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Machine Learning at GGI - Conference

Sep 5 – 9, 2022
Europe/Rome timezone

Contribution List

48 out of 48 displayed
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  1. 1. Registration
    9/5/22, 9:00 AM
  2. 2. Welcome
    9/5/22, 11:00 AM
  3. 33. Machine Learning and Large Scale Quantum Systems
    Eliska Greplova
    9/5/22, 11:15 AM
  4. 34. Machine Learning for quantum noise discrimination
    Stefano Martina
    9/5/22, 11:50 AM
  5. 35. Spectral Pruning for Neural Network
    Lorenzo Giambagli
    9/5/22, 12:25 PM
  6. 19. A semi-supervised approach to dark matter searches in direct detection data with machine learning
    Andre Scaffidi
    9/5/22, 2:30 PM
  7. 18. Two machine learning based methods for field-level inference of cosmological parameters
    Moritz Münchmeyer
    9/5/22, 3:05 PM
  8. 39. Recurrent NNs for fast signal discovery in pulsar timing arrays
    Marat Freytsis
    9/5/22, 3:40 PM
  9. 5. Using Machine Learning for Physics: The Underlying Mathematical Assumptions
    Jeff Byers
    9/5/22, 4:45 PM
    HEP
  10. 3. Product Jacobi-Theta Boltzmann machines with score matching
    Andrea Pasquale
    9/5/22, 5:20 PM
    HEP
  11. 40. Quantum Computing & Quantum Machine Learning
    Filippo Caruso
    9/6/22, 10:40 AM
  12. 41. Algorithmic exploitation of the energy landscape in non-convex neural networks
    Riccardo Zecchina
    9/6/22, 11:15 AM
  13. 42. Coarse-graining for classical and quantum systems
    Cecilia Clementi
    9/6/22, 11:50 AM
  14. 43. Bridge between Classical & Quantum Machine Learning
    Jack Araz
    9/6/22, 12:25 PM
  15. 20. Cosmological simulations of Galaxy formation supervised by Machine Learning algorithms
    Luca Graziani
    9/6/22, 2:30 PM
  16. 21. Reconstructing blended galaxies with Machine Learning
    Lavanya Nemani
    9/6/22, 3:05 PM
  17. 22. Digging into data cubes with Deep Learning
    Giuseppe Longo
    9/6/22, 3:40 PM
  18. 28. Style-based quantum generative adversarial networks for Monte Carlo events
    Stefano Carrazza
    9/6/22, 4:45 PM
    HEP
  19. 27. PDF determination: from NN fitting to posterior sampling
    Alessandro Candido
    9/6/22, 5:20 PM
    HEP
  20. 7. The LHC Physics case
    Marco Zanetti (Istituto Nazionale di Fisica Nucleare), Marco Zanetti (CERN)
    9/7/22, 10:40 AM
    HEP
  21. 8. Learning New Physics from a Machine
    Gaia Grosso
    9/7/22, 11:15 AM
    HEP
  22. 9. Efficient nonparametric methods for statistical anomaly detection
    Marco Letizia
    9/7/22, 11:50 AM
    HEP
  23. 17. Challenges for unsupervised anomaly detection in particle physics
    Katherine Fraser
    9/7/22, 12:25 PM
    HEP
  24. 44. A theory explaining the limits and performances of algorithms based on simulated annealing in solving sparse hard inference problems
    Federico Ricci Tersenghi
    9/7/22, 2:30 PM
  25. 45. Quantum associative memory for quantum image recognition
    Sreetama Das
    9/7/22, 3:05 PM
  26. 46. Reinforcement Learning for Quantum Technologies
    Florian Marquardt
    9/7/22, 3:40 PM
  27. 48. Applying DNN to extract nuclear matter properties from neutron stars
    Marcio Ferreira
    9/7/22, 4:45 PM
  28. 6. Visit to Galileo's Villa il Gioiello
    9/7/22, 6:00 PM
  29. 23. NCPA mitigation using PCA and Neural Network
    Alessandro Terreri
    9/8/22, 10:40 AM
  30. 24. Accelerating cosmological inference with Deep Learning
    Marco Bonici
    9/8/22, 11:15 AM
  31. 25. Graph-Convolutional Neural Networks as a new tool to extract clustering information of the Cosmic web
    Farida Farsian
    9/8/22, 11:50 AM
  32. 26. Optimal compression of the cosmic 21-cm signal
    David Prelogovic
    9/8/22, 12:25 PM
  33. 10. Parametrized classifiers for optimal EFT sensitivity
    Giuliano Panico
    9/8/22, 2:30 PM
    HEP
  34. 31. Towards an optimal global SMEFT fit with machine learning
    Jaco Ter Hoeve
    9/8/22, 3:05 PM
    HEP
  35. 11. Exploring EFT with ML at the LHC
    Robert Schöfbeck
    9/8/22, 3:40 PM
    HEP
  36. 12. PHYSTAT Activities
    Louis Lyons
    9/8/22, 4:45 PM
    HEP
  37. 32. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models
    Manuel Szewc
    9/8/22, 5:20 PM
    HEP
  38. 47. Translation and Rotation Equivariant Normalizing Flows for cosmology and beyond
    Uros Seljak
    9/9/22, 10:40 AM
  39. 14. Particle Cloud Generation with Message Passing GANs
    Raghav Kansal
    9/9/22, 11:15 AM
    HEP
  40. 15. Gradient estimators for normalising flows
    Piotr Bialas
    9/9/22, 11:50 AM
    HEP
  41. 16. Machine learning for supersymmetric matrix models
    Enrico Rinaldi
    9/9/22, 12:25 PM
    HEP
  42. 36. Rapid Exploration of Topological Band Structures Using Deep Learning
    Vittorio Peano
    9/9/22, 2:30 PM
  43. 37. Renormalized Mutual Information for Artificial Scientific Discovery
    Leopoldo Sarra
    9/9/22, 3:05 PM
  44. 38. Quantum density peak clustering
    Lorenzo Buffoni
    9/9/22, 3:40 PM
  45. 4. TBA
    HEP
  46. 29. TBA
    HEP
  47. 30. TBA
    HEP
  48. 13. Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models
    Manuel Szewc
    HEP