Machine Learning at GGI - Conference

Session

HEP

5 Sept 2022, 16:45

5. Using Machine Learning for Physics: The Underlying Mathematical Assumptions

Jeff Byers

05/09/2022, 16:45

3. Product Jacobi-Theta Boltzmann machines with score matching

Andrea Pasquale

05/09/2022, 17:20

28. Style-based quantum generative adversarial networks for Monte Carlo events

Stefano Carrazza

06/09/2022, 16:45

27. PDF determination: from NN fitting to posterior sampling

Alessandro Candido

06/09/2022, 17:20

7. The LHC Physics case

Marco Zanetti (Istituto Nazionale di Fisica Nucleare), Marco Zanetti (CERN)

07/09/2022, 10:40

8. Learning New Physics from a Machine

Gaia Grosso

07/09/2022, 11:15

9. Efficient nonparametric methods for statistical anomaly detection

Marco Letizia

07/09/2022, 11:50

17. Challenges for unsupervised anomaly detection in particle physics

Katherine Fraser

07/09/2022, 12:25

10. Parametrized classifiers for optimal EFT sensitivity

Giuliano Panico

08/09/2022, 14:30

31. Towards an optimal global SMEFT fit with machine learning

Jaco Ter Hoeve

08/09/2022, 15:05

11. Exploring EFT with ML at the LHC

Robert Schöfbeck

08/09/2022, 15:40

12. PHYSTAT Activities

Louis Lyons

08/09/2022, 16:45

32. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models

Manuel Szewc

08/09/2022, 17:20

14. Particle Cloud Generation with Message Passing GANs

Raghav Kansal

09/09/2022, 11:15

15. Gradient estimators for normalising flows

Piotr Bialas

09/09/2022, 11:50

16. Machine learning for supersymmetric matrix models

Enrico Rinaldi

09/09/2022, 12:25

4. TBA

29. TBA

30. TBA

13. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models

Manuel Szewc