b'Fernando Gama'

Decentralized Control with Graph Neural Networks

Dynamical systems consisting of a set of autonomous agents face the chal...
12/29/2020 ∙ by Fernando Gama, et al. ∙ 16 ∙ share

read it
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) learn compositional represen...
10/27/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

read it
Discriminability of Single-Layer Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...
10/17/2020 ∙ by Samuel Pfrommer, et al. ∙ 0 ∙ share

read it
Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)

Spherical signals are useful mathematical models for data arising in man...
10/12/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

read it
Graph Neural Networks: Architectures, Stability and Transferability

Graph Neural Networks (GNNs) are information processing architectures fo...
08/04/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

read it
Wide and Deep Graph Neural Networks with Distributed Online Learning

Graph neural networks (GNNs) learn representations from network data wit...
06/11/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

read it
Graph Neural Networks for Decentralized Controllers

Dynamical systems comprised of autonomous agents arise in many relevant ...
03/23/2020 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
Graphs, Convolutions, and Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...
03/08/2020 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms

Despite the popularity of decentralized controller learning, very few su...
02/06/2020 ∙ by Ting-Kuei Hu, et al. ∙ 0 ∙ share

read it
Gated Graph Recurrent Neural Networks

Graph processes exhibit a temporal structure determined by the sequence ...
02/03/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

read it
EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structur...
01/21/2020 ∙ by Elvin Isufi, et al. ∙ 29 ∙ share

read it
Graph Neural Networks for Decentralized Multi-Robot Path Planning

Efficient and collision-free navigation in multi-robot systems is fundam...
12/12/2019 ∙ by Qingbiao Li, et al. ∙ 0 ∙ share

read it
Stability of Graph Neural Networks to Relative Perturbations

Graph neural networks (GNNs), consisting of a cascade of layers applying...
10/21/2019 ∙ by Fernando Gama, et al. ∙ 24 ∙ share

read it
Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures...
06/11/2019 ∙ by Fernando Gama, et al. ∙ 4 ∙ share

read it
Stability Properties of Graph Neural Networks

Data stemming from networks exhibit an irregular support, whereby each d...
05/11/2019 ∙ by Fernando Gama, et al. ∙ 22 ∙ share

read it
Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Graph signals are signals with an irregular structure that can be descri...
03/29/2019 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

read it
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

We consider the problem of finding distributed controllers for large net...
03/25/2019 ∙ by Ekaterina Tolstaya, et al. ∙ 0 ∙ share

read it
Gated Graph Convolutional Recurrent Neural Networks

Graph processes model a number of important problems such as identifying...
03/05/2019 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

read it
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs

This paper reviews graph convolutional neural networks (GCNNs) through t...
03/04/2019 ∙ by Elvin Isufi, et al. ∙ 0 ∙ share

read it
Median activation functions for graph neural networks

Graph neural networks (GNNs) have been shown to replicate convolutional ...
10/29/2018 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

read it
Diffusion Scattering Transforms on Graphs

Stability is a key aspect of data analysis. In many applications, the na...
06/22/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
Convolutional Neural Networks Architectures for Signals Supported on Graphs

We describe two architectures that generalize convolutional neural netwo...
05/01/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
Ergodicity in Stationary Graph Processes: A Weak Law of Large Numbers

For stationary signals in time the weak law of large numbers (WLLN) stat...
03/12/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
MIMO Graph Filters for Convolutional Neural Networks

Superior performance and ease of implementation have fostered the adopti...
03/06/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it
Convolutional Neural Networks Via Node-Varying Graph Filters

Convolutional neural networks (CNNs) are being applied to an increasing ...
10/27/2017 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

read it

Fernando Gama

Featured Co-authors

Zhangyang Wang

Alejandro Ribeiro

Joan Bruna

Vijay Kumar

Geert Leus

Amanda Prorok

Elvin Isufi

Luana Ruiz

Antonio G. Marques

Qingbiao Li

Brian M. Sadler

Decentralized Control with Graph Neural Networks

Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Discriminability of Single-Layer Graph Neural Networks

Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)

Graph Neural Networks: Architectures, Stability and Transferability

Wide and Deep Graph Neural Networks with Distributed Online Learning

Graph Neural Networks for Decentralized Controllers

Graphs, Convolutions, and Neural Networks

VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms

Gated Graph Recurrent Neural Networks

EdgeNets:Edge Varying Graph Neural Networks

Graph Neural Networks for Decentralized Multi-Robot Path Planning

Stability of Graph Neural Networks to Relative Perturbations

Stability of Graph Scattering Transforms

Stability Properties of Graph Neural Networks

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

Gated Graph Convolutional Recurrent Neural Networks

Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs

Median activation functions for graph neural networks

Diffusion Scattering Transforms on Graphs

Convolutional Neural Networks Architectures for Signals Supported on Graphs

Ergodicity in Stationary Graph Processes: A Weak Law of Large Numbers

MIMO Graph Filters for Convolutional Neural Networks

Convolutional Neural Networks Via Node-Varying Graph Filters