The following, non-unique, tracks have been defined
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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
