410 citations · 1.5k across the 29 of their papers we have counts for
20 papers · 1 filter
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…
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…
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.…
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…
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…
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…
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…
Stability of Graph Neural Networks to Relative Perturbations
Fernando Gama, Joan Bruna, Alejandro Ribeiro
Graph neural networks (GNNs), consisting of a cascade of layers applying a graph convolution followed by a pointwise nonlinearity, have become a powerful architecture to process si…
Stability of Graph Scattering Transforms
Fernando Gama, Joan Bruna, Alejandro Ribeiro
Scattering transforms are non-trainable deep convolutional architectures that exploit the multi-scale resolution of a wavelet filter bank to obtain an appropriate representation of…
Stability Properties of Graph Neural Networks
Fernando Gama, Joan Bruna, Alejandro Ribeiro
Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of graph signals, exhibiting success in recommender systems, power outage prediction, and moti…
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
Elvin Isufi, Fernando Gama, Alejandro Ribeiro
This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local re…
Gated Graph Convolutional Recurrent Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recur…
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…
Diffusion Scattering Transforms on Graphs
Fernando Gama, Alejandro Ribeiro, Joan Bruna
Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms…
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…
Convolutional Neural Networks Via Node-Varying Graph Filters
Fernando Gama, Geert Leus, Antonio G. Marques +1
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Sinc…