Login

3rd IML Machine Learning Workshop

15–18 Apr 2019
CERN
Europe/Zurich timezone
There is a live webcast for this event.

Session

Submitted contributions

16 Apr 2019, 14:00
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium

CERN

400
Show room on map

Conveners

Submitted contributions: Session 2

  • Steven Randolph Schramm (Universite de Geneve (CH))
  • David Rousseau (LAL-Orsay, FR)

Presentation materials

There are no materials yet.

  1. 21. Novelty Detection Meets Collider Physics
    Ms Ying-Ying Li (HKUST)
    16/04/2019, 14:00

    Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to...

    Go to contribution page
  2. 31. Uncertain Networks
    Jennifer Thompson (ITP Heidelberg)
    16/04/2019, 14:30

    Machine learning methods are being increasingly and successfully applied to many different physics problems. However, currently uncertainties in machine learning methods are not modelled well, if at all. In this talk I will discuss how using Bayesian neural networks can give us a handle on uncertainties in machine learning. I will use tagging tops vs. QCD as an example of how these networks...

    Go to contribution page
  3. 28. Exploring SMEFT in VH channel with Machine Learning
    Charanjit Kaur Khosa
    16/04/2019, 15:00

    We use Machine Learning(ML) techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the SMEFT framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the...

    Go to contribution page
  4. 24. The Tracking Machine Learning challenge
    David Rousseau (LAL-Orsay, FR)
    16/04/2019, 15:30

    The HL-LHC will see ATLAS and CMS see proton bunch collisions reaching track multiplicity up to 10.000 charged tracks per event. Algorithms need to be developed to harness the increased combinatorial complexity. To engage the Computer Science community to contribute new ideas, we have organized a Tracking Machine Learning challenge (TrackML). Participants were provided events with 100k 3D...

    Go to contribution page
  5. 55. Talk 1
  6. 56. Talk 2
  7. 57. Talk 3
  8. 58. Talk 4
  9. 59. Talk 5
Building timetable...