activity
20182022
most citedStability of Graph Scattering Transforms

36 citations · 47 across the 7 of their papers we have counts for

collaborators

27 papers

eess.SY2022

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…

eess.SY20228 cited

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…

cs.LG2021

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…

cs.RO20212 cited

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…

eess.SY20201 cited

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…

eess.SP2020

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…