Simple feed-forward neural networks trained on crossing symmetry plus a single anchor value reproduce CFT correlators to percent-level accuracy, and the authors conjecture this works because physical correlators are the smoothest allowed functions.
CoRRabs/2405.17556(2024)
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A regression-tree-based method computes guaranteed bounds on the safe output probability for neural networks under probabilistic inputs by generating safe and unsafe hulls via boundary-aware sampling and prioritized refinement.
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Neural Spectral Bias and Conformal Correlators I: Introduction and Applications
Simple feed-forward neural networks trained on crossing symmetry plus a single anchor value reproduce CFT correlators to percent-level accuracy, and the authors conjecture this works because physical correlators are the smoothest allowed functions.
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Probabilistic Verification of Neural Networks via Efficient Probabilistic Hull Generation
A regression-tree-based method computes guaranteed bounds on the safe output probability for neural networks under probabilistic inputs by generating safe and unsafe hulls via boundary-aware sampling and prioritized refinement.