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.
[2, 3] instead gives⟨|a i|2n⟩= (2n−1)!!⟨|a i|2⟩n, which amplifies the bias and variance by numerical prefactors but does not change the parametric dependence onα
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
hep-th 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.