Neural networks optimized solely on crossing symmetry reconstruct CFT correlators from minimal input data to few-percent accuracy across generalized free fields, minimal models, Ising, N=4 SYM, and AdS diagrams.
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
Neural networks optimized solely on crossing symmetry reconstruct CFT correlators from minimal input data to few-percent accuracy across generalized free fields, minimal models, Ising, N=4 SYM, and AdS diagrams.
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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.