HEPML-LivingReview and HEP-ML-Resources
These are complementary resources where the Living Review provides in-depth technical analysis of ML methods applied to specific HEP problems, while HEP-ML-Resources serves as a curated index of external learning materials and tools to support that learning journey.
About HEPML-LivingReview
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.
About HEP-ML-Resources
iml-wg/HEP-ML-Resources
Listing of useful learning resources for machine learning applications in high energy physics (HEPML)
Organized collection of curated lectures, tutorials, datasets, and papers spanning foundational ML concepts (GANs, deep learning, boosted decision trees) through domain-specific applications in particle detection and reconstruction. Structures resources by learning level and format—from introductory seminars and hands-on Jupyter notebooks to archived summer school curricula—enabling practitioners to navigate ML adoption across the HEP ecosystem including PyTorch, Keras, scikit-learn, and TMVA frameworks.
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