Name: UW EPE Seminar - Taylor Faucett: Physicists Learning from Machines Learning
Start: 2020-11-19T12:30:00-08:00
End: 2020-11-19T13:30:00-08:00
Location: zoom

- 12:30 → 13:20
  
  Physicists Learning from Machines Learning: Smart but Interpretable Neural Networks for Physics at the LHC 50m
  
  Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach, which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without sacrificing performance improvements. We apply this approach to benchmark high-energy problems of fat-jet classification and electron identification. In each case, we find simple new observables that provide additional classification power and novel insights into the nature of the problem.
  
  Bio
  Taylor Faucett is a graduate student at the University of California, Irvine where he works on research applications of big data techniques and machine learning to topics in high-energy physics. In particular, his interests lie in the design of novel machine learning architectures which transform complex black-box neural networks into interpretable models which can yield new insights into physics studied at the LHC. Currently, Taylor is studying as a Chateaubriand Fellow at the Université Clermont Auvergne (UCA) in Clermont-Ferrand, France.
  
  References
  https://arxiv.org/abs/2010.11998
  https://arxiv.org/abs/2011.01984
  
  Speaker: Taylor James Faucett (University of California, Irvine)
  
  Seminar_Talk.pdf