Philip Keicher
Published 2023-01-23Version 1
Machine learning is essential in many aspects of top-quark related physics in the ATLAS and CMS Collaborations. This work aims to give a brief overview over current applications in the two collaborations as well as on-going studies for future applications. Copyright 2023 CERN for the benefit of the ATLAS and CMS Collaborations. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license
Comments: Talk at the 15th International Workshop on Top Quark Physics, Durham, UK, 4-9 September 2022
MLaaS4HEP: Machine Learning as a Service for HEP
Machine Learning as a Service for HEP
Towards Robust Averaging of Exclusive B ->X_u l nu Measurements
![QR code for arXiv:2301.09534 [hep-ex]](http://oss.arxitics.com/images/favicon.svg)