Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider

5 Nov 2019Taoli Cheng

Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not only helps us understand the behaviour of neural networks, but also helps improve the performance of deep learning models through proper interpretation... (read more)

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