385 citations · 1.8k across the 36 of their papers we have counts for
7 papers · 1 filter
Joint inference of multiple graphs with hidden variables from stationary graph signals
Samuel Rey, Andrei Buciulea, Madeline Navarro +2
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference. However, current approaches are limited by typically focu…
Learning Graphs from Smooth and Graph-Stationary Signals with Hidden Variables
Andrei Buciulea, Samuel Rey, Antonio G. Marques
Network-topology inference from (vertex) signal observations is a prominent problem across data-science and engineering disciplines. Most existing schemes assume that observations…
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters
Victor M. Tenorio, Samuel Rey, Fernando Gama +2
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular…
Untrained Graph Neural Networks for Denoising
Samuel Rey, Santiago Segarra, Reinhard Heckel +1
A fundamental problem in signal processing is to denoise a signal. While there are many well-performing methods for denoising signals defined on regular supports, such as images de…
Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
David Ramírez, Antonio G. Marques, Santiago Segarra
Key to successfully deal with complex contemporary datasets is the development of tractable models that account for the irregular structure of the information at hand. This paper p…
Low-rank State-action Value-function Approximation
Sergio Rozada, Victor Tenorio, Antonio G. Marques
Value functions are central to Dynamic Programming and Reinforcement Learning but their exact estimation suffers from the curse of dimensionality, challenging the development of pr…
Robust graph-filter identification with graph denoising regularization
Samuel Rey, Antonio G. Marques
When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone…