b'Fernando Gama'

Wide and Deep Graph Neural Network with Distributed Online Learning

Graph neural networks (GNNs) are naturally distributed architectures for...
07/19/2021 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

In this paper, we present a perception-action-communication loop design ...
06/24/2021 ∙ by Ting-Kuei Hu, et al. ∙ 0 ∙ share

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Node-Variant Graph Filters in Graph Neural Networks

Graph neural networks (GNNs) have been successfully employed in a myriad...
05/31/2021 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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