Structures of Neural Network Effective Theories

• URL: http://arxiv.org/abs/2305.02334v1
• Date: Wed, 3 May 2023 18:00:00 GMT
• Title: Structures of Neural Network Effective Theories
• Authors: Ian Banta, Tianji Cai, Nathaniel Craig, Zhengkang Zhang
• Abstract summary: We develop a diagrammatic approach to effective field theories corresponding to deep neural networks. The structures of EFT calculations make it transparent that a single condition governs criticality of all connected correlators of neuron preactivations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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