Geometric Deep Learning: a Temperature Based Analysis of Graph Neural Networks

We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previously defined in [7] and study it in the various layers for GCN and GAT models. Potential future applications of our findi...

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Bibliographic Details
Published in:arXiv.org 2023-09
Main Authors: Lapenna, M, Faglioni, F, Zanchetta, F, Fioresi, R
Format: Article
Language:English
Subjects:
Deep learning
Graph neural networks
Relativistic particles
Online Access:Get full text
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Summary:We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previously defined in [7] and study it in the various layers for GCN and GAT models. Potential future applications of our findings are discussed.
ISSN:2331-8422