Generalization error of graph neural networks in the mean-field regime

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...

书目详细资料
Main Authors:	Aminian, G, He, Y, Reinert, G, Szpruch, L, Cohen, S
格式:	Conference item
语言:	English
出版:	Proceedings of Machine Learning Research 2024

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