410 citations · 1.5k across the 29 of their papers we have counts for
41 papers
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…
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters
Fengjun Yang, Fernando Gama, Somayeh Sojoudi +1
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and infor…
Unsupervised Optimal Power Flow Using Graph Neural Networks
Damian Owerko, Fernando Gama, Alejandro Ribeiro
Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is…
Stability of Aggregation Graph Neural Networks
Alejandro Parada-Mayorga, Zhiyang Wang, Fernando Gama +1
In this paper we study the stability properties of aggregation graph neural networks (Agg-GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid architectu…
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…
Unrolling Particles: Unsupervised Learning of Sampling Distributions
Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk +1
Particle filtering is used to compute good nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted averag…
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…
Wide and Deep Graph Neural Network with Distributed Online Learning
Zhan Gao, Fernando Gama, Alejandro Ribeiro
Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates for decentralized tasks.…
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks
Ting-Kuei Hu, Fernando Gama, Tianlong Chen +4
In this paper, we present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-contro…
Node-Variant Graph Filters in Graph Neural Networks
Fernando Gama, Brendon G. Anderson, Somayeh Sojoudi
Graph neural networks (GNNs) have been successfully employed in a myriad of applications involving graph signals. Theoretical findings establish that GNNs use nonlinear activation…
Distributed Linear-Quadratic Control with Graph Neural Networks
Fernando Gama, Somayeh Sojoudi
Controlling network systems has become a problem of paramount importance. In this paper, we consider a distributed linear-quadratic problem and propose the use of graph neural netw…
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning
Fernando Gama, Qingbiao Li, Ekaterina Tolstaya +2
Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controller…
Graph Neural Networks for Distributed Linear-Quadratic Control
Fernando Gama, Somayeh Sojoudi
The linear-quadratic controller is one of the fundamental problems in control theory. The optimal solution is a linear controller that requires access to the state of the entire sy…
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…
Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)
Zhan Gao, Fernando Gama, Alejandro Ribeiro
Spherical convolutional neural networks (Spherical CNNs) learn nonlinear representations from 3D data by exploiting the data structure and have shown promising performance in shape…
Graph Neural Networks: Architectures, Stability and Transferability
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (…
Wide and Deep Graph Neural Networks with Distributed Online Learning
Zhan Gao, Fernando Gama, Alejandro Ribeiro
Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oft…
Graph Neural Networks for Decentralized Controllers
Fernando Gama, Ekaterina Tolstaya, Alejandro Ribeiro
Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of paramou…
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
Fernando Gama, Elvin Isufi, Geert Leus +1
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning f…
VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms
Ting-Kuei Hu, Fernando Gama, Tianlong Chen +3
Decentralized coordination of a robot swarm requires addressing the tension between local perceptions and actions, and the accomplishment of a global objective. In this work, we pr…
Gated Graph Recurrent Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph processes exhibit a temporal structure determined by the sequence index and and a spatial structure determined by the graph support. To learn from graph processes, an informa…
EdgeNets:Edge Varying Graph Neural Networks
Elvin Isufi, Fernando Gama, Alejandro Ribeiro
Driven by the outstanding performance of neural networks in the structured Euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and…