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20092025
most citedGraph-signal Reconstruction and Blind Deconvolution for Structured Inputs

21 citations · 49 across the 10 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.SP2019

Generative Adversarial Networks For Graph Data Imputation From Signed Observations

Amarlingam Madapu, Santiago Segarra, Sundeep Prabhakar Chepuri +1

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 d…

eess.SP2019

Estimating Network Processes via Blind Identification of Multiple Graph Filters

Yu Zhu, Fernando J. Iglesias, Antonio G. Marques +1

This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel id…

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…

eess.SP2019

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Luana Ruiz, Fernando Gama, Antonio G. Marques +1

Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these grap…

cs.NI2019

Distributed Network Caching via Dynamic Programming

Alireza Sadeghi, Antonio G. Marques, Georgios B. Giannakis

Next-generation communication networks are envisioned to extensively utilize storage-enabled caching units to alleviate unfavorable surges of data traffic by pro-actively storing a…