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Structures of neural network effective theories

Banta, Ian (ORCID:000000022891992X)·Cai, Tianji (ORCID:0000000232359486)·Craig, Nathaniel·Zhang, Zhengkang (ORCID:0000000183055581)
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Abstract

We develop a diagrammatic approach to effective field theories (EFTs) corresponding to deep neural networks at initialization, which dramatically simplifies computations of finite-width corrections to neuron statistics. The structures of EFT calculations make it transparent that a single condition governs criticality of all connected correlators of neuron preactivations. Understanding of such EFTs may facilitate progress in both deep learning and field theory simulations. Published by the American Physical Society 2024

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Banta, Ian (ORCID:000000022891992X), Cai, Tianji (ORCID:0000000232359486), Craig, Nathaniel, Zhang, Zhengkang (ORCID:0000000183055581). 2024-05-07. Structures of neural network effective theories. https://doi.org/10.1103/physrevd.109.105007

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