papers, 8 papers with code

Robust Graph Neural Network based on Graph Denoising

1 code implementation11 Dec 2023 Victor M. Tenorio, Samuel Rey, Antonio G. Marques

Graph Neural Networks (GNNs) have emerged as a notorious alternative to address learning problems dealing with non-Euclidean datasets.

Denoising Graph Learning

Blind Deconvolution of Sparse Graph Signals in the Presence of Perturbations

no code implementations16 Sep 2023 Victor M. Tenorio, Samuel Rey, Antonio G. Marques

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.

Joint Network Topology Inference in the Presence of Hidden Nodes

no code implementations30 Jun 2023 Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra

We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations.

Robust Network Topology Inference and Processing of Graph Signals

no code implementations26 Feb 2023 Samuel Rey

With classical techniques facing troubles to deal with the irregular (non-Euclidean) domain where the signals are defined, a popular approach at the heart of graph signal processing (GSP) is to: (i) represent the underlying support via a graph and (ii) exploit the topology of this graph to process the signals at hand.

Joint graph learning from Gaussian observations in the presence of hidden nodes

1 code implementation4 Dec 2022 Samuel Rey, Madeline Navarro, Andrei Buciulea, Santiago Segarra, Antonio G. Marques

Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.

Graph Learning Graph Similarity

Robust Graph Filter Identification and Graph Denoising from Signal Observations

1 code implementation16 Oct 2022 Samuel Rey, Victor M. Tenorio, Antonio G. Marques

Different from existing works, we formulate a non-convex optimization problem that operates in the vertex domain and jointly performs GF identification and graph denoising.

Denoising

Enhanced graph-learning schemes driven by similar distributions of motifs

no code implementations11 Jul 2022 Samuel Rey, T. Mitchell Roddenberry, Santiago Segarra, Antonio G. Marques

Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the network topology inference optimization problem.

Graph Learning Inference Optimization

Joint inference of multiple graphs with hidden variables from stationary graph signals

1 code implementation5 Oct 2021 Samuel Rey, Andrei Buciulea, Madeline Navarro, Santiago Segarra, Antonio G. Marques

Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference.

A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters

1 code implementation2 Oct 2021 Victor M. Tenorio, Samuel Rey, Fernando Gama, Santiago Segarra, Antonio G. Marques

Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular domains.

Denoising Node Classification

Untrained Graph Neural Networks for Denoising

1 code implementation24 Sep 2021 Samuel Rey, Santiago Segarra, Reinhard Heckel, Antonio G. Marques

This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios.

Denoising

Robust graph-filter identification with graph denoising regularization

1 code implementation10 Mar 2021 Samuel Rey, Antonio G. Marques

When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known.

Denoising

An Underparametrized Deep Decoder Architecture for Graph Signals

1 code implementation2 Aug 2019 Samuel Rey, Antonio G. Marques, Santiago Segarra

While deep convolutional architectures have achieved remarkable results in a gamut of supervised applications dealing with images and speech, recent works show that deep untrained non-convolutional architectures can also outperform state-of-the-art methods in several tasks such as image compression and denoising.

Clustering Denoising +1

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