Fernando Gama

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

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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

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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

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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

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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

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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

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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

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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

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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

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Stability of Graph Scattering Transforms

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Fernando Gama

Featured Co-authors

Zhangyang Wang

Alejandro Ribeiro

Joan Bruna

Vijay Kumar

Geert Leus

Amanda Prorok

Elvin Isufi

Antonio G. Marques

Brian M. Sadler

Zhan Gao

Luana Ruiz

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