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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 2018Show all

6 papers · 1 filter

eess.SP2018

Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing

Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Marques +1

Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by \emph{cac…

cs.LG2018

A Recurrent Graph Neural Network for Multi-Relational Data

Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis

The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering. In this paper,…

eess.SP2018

Connecting the Dots: Identifying Network Structure via Graph Signal Processing

Gonzalo Mateos, Santiago Segarra, Antonio G. Marques +1

Network topology inference is a prominent problem in Network Science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known, and then analy…

cs.LG2018

Median activation functions for graph neural networks

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

Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolu…

eess.SP2018

Convolutional Neural Network Architectures for Signals Supported on Graphs

Fernando Gama, Antonio G. Marques, Geert Leus +1

Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural ne…

cs.LG2018

MIMO Graph Filters for Convolutional Neural Networks

Fernando Gama, Antonio G. Marques, Alejandro Ribeiro +1

Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks. CNNs impl…