Structures of Neural Network Effective Theories

Ian Banta, Tianji Cai, Nathaniel Craig, Zhengkang Zhang
5/3/2023
hep-thcond-mat.dis-nncs.LG

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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.

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Cite this paper

@article{banta2023structures,
  title  = {Structures of Neural Network Effective Theories},
  author = {Ian Banta and Tianji Cai and Nathaniel Craig and Zhengkang Zhang},
  year   = {2023},
  eprint = {2305.02334},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.02334v1}
}

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