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Machine Learning at GGI - Conference

5–9 Sept 2022
Europe/Rome timezone

Session

HEP

5 Sept 2022, 16:45

Presentation materials

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  1. 5. Using Machine Learning for Physics: The Underlying Mathematical Assumptions
    Jeff Byers
    05/09/2022, 16:45
  2. 3. Product Jacobi-Theta Boltzmann machines with score matching
    Andrea Pasquale
    05/09/2022, 17:20
  3. 28. Style-based quantum generative adversarial networks for Monte Carlo events
    Stefano Carrazza
    06/09/2022, 16:45
  4. 27. PDF determination: from NN fitting to posterior sampling
    Alessandro Candido
    06/09/2022, 17:20
  5. 7. The LHC Physics case
    Marco Zanetti (Istituto Nazionale di Fisica Nucleare), Marco Zanetti (CERN)
    07/09/2022, 10:40
  6. 8. Learning New Physics from a Machine
    Gaia Grosso
    07/09/2022, 11:15
  7. 9. Efficient nonparametric methods for statistical anomaly detection
    Marco Letizia
    07/09/2022, 11:50
  8. 17. Challenges for unsupervised anomaly detection in particle physics
    Katherine Fraser
    07/09/2022, 12:25
  9. 10. Parametrized classifiers for optimal EFT sensitivity
    Giuliano Panico
    08/09/2022, 14:30
  10. 31. Towards an optimal global SMEFT fit with machine learning
    Jaco Ter Hoeve
    08/09/2022, 15:05
  11. 11. Exploring EFT with ML at the LHC
    Robert Schöfbeck
    08/09/2022, 15:40
  12. 12. PHYSTAT Activities
    Louis Lyons
    08/09/2022, 16:45
  13. 32. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models
    Manuel Szewc
    08/09/2022, 17:20
  14. 14. Particle Cloud Generation with Message Passing GANs
    Raghav Kansal
    09/09/2022, 11:15
  15. 15. Gradient estimators for normalising flows
    Piotr Bialas
    09/09/2022, 11:50
  16. 16. Machine learning for supersymmetric matrix models
    Enrico Rinaldi
    09/09/2022, 12:25
  17. 4. TBA
  18. 29. TBA
  19. 30. TBA
  20. 13. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models
    Manuel Szewc
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