α=0 architecture in NNFT minimizes finite-width variance, removes IR corrections, and sets a fundamental SNR bound for correlation functions in scalar field theory.
Yang, Tensor programs i: Wide feedforward or re- current neural networks of any architecture are gaussian processes (2019) arXiv:1910.12478 [cs.NE]
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The work tests perturbative viability of single-layer neural networks for local QFTs at finite neuron number N in phi^4 theory, finding UV-cutoff-sensitive O(1/N) corrections with weak convergence and proposing a modification for better scaling.
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Optimal Architecture and Fundamental Bounds in Neural Network Field Theory
α=0 architecture in NNFT minimizes finite-width variance, removes IR corrections, and sets a fundamental SNR bound for correlation functions in scalar field theory.
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Viability of perturbative expansion for quantum field theories on neurons
The work tests perturbative viability of single-layer neural networks for local QFTs at finite neuron number N in phi^4 theory, finding UV-cutoff-sensitive O(1/N) corrections with weak convergence and proposing a modification for better scaling.