IML Machine Learning Working Group




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    • 16:00 16:05
      News 5m
      Speakers: Andrea Wulzer (CERN and EPFL) , David Rousseau (LAL-Orsay, FR) , Gian Michele Innocenti (CERN) , Lorenzo Moneta (CERN) , Loukas Gouskos (CERN) , Paul Seyfert (CERN) , Riccardo Torre (CERN)
    • 16:05 16:25
      Efficiency Parameterization with Neural Networks 20m
      Speakers: Francesco Armando Di Bello (Sapienza Universita e INFN, Roma I (IT)) , Jonathan Shlomi (Weizmann Institute of Science (IL))
    • 16:25 16:45
      Adversarial domain adaptation to reduce sample bias in a classification ML algorithm 20m

      We apply adversarial domain adaptation to reduce sample bias in a classification machine learning algorithm. We add a gradient reversal layer to a neural network to simultaneously classify signal versus background events, while minimising the difference of the classifier response to a background sample using an alternative MC model. We show this on the example of simulated events at the LHC with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification.

      Speakers: Jose Manuel Clavijo Columbie (Deutsches Elektronen-Synchrotron (DE)) , Judith Katzy (Deutsches Elektronen-Synchrotron (DE)) , Paul Glaysher (DESY)
    • 16:45 17:05
      IML Citation repository 20m
      Speakers: Ben Nachman (Lawrence Berkeley National Lab. (US)) , Matthew Feickert (Univ. Illinois at Urbana Champaign (US))