48 citations · 98 across the 6 of their papers we have counts for
6 papers
Calibrating Bayesian Generative Machine Learning for Bayesiamplification
Sebastian Bieringer, Sascha Diefenbacher, Gregor Kasieczka +1
Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural netw…
Classifier Surrogates: Sharing AI-based Searches with the World
Sebastian Bieringer, Gregor Kasieczka, Jan Kieseler +1
In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underly…
AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization
Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen +1
Uncertainty estimation is a key issue when considering the application of deep neural network methods in science and engineering. In this work, we introduce a novel algorithm that…
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen +1
MALA is a popular gradient-based Markov chain Monte Carlo method to access the Gibbs-posterior distribution. Stochastic MALA (sMALA) scales to large data sets, but changes the targ…
Calomplification -- The Power of Generative Calorimeter Models
Sebastian Bieringer, Anja Butter, Sascha Diefenbacher +7
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especia…
Measuring QCD Splittings with Invertible Networks
Sebastian Bieringer, Anja Butter, Theo Heimel +4
QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks…