# Machine learning challenges in theoretical HEP

@article{Carrazza2017MachineLC, title={Machine learning challenges in theoretical HEP}, author={Stefano Carrazza}, journal={Journal of Physics: Conference Series}, year={2017}, volume={1085} }

In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP. We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools. Finally, we provide an outlook about future applications and developments…

## 10 Citations

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