Graph Neural Networks for Learning Equivariant Representations of Neural Networks

Published: 16 Jan 2024, Last Modified: 18 Mar 2024ICLR 2024 oralEveryoneRevisionsBibTeX
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Keywords: Deep weight space, Graph neural networks, Transformers, Permutation equivariance, Implicit neural representations, Networks for networks, Neural graphs
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TL;DR: We propose graph neural networks that learn permutation equivariant representations of other neural networks
Abstract: Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However, existing approaches either overlook the inherent permutation symmetry in the neural network or rely on intricate weight-sharing patterns to achieve equivariance, while ignoring the impact of the network architecture itself. In this work, we propose to represent neural networks as computational graphs of parameters, which allows us to harness powerful graph neural networks and transformers that preserve permutation symmetry. Consequently, our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations, predicting generalization performance, and learning to optimize, while consistently outperforming state-of-the-art methods. The source code is open-sourced at
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Primary Area: learning on graphs and other geometries & topologies
Submission Number: 7510