36 citations · 63 across the 9 of their papers we have counts for
10 papers · 1 filter
Graph Filters for Signal Processing and Machine Learning on Graphs
Elvin Isufi, Fernando Gama, David I. Shuman +1
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and mac…
On Local Distributions in Graph Signal Processing
T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk +1
Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distribut…
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…
Stability Analysis of Unfolded WMMSE for Power Allocation
Arindam Chowdhury, Fernando Gama, Santiago Segarra
Power allocation is one of the fundamental problems in wireless networks and a wide variety of algorithms address this problem from different perspectives. A common element among t…
Discriminability of Single-Layer Graph Neural Networks
Samuel Pfrommer, Fernando Gama, Alejandro Ribeiro
Network data can be conveniently modeled as a graph signal, where data values are assigned to the nodes of a graph describing the underlying network topology. Successful learning f…
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro +1
Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approac…