Monday
19 Oct/20
09:00 (Europe/Zurich)
Ends: 23 Oct/20 18:10

4th Inter-experiment Machine Learning Workshop

The event will take place remotely. Please make sure to be registered  to  lhc-machinelearning-wg@cern.ch CERN egroup, to be informed about further developments.

Agenda might still be slightly adjusted

 

This is the fourth annual workshop of the LPCC inter-experimental machine learning working group. 

The structure is the following :

  • Monday 19th Oct vPM : hands-on hls4ml tutorial 
  • Tuesday 20th Oct : Plenary
  • Wednesday 21st 10AM-5PM : workshop session, 5PM plenary
  • Thursday 22nd 9AM-4PM : workshop session, 4PM Deep Dive on Graph Networks for Learning Simulation (Alvaro Sanchez-Gonzalez & Peter Battaglia, Deepmind), 5PM Tracking with Graph Network walkthrough  
  • Friday 23 :  10 AM - 6PM : workshop session

All talks will be recorded.

For the contributed talks, the following (non exclusive) Tracks have been defined:

  1. ML for data reduction : Application of Machine Learning to data reduction, reconstruction, building/tagging of intermediate object
  2. ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference
  3. 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
  4. Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis
  5. ML algorithms : Machine Learning development across applications
  6. ML infrastructure : Hardware and software for Machine Learning
  7. ML training, courses and tutorials
  8. ML open datasets and challenges
  9. ML for astroparticle
  10. ML for experimental particle physics
  11. ML for phenomenology and theory
  12. ML for particle accelerators
  13. Other

This workshop is organized by the CERN IML coordinators. To keep up to date with ML at LHC, please register to lhc-machinelearning-wg@cern.ch CERN egroup.

 

The Zoom coordinates are attached to the timetable page as material.

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