Graph Convolutional Neural Networks (GCNN) Collection
These publications describe the SEI's applied graph signal processing techniques that create new tools for GCNNs.
Publisher:
Software Engineering Institute
Abstract
Large, complex datasets (e.g., sensor data, web traffic) require new approaches to graph processing. The SEI applied graph signal processing techniques to create new tools for graph convolutional neural networks (GCNNs), extending deep learning to graph problems.
Collection Contents
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Graph Convolutional Neural Networks
October 28, 2019 • Poster
By Oren Wright
This poster describes research to use graph convolutional neural networks to extend deep learning.
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Graph Convolutional Neural Networks
October 28, 2019 • Presentation
By Oren Wright
This project used graph signal processing formalisms to create new deep learning tools for graph convolutional neural networks (GCNNs).
read -
Graph Convolutional Neural Networks
November 12, 2019 • Video
By Oren Wright
Watch SEI researcher Mr. Oren Wright discuss using graph signal processing formalisms to create new deep learning tools for graph convolutional neural networks (GCNNs) to answer the question "how does AI learn structure?"
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