The workshop will take place remotely only, on mornings : 9AM to 1PM
Machine Learning is now potentially impacting many aspects of physics.
This workshop covers current development with Machine Learning at IN2P3 and CEA-IRFU, following up from the Jan 2020 workshop
Submission of contributions is now closed.
The following non exclusive Tracks have been defined (a contribution can be relevant to 2 tracks, preferably not more):
ML for data reduction : Application of Machine Learning to data reduction, reconstruction, building/tagging of intermediate object
ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference
ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model
Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis
ML algorithms : Machine Learning development across applications
ML infrastructure : Hardware and software for Machine Learning
ML training, courses and tutorials
ML open datasets and challenges
ML for astroparticle
ML for experimental particle physics
ML for nuclear physics
ML for phenomenology and theory
ML for particle accelerators
The workshop will be on zoom. Connection details are sent by mail to registrants.
Please make sure you are subscribed to MACHINE-LEARNING-L@in2p3.fr on IN2P3 listserv to keep up to date with ML.
Organisation : Valérie Gautard (CEA/IRFU), David Rousseau (IJCLab)