S'authentifier

Learning to Discover

19–29 avr. 2022
Institut Pascal
Fuseau horaire Europe/Paris

Contact

Session

Dealing with Uncertainties workshop

UNC
21 avr. 2022, 08:55
Institut Pascal

Institut Pascal

Présidents de session

Dealing with Uncertainties workshop: Thursday morning

  • Yann COADOU (CPPM, Aix-Marseille Université, CNRS/IN2P3)

Dealing with Uncertainties workshop: Thursday afternoon

  • Il n'a pas de président de session pour ce bloc

Dealing with Uncertainties workshop: Friday morning

  • Anja Butter (ITP Heidelberg)

Dealing with Uncertainties workshop: Friday afternoon

  • Il n'a pas de président de session pour ce bloc

Documents de présentation

Aucun document.

  1. 56. Workshop introduction
    21/04/2022 09:00
  2. 57. Dealing with statistical and systematical uncertainties in High Energy Physics
    Nicolas Berger (LAPP Annecy)
    21/04/2022 09:15
  3. 59. MAPIE - Model Agnostic Prediction Interval Estimator : a sk-learn contrib
    Nicolas Brunel (Quantmetry), Thomas Morzadec (quantmetry), Vianney Taquet (Quantmetry), Vincent Blot (quantmetry)
    21/04/2022 11:30
  4. 63. Breakout session : MAPIE on HEP data
    21/04/2022 14:15
  5. 60. Breakout sessions : public dataset for evaluation of systematics uncertainties strategies
    21/04/2022 16:00
  6. 58. Uncertainties in Deep Learning
    Geoffrey Daniel (CEA)
    22/04/2022 09:00

    Deep learning algorithms, based on deep neural networks, have been deployed for numerous applications over the past few years, especially in the fields of image processing and natural language processing. Their relevance are now studied for scientific applications, for instance, as new methods to solve inverse problems or as surrogate models to accelerate computations in complex simulations....

    Aller à la page de la contribution
  7. 62. Breakout sessions
    22/04/2022 11:00
  8. 61. Simulation-based inference: a cautionary tale
    Gilles Louppe (University of Liège)
    22/04/2022 14:00

    Simulation-based inference (SBI) enables approximate Bayesian inference when high-fidelity computer simulators are used to describe phenomena of interest. SBI algorithms have quickly developed and matured over the past years and are already in use across many domains of science such as particle physics, astronomy, cosmology, neuroscience or epidemiology.
    Inference, however, remains only...

    Aller à la page de la contribution
  9. 64. Break-out sessions conclusion
    22/04/2022 16:00
Ordre du jour en construction...