Generalization error of graph neural networks in the mean-field regime
This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explore two widely utilized types of graph neural networks: graph convolutional neural...
| Hlavní autoři: | , , , , |
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| Médium: | Conference item |
| Jazyk: | English |
| Vydáno: |
Proceedings of Machine Learning Research
2024
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