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

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Max Guillen, Philipp Misof, Jan E. Gerken ·

    Finite-Width Neural Tangent Kernels from Feynman Diagrams

    arXiv:2508.11522v4 Announce Type: replace Abstract: Neural tangent kernels (NTKs) are a powerful tool for analyzing deep, non-linear neural networks. In the infinite-width limit, NTKs can easily be computed for most common architectures, yielding full analytic control over the tr…

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