b'Fernando Gama'

research

∙ 07/08/2022

Stability of Aggregation Graph Neural Networks

In this paper we study the stability properties of aggregation graph neu...

Alejandro Parada-Mayorga, et al. ∙

research

∙ 10/14/2021

Stability Analysis of Unfolded WMMSE for Power Allocation

Power allocation is one of the fundamental problems in wireless networks...

Arindam Chowdhury, et al. ∙

research

∙ 10/06/2021

Unrolling Particles: Unsupervised Learning of Sampling Distributions

Particle filtering is used to compute good nonlinear estimates of comple...

Fernando Gama, et al. ∙

research

∙ 10/02/2021

A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters

Graph convolutional neural networks (GCNNs) are popular deep learning ar...

Victor M. Tenorio, et al. ∙

research

∙ 07/19/2021

Wide and Deep Graph Neural Network with Distributed Online Learning

Graph neural networks (GNNs) are naturally distributed architectures for...

Zhan Gao, et al. ∙

research

∙ 06/24/2021

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

In this paper, we present a perception-action-communication loop design ...

Ting-Kuei Hu, et al. ∙

research

∙ 05/31/2021

Node-Variant Graph Filters in Graph Neural Networks

Graph neural networks (GNNs) have been successfully employed in a myriad...

Fernando Gama, et al. ∙

research

∙ 12/29/2020

Decentralized Control with Graph Neural Networks

Dynamical systems consisting of a set of autonomous agents face the chal...

Fernando Gama, et al. ∙

research

∙ 10/27/2020

Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) learn compositional represen...

Luana Ruiz, et al. ∙

research

∙ 10/17/2020

Discriminability of Single-Layer Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...

Samuel Pfrommer, et al. ∙

research

∙ 10/12/2020

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

Spherical signals are useful mathematical models for data arising in man...

Zhan Gao, et al. ∙

research

∙ 08/04/2020

Graph Neural Networks: Architectures, Stability and Transferability

Graph Neural Networks (GNNs) are information processing architectures fo...

Luana Ruiz, et al. ∙

research

∙ 06/11/2020

Wide and Deep Graph Neural Networks with Distributed Online Learning

Graph neural networks (GNNs) learn representations from network data wit...

Zhan Gao, et al. ∙

research

∙ 03/23/2020

Graph Neural Networks for Decentralized Controllers

Dynamical systems comprised of autonomous agents arise in many relevant ...

Fernando Gama, et al. ∙

research

∙ 03/08/2020

Graphs, Convolutions, and Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...

Fernando Gama, et al. ∙

research

∙ 02/06/2020

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

Despite the popularity of decentralized controller learning, very few su...

Ting-Kuei Hu, et al. ∙

research

∙ 02/03/2020

Gated Graph Recurrent Neural Networks

Graph processes exhibit a temporal structure determined by the sequence ...

Luana Ruiz, et al. ∙

research

∙ 01/21/2020

EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structur...

Elvin Isufi, et al. ∙

research

∙ 12/12/2019

Graph Neural Networks for Decentralized Multi-Robot Path Planning

Efficient and collision-free navigation in multi-robot systems is fundam...

Qingbiao Li, et al. ∙

research

∙ 10/21/2019

Stability of Graph Neural Networks to Relative Perturbations

Graph neural networks (GNNs), consisting of a cascade of layers applying...

Fernando Gama, et al. ∙

research

∙ 06/11/2019

Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures...

Fernando Gama, et al. ∙

research

∙ 05/11/2019

Stability Properties of Graph Neural Networks

Data stemming from networks exhibit an irregular support, whereby each d...

Fernando Gama, et al. ∙

research

∙ 03/29/2019

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Graph signals are signals with an irregular structure that can be descri...

Luana Ruiz, et al. ∙

research

∙ 03/25/2019

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

We consider the problem of finding distributed controllers for large net...

Ekaterina Tolstaya, et al. ∙

research

∙ 03/05/2019

Gated Graph Convolutional Recurrent Neural Networks

Graph processes model a number of important problems such as identifying...

Luana Ruiz, et al. ∙

research

∙ 03/04/2019

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

This paper reviews graph convolutional neural networks (GCNNs) through t...

Elvin Isufi, et al. ∙

research

∙ 10/29/2018

Median activation functions for graph neural networks

Graph neural networks (GNNs) have been shown to replicate convolutional ...

Luana Ruiz, et al. ∙

research

∙ 06/22/2018

Diffusion Scattering Transforms on Graphs

Stability is a key aspect of data analysis. In many applications, the na...

Fernando Gama, et al. ∙

research

∙ 05/01/2018

Convolutional Neural Networks Architectures for Signals Supported on Graphs

We describe two architectures that generalize convolutional neural netwo...

Fernando Gama, et al. ∙

research

∙ 03/12/2018

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

For stationary signals in time the weak law of large numbers (WLLN) stat...

Fernando Gama, et al. ∙

research

∙ 03/06/2018

MIMO Graph Filters for Convolutional Neural Networks

Superior performance and ease of implementation have fostered the adopti...

Fernando Gama, et al. ∙

research

∙ 10/27/2017

Convolutional Neural Networks Via Node-Varying Graph Filters

Convolutional neural networks (CNNs) are being applied to an increasing ...

Fernando Gama, et al. ∙

Fernando Gama

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