Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit
Keywords: Bayesian Networks, Gaussian Process, Kernel Renormalization, Graph Neural Networks, Residual Network, Theory of Generalization
TL;DR: We use statistical learning theory to understand parallel graph neural networks, and find a narrow width limit where networks generalize better with small width.
Abstract: The infinite width limit of random neural networks is known to result in Neural Networks as Gaussian Process (NNGP) (Lee et al. (2018)), characterized by task-independent kernels. It is widely accepted that larger network widths contribute to improved generalization (Park et al. (2019)). However, this work challenges this notion by investigating the narrow width limit of the Bayesian Parallel Branching Graph Neural Network (BPB-GNN), an architecture that resembles residual-GCN. We demonstrate that when the width of a BPB-GNN is significantly smaller compared to the number of training examples, each branch exhibits more robust learning due to a symmetry breaking of branches in kernel renormalization. Surprisingly, the performance of a BPB-GNN in the narrow width limit is generally superior to or comparable to that achieved in the wide width limit in bias-limited scenarios. Furthermore, the readout norms of each branch in the narrow width limit are mostly independent of the architectural hyperparameters but generally reflective of the nature of the data. We also extend the results to other more general architectures such as the residual-MLP and demonstrate that the narrow width effect is a general feature of the branching networks. Our results characterize a newly defined narrow-width regime for parallel branching networks in general.
Primary Area: learning theory
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Submission Number: 12773
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