Speaker
Stefan Wunsch
(KIT - Karlsruhe Institute of Technology (DE))
Description
TMVA has been a pioneering effort which set a milestone for machine-learning (ML) in high-energy physics (HEP) more than ten years ago and remains in use in numerous analyses of LHC experiments.
On the other hand, the ML landscape explosively evolved during these years and - as industry stepped in - ML became suddenly one of the most active fields in science. This talk discusses how TMVA can make the difference for ML in HEP in the future by combining the developments in the ML community and recent developments in the ROOT framework.
Primary authors
Stefan Wunsch
(KIT - Karlsruhe Institute of Technology (DE))
Lorenzo Moneta
(CERN)
