The event will take place remotely. 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.
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:
- 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
The Zoom coordinates are attached to the timetable page as material.