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09/16/2023
Recovering Missing Node Features with Local Structure-based Embeddings
Node features bolster graph-based learning when exploited jointly with n...
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12/04/2022
Joint graph learning from Gaussian observations in the presence of hidden nodes
Graph learning problems are typically approached by focusing on learning...
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01/21/2022
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
Value-function (VF) approximation is a central problem in Reinforcement ...
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11/10/2021
Learning Graphs from Smooth and Graph-Stationary Signals with Hidden Variables
Network-topology inference from (vertex) signal observations is a promin...
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10/05/2021
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent p...
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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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09/24/2021
Untrained Graph Neural Networks for Denoising
A fundamental problem in signal processing is to denoise a signal. While...
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04/18/2021
Low-rank State-action Value-function Approximation
Value functions are central to Dynamic Programming and Reinforcement Lea...
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10/16/2020
Joint Inference of Multiple Graphs from Matrix Polynomials
Inferring graph structure from observations on the nodes is an important...
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03/15/2020
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning
The era of "data deluge" has sparked renewed interest in graph-based lea...
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08/02/2019
An Underparametrized Deep Decoder Architecture for Graph Signals
While deep convolutional architectures have achieved remarkable results ...
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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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02/19/2019
Distributed Network Caching via Dynamic Programming
Next-generation communication networks are envisioned to extensively uti...
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11/05/2018
A Recurrent Graph Neural Network for Multi-Relational Data
The era of data deluge has sparked the interest in graph-based learning ...
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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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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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03/06/2018