iml-wg/HEPML-LivingReview
Living Review of Machine Learning for Particle Physics
Organizes and curates research papers by application domain—jet tagging, calorimeter simulation, anomaly detection, unfolding—making it easier to navigate ML developments across experimental and phenomenological HEP workflows. Built with community contributions and indexed via the INSPIRE REST API, the review continuously tracks publications by category and provides BibTeX citations for reproducible referencing in physics papers.
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TeX
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Last pushed
Mar 02, 2026
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