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):
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ML for data reduction : Application of Machine Learning to data reduction, reconstruction, building/tagging of intermediate object
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ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference
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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
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Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis
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ML algorithms : Machine Learning development across applications
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ML infrastructure : Hardware and software for Machine Learning
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ML training, courses and tutorials
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ML open datasets and challenges
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ML for astroparticle
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ML for experimental particle physics
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ML for nuclear physics
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ML for phenomenology and theory
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ML for particle accelerators
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Special contribution
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)
