3 papers
eess.SP2022
Joint graph learning from Gaussian observations in the presence of hidden nodes
Samuel Rey, Madeline Navarro, Andrei Buciulea +2
Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups…
eess.SP2021
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…
eess.SP2019
An Underparametrized Deep Decoder Architecture for Graph Signals
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…