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Machine Learning at GGI - Conference

Sep 5 – 9, 2022
Europe/Rome timezone

Session

HEP

Sep 5, 2022, 4:45 PM

Presentation materials

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  1. 5. Using Machine Learning for Physics: The Underlying Mathematical Assumptions
    Jeff Byers
    9/5/22, 4:45 PM
  2. 3. Product Jacobi-Theta Boltzmann machines with score matching
    Andrea Pasquale
    9/5/22, 5:20 PM
  3. 28. Style-based quantum generative adversarial networks for Monte Carlo events
    Stefano Carrazza
    9/6/22, 4:45 PM
  4. 27. PDF determination: from NN fitting to posterior sampling
    Alessandro Candido
    9/6/22, 5:20 PM
  5. 7. The LHC Physics case
    Marco Zanetti (Istituto Nazionale di Fisica Nucleare), Marco Zanetti (CERN)
    9/7/22, 10:40 AM
  6. 8. Learning New Physics from a Machine
    Gaia Grosso
    9/7/22, 11:15 AM
  7. 9. Efficient nonparametric methods for statistical anomaly detection
    Marco Letizia
    9/7/22, 11:50 AM
  8. 17. Challenges for unsupervised anomaly detection in particle physics
    Katherine Fraser
    9/7/22, 12:25 PM
  9. 10. Parametrized classifiers for optimal EFT sensitivity
    Giuliano Panico
    9/8/22, 2:30 PM
  10. 31. Towards an optimal global SMEFT fit with machine learning
    Jaco Ter Hoeve
    9/8/22, 3:05 PM
  11. 11. Exploring EFT with ML at the LHC
    Robert Schöfbeck
    9/8/22, 3:40 PM
  12. 12. PHYSTAT Activities
    Louis Lyons
    9/8/22, 4:45 PM
  13. 32. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models
    Manuel Szewc
    9/8/22, 5:20 PM
  14. 14. Particle Cloud Generation with Message Passing GANs
    Raghav Kansal
    9/9/22, 11:15 AM
  15. 15. Gradient estimators for normalising flows
    Piotr Bialas
    9/9/22, 11:50 AM
  16. 16. Machine learning for supersymmetric matrix models
    Enrico Rinaldi
    9/9/22, 12:25 PM
  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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