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- W3211318902 abstract "Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data." @default.
- W3211318902 created "2021-11-08" @default.
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- W3211318902 date "2023-04-20" @default.
- W3211318902 modified "2023-10-15" @default.
- W3211318902 title "Generative networks for precision enthusiasts" @default.
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- W3211318902 doi "https://doi.org/10.21468/scipostphys.14.4.078" @default.
- W3211318902 hasPublicationYear "2023" @default.
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