3 papers
hep-ph2020
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…
cs.CV2020
Generative Classifiers as a Basis for Trustworthy Image Classification
Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe +1
With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainabil…
hep-ph2020
Invertible Networks or Partons to Detector and Back Again
Marco Bellagente, Anja Butter, Gregor Kasieczka +5
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simul…