113 citations · 168 across the 3 of their papers we have counts for
4 papers · 1 filter
How to GAN away Detector Effects
Marco Bellagente, Anja Butter, Gregor Kasieczka +2
LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations…
CapsNets Continuing the Convolutional Quest
Sascha Diefenbacher, Hermann Frost, Gregor Kasieczka +2
Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show h…
Deep-Learning Jets with Uncertainties and More
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka +3
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main featur…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…