ML/DL in HEP: from prototypes to production
Theoretical and algorithmic advances, availability of data, and computing power are driving AI. Specifically in the Machine Learning and Deep Learning domains, these advances have opened the door to exceptional perspectives for application in the most diverse fields of science, business and society at large, and notably in High Energy Physics (HEP).
Today, many HEP experiments are working on integrating Machine/Deep Learning into their workflows for different applications: from data quality assurance, to real-time selection of interesting collision events, to simulation and data analysis. Many of studies are going beyond the initial prototyping stage and start facing new challenges:
-- Detailed performance assessment and interpretability of the results
-- Computing resources optimisation
-- Integration in the data processing workflow.
These are some of the themes we would like to explore in the discussion. Please enter below your questions related to one of those fields. Questions on different subject may also be considered. Please send any comment to acat-tc2-2019@cern.ch. Thank you!
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