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02/02/2023
Unsupervised Learning of Sampling Distributions for Particle Filters
Accurate estimation of the states of a nonlinear dynamical system is cru...
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11/16/2022
Graph Filters for Signal Processing and Machine Learning on Graphs
Filters are fundamental in extracting information from data. For time se...
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10/17/2022
Unsupervised Optimal Power Flow Using Graph Neural Networks
Optimal power flow (OPF) is a critical optimization problem that allocat...
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07/08/2022
Stability of Aggregation Graph Neural Networks
In this paper we study the stability properties of aggregation graph neu...
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10/14/2021
Stability Analysis of Unfolded WMMSE for Power Allocation
Power allocation is one of the fundamental problems in wireless networks...
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10/06/2021
Unrolling Particles: Unsupervised Learning of Sampling Distributions
Particle filtering is used to compute good nonlinear estimates of comple...
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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...
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07/19/2021
Wide and Deep Graph Neural Network with Distributed Online Learning
Graph neural networks (GNNs) are naturally distributed architectures for...
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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 ...
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05/31/2021
Node-Variant Graph Filters in Graph Neural Networks
Graph neural networks (GNNs) have been successfully employed in a myriad...
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12/29/2020
Decentralized Control with Graph Neural Networks
Dynamical systems consisting of a set of autonomous agents face the chal...
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10/27/2020
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) learn compositional represen...
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10/17/2020
Discriminability of Single-Layer Graph Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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10/12/2020
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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08/04/2020
Graph Neural Networks: Architectures, Stability and Transferability
Graph Neural Networks (GNNs) are information processing architectures fo...
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06/11/2020
Wide and Deep Graph Neural Networks with Distributed Online Learning
Graph neural networks (GNNs) learn representations from network data wit...
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03/23/2020
Graph Neural Networks for Decentralized Controllers
Dynamical systems comprised of autonomous agents arise in many relevant ...
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03/08/2020
Graphs, Convolutions, and Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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02/06/2020
VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms
Despite the popularity of decentralized controller learning, very few su...
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02/03/2020
Gated Graph Recurrent Neural Networks
Graph processes exhibit a temporal structure determined by the sequence ...
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01/21/2020
EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
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12/12/2019
Graph Neural Networks for Decentralized Multi-Robot Path Planning
Efficient and collision-free navigation in multi-robot systems is fundam...
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10/21/2019
Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
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06/11/2019
Stability of Graph Scattering Transforms
Scattering transforms are non-trainable deep convolutional architectures...
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05/11/2019
Stability Properties of Graph Neural Networks
Data stemming from networks exhibit an irregular support, whereby each d...
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03/29/2019
Invariance-Preserving Localized Activation Functions for Graph Neural Networks
Graph signals are signals with an irregular structure that can be descri...
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03/25/2019
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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03/05/2019
Gated Graph Convolutional Recurrent Neural Networks
Graph processes model a number of important problems such as identifying...
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03/04/2019
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
This paper reviews graph convolutional neural networks (GCNNs) through t...
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10/29/2018
Median activation functions for graph neural networks
Graph neural networks (GNNs) have been shown to replicate convolutional ...
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06/22/2018
Diffusion Scattering Transforms on Graphs
Stability is a key aspect of data analysis. In many applications, the na...
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05/01/2018
Convolutional Neural Networks Architectures for Signals Supported on Graphs
We describe two architectures that generalize convolutional neural netwo...
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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...
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03/06/2018
MIMO Graph Filters for Convolutional Neural Networks
Superior performance and ease of implementation have fostered the adopti...
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10/27/2017