240 citations · 1.9k across the 47 of their papers we have counts for
3 papers · 1 filter
Know What You Don't Flow
Anja Butter, Sascha Diefenbacher, Tilman Plehn +1
Calibrated learned uncertainties are a key requirement also for generative neural networks in LHC physics. For a toy model with an explicit likelihood we show how a heteroscedastic…
Local Conformal Predictions for Calibrated Surrogates
Suprio Dubey, Henning Bahl, Anja Butter +2
Neural network surrogates for LHC scattering amplitudes require trustworthy uncertainty estimates, a challenging task given the non-Gaussian systematics. We target it using conform…
Scaling laws for amplitude surrogates
Henning Bahl, Victor Bresó-Pla, Anja Butter +1
Scaling laws describing the dependence of neural network performance on the amount of training data, the spent compute, and the network size have emerged across a huge variety of m…