Due to increasing travel restrictions as well as CERN limiting strongly the possibility to have meetings on site, we reluctantly had to take the decision to postpone the IML workshop to fall 2020.
Meanwhile, IML vidyo monthly meetings will take place on Tuesday 3PM to 5PM:
Please send email to firstname.lastname@example.org if you'd like to present there, with a short abstract and your availability for these meetings.
Please make sure to be registered to email@example.com CERN egroup, to be informed about further developments.
This is the fourth annual workshop of the LPCC inter-experimental machine learning working group. As 2019 edition, it will take place at CERN, and everyone interested in ML for HEP is invited! Remote participation will be supported via the Vidyo and CERN webcast services.
The following structure is anticipated:
- Tuesday 2nd June : hands-on tutorials
- Wednesday 3rd June : morning : invited talks (confirmed speakers Peter Battaglia (DeepMind), Ulrich Koethe (U Heidelberg), Amir Farbin (UTA), Kazuhiro Terao (SLAC) , afternoon industry session
- Thursday 4th/Friday 5th : contributed talks
Abstract submission is now opened until Thursday 30th April.
For the contributed talks, the following (non exclusive) Tracks have been defined:
- 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 phenomenology and theory
- ML for particle accelerators