Machine Learning is now potentially impacting many aspects of physics.
This workshop covers current development with Machine Learning at IN2P3 and CEA-IRFU.
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
-
Special contribution
Physical presence on site at CC-IN2P3 Lyon from 10:30 Wednesday to 4:30 Thursday is recommended.
A Renavisio video conference has been booked (see details attached, and renavision user guide )
Zoom video conference: https://zoom.us/j/300282656
Please register and make sure to tick your physical attendance on Wed and Thursday and for Dinner Wed night (which we will organize once we have a guess on the attendance ).
Please make sure you are subscribed to MACHINE-LEARNING-L@in2p3.fr on IN2P3 listserv to keep up to date with ML.
Getting to CC-IN2P3 is just 15' tram from main train station Lyon-Part-Dieu plan
Do not forget to bring an ID to enter CC IN2P3!
Organisation : Valérie Gautard (CEA/IRFU), David Rousseau (LAL)
Organisation locale : Hayette Aidel, Sébastien Gadrat, Fabio Hernandez (CC-IN2P3)
