Speaker
Zhuoni Qian
(Hangzhou Normal University)
Description
Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Thus the interpretable machine learning (IML) framework become necessary in the HEP large data era. I am demonstraintg how the IML framework can be achieved with detailed analysis on a few LHC processes as example, and explaining further application and interpretation concepts.
Primary author
Zhuoni Qian
(Hangzhou Normal University)
