Publications (6)
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…
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…
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…
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…
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…