
Decentralized Control with Graph Neural Networks
Dynamical systems consisting of a set of autonomous agents face the chal...
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Nonlinear StateSpace Generalizations of Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) learn compositional represen...
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Discriminability of SingleLayer Graph Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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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...
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Graph Neural Networks: Architectures, Stability and Transferability
Graph Neural Networks (GNNs) are information processing architectures fo...
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Wide and Deep Graph Neural Networks with Distributed Online Learning
Graph neural networks (GNNs) learn representations from network data wit...
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Graph Neural Networks for Decentralized Controllers
Dynamical systems comprised of autonomous agents arise in many relevant ...
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Graphs, Convolutions, and Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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VGAI: A VisionBased Decentralized Controller Learning Framework for Robot Swarms
Despite the popularity of decentralized controller learning, very few su...
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Gated Graph Recurrent Neural Networks
Graph processes exhibit a temporal structure determined by the sequence ...
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EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
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Graph Neural Networks for Decentralized MultiRobot Path Planning
Efficient and collisionfree navigation in multirobot systems is fundam...
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Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
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Stability of Graph Scattering Transforms
Scattering transforms are nontrainable deep convolutional architectures...
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Stability Properties of Graph Neural Networks
Data stemming from networks exhibit an irregular support, whereby each d...
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InvariancePreserving Localized Activation Functions for Graph Neural Networks
Graph signals are signals with an irregular structure that can be descri...
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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...
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Gated Graph Convolutional Recurrent Neural Networks
Graph processes model a number of important problems such as identifying...
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Generalizing Graph Convolutional Neural Networks with EdgeVariant Recursions on Graphs
This paper reviews graph convolutional neural networks (GCNNs) through t...
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Median activation functions for graph neural networks
Graph neural networks (GNNs) have been shown to replicate convolutional ...
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Diffusion Scattering Transforms on Graphs
Stability is a key aspect of data analysis. In many applications, the na...
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Convolutional Neural Networks Architectures for Signals Supported on Graphs
We describe two architectures that generalize convolutional neural netwo...
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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...
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MIMO Graph Filters for Convolutional Neural Networks
Superior performance and ease of implementation have fostered the adopti...
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Convolutional Neural Networks Via NodeVarying Graph Filters
Convolutional neural networks (CNNs) are being applied to an increasing ...
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Fernando Gama
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