Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
Neural Scaling Laws for Boosted Jet Tagging
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First NLO-QCD amplitude-assisted ML regression for longitudinal-boson production rate in di-boson events at the LHC, benchmarked against random forests.
Pretraining data composition can be used to engineer neural scaling laws in hadronic jet classification toward data-heavy rather than model-size-heavy regimes.
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
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