Structures of Neural Network Effective Theories

Ian Banta; Tianji Cai; Nathaniel Craig; Zhengkang Zhang

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arXiv · 2305.02334

Structures of Neural Network Effective Theories

Ian Banta·Tianji Cai·Nathaniel Craig·Zhengkang Zhang

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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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hep-th cond-mat.dis-nn cs.LG hep-ph stat.ML

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Ian Banta, Tianji Cai, Nathaniel Craig, Zhengkang Zhang. 2023-05-03. Structures of Neural Network Effective Theories. https://arxiv.org/abs/2305.02334

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