Machine Learning at GGI - Conference

Session

HEP

Sep 5, 2022, 4:45 PM

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

Jeff Byers

9/5/22, 4:45 PM

3. Product Jacobi-Theta Boltzmann machines with score matching

Andrea Pasquale

9/5/22, 5:20 PM

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

Stefano Carrazza

9/6/22, 4:45 PM

27. PDF determination: from NN fitting to posterior sampling

Alessandro Candido

9/6/22, 5:20 PM

7. The LHC Physics case

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

9/7/22, 10:40 AM

8. Learning New Physics from a Machine

Gaia Grosso

9/7/22, 11:15 AM

9. Efficient nonparametric methods for statistical anomaly detection

Marco Letizia

9/7/22, 11:50 AM

17. Challenges for unsupervised anomaly detection in particle physics

Katherine Fraser

9/7/22, 12:25 PM

10. Parametrized classifiers for optimal EFT sensitivity

Giuliano Panico

9/8/22, 2:30 PM

31. Towards an optimal global SMEFT fit with machine learning

Jaco Ter Hoeve

9/8/22, 3:05 PM

11. Exploring EFT with ML at the LHC

Robert Schöfbeck

9/8/22, 3:40 PM

12. PHYSTAT Activities

Louis Lyons

9/8/22, 4:45 PM

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

Manuel Szewc

9/8/22, 5:20 PM

14. Particle Cloud Generation with Message Passing GANs

Raghav Kansal

9/9/22, 11:15 AM

15. Gradient estimators for normalising flows

Piotr Bialas

9/9/22, 11:50 AM

16. Machine learning for supersymmetric matrix models

Enrico Rinaldi

9/9/22, 12:25 PM

4. TBA

29. TBA

30. TBA

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

Manuel Szewc