Feynman Diagrams Used to Calculate Finite-Width Neural Network Kernel Corrections

Researchers have developed a novel method using Feynman diagrams to compute finite-width corrections to neural tangent kernels (NTKs). This approach simplifies algebraic manipulations and enables layer-wise recursion relations for predicting training dynamics at the leading order. The framework has been demonstrated to extend stability results for deep networks and confirm the absence of finite-width corrections for scale-invariant nonlinearities like ReLU on the Gram matrix diagonal. Numerical implementations show that these corrections align with sampled neural network statistics for widths greater than approximately 20. AI

IMPACT Introduces a new theoretical framework for analyzing neural network training dynamics, potentially leading to deeper understanding and more stable models.

RANK_REASON Academic paper published on arXiv detailing a new theoretical framework for neural network analysis. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Gramian matrix
• Hugging Face
• neural tangent kernels
• Philipp Misof
• rectifier
• ScienceCast

• paper
• infra

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