Understanding Event-Generation Networks via Uncertainties
Abstract
Generative models and normalizing flow based models have made great progress in recent years both in their theoretical development as well as in a growing number of applications. As such models become applied more and more with it increases the desire for predictive uncertainty to know when to trust the underlying model. In this extended abstract we target the application area of Large Hadron Collider (LHC) simulations and show how to extend normalizing flows with probabilistic Bayesian Neural Network based transformations to model LHC events with uncertainties.
Cite
Text
Bellagente et al. "Understanding Event-Generation Networks via Uncertainties." ICML 2021 Workshops: INNF, 2021.Markdown
[Bellagente et al. "Understanding Event-Generation Networks via Uncertainties." ICML 2021 Workshops: INNF, 2021.](https://mlanthology.org/icmlw/2021/bellagente2021icmlw-understanding/)BibTeX
@inproceedings{bellagente2021icmlw-understanding,
title = {{Understanding Event-Generation Networks via Uncertainties}},
author = {Bellagente, Marco and Luchmann, Michel and Haussmann, Manuel and Plehn, Tilman},
booktitle = {ICML 2021 Workshops: INNF},
year = {2021},
url = {https://mlanthology.org/icmlw/2021/bellagente2021icmlw-understanding/}
}