Name: IML Machine Learning Working Group
Start: 2020-05-05T16:00:00+02:00
End: 2020-05-05T18:00:00+02:00
Location: Zoom-only

- 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)
  
  News
- 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))
  
  efNN_IML.pdf
  
  link2paper
- 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)
  
  IML_05.05.2020_JoseClavijo.pdf
  
  link2paper
- 16:45 → 17:05
  
  IML Citation repository 20m
  
  Speakers: Ben Nachman (Lawrence Berkeley National Lab. (US)), Matthew Feickert (Univ. Illinois at Urbana Champaign (US))
  
  2020-05-05.pdf