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PCCP Workshop Series : Bayesian Deep Learning for Cosmology and Gravitational waves

4–6 mars 2020
PCCP, APC laboratory, Université de Paris
Fuseau horaire Europe/Paris

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Liste des Contributions

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  1. 50. Logistics information
    04/03/2020 14:00
  2. 46. An Introduction to Bayesian Deep Learning
    Frédéric Pennerath (CentraleSupélec)
    04/03/2020 14:30

    Bayesian Deep Learning (BDL) fills an important gap in the current deep neural networks, no matter powerful they are: in figurative terms, one could say BDL gives to AI the introspective ability to assess its own level of ignorance due to a lack of observations.
    In more technical terms, BDL adopts the view of Bayesian statistics by replacing the weights of neural networks by...

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  3. 32. Star-galaxy separation via Gaussian Processes with Neural Network Dual Kernels
    Dr Imene Goumiri (llnl)
    04/03/2020 15:10
  4. 33. Detection of gravitational-wave signals from binary neutron star signals using machine learning
    Marlin Benedikt Schäfer (Albert Einstein Institut Hannover (AEI Hannover))
    04/03/2020 15:30
  5. 30. Deep learning dark matter map reconstructions and parameter inference with Dark Energy Survey data
    Dr Niall Jeffrey (École normale supérieure)
    04/03/2020 16:10
  6. 31. Neural networks estimation of the dense-matter equation of state from neutron-star observables
    M. Filip Morawski (Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences)
    04/03/2020 16:30
  7. 47. Bayesian analysis and Supernova Photometric Cosmology
    Dr Emille Ishida (LPC-UCA)
    04/03/2020 17:10

    In the end of the 20th century type Ia supernovae provided the first evidence for accelerated cosmic expansion -- completely changing the cosmological model paradigm. Since then, the astronomical community has devote much of its resources to the construction of large scale sky surveys which are expected to achieve first light in the next few years. The upcoming Large Survey of Space and Time...

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  8. 27. Tutorial: Bayesian deep learning - MCMC vs VI
    Dr Tom Charnock (IAP)
    05/03/2020 09:30
  9. 48. TensorFlow Probability
    Brian Patton (Google)
    05/03/2020 14:00
  10. 34. Deep learning for a faster Hamiltonian Monte Carlo sampler
    M. Marc Arène (APC)
    05/03/2020 14:40
  11. 35. Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
    M. Mike Walmsley (University of Oxford)
    05/03/2020 15:00
  12. 36. Bayesian parameter estimation using conditional variational autoencoders for gravitational wave astronomy
    M. Hunter Gabbard (University of Glasgow)
    05/03/2020 15:20
  13. 37. Denoising gravitational wave signals with a variational autoencoder
    Dr Philippe BACON (Laboratoire Astroparticule et Cosmologie)
    05/03/2020 16:10
  14. 38. Solving source separation problem for LISA data analysis with Autoencoders
    Dr Natalia Korsakova (Observatoire de la Côte d'Azur)
    05/03/2020 16:30
  15. 39. The devil is in the details: interpreting probabilities from machine learning
    Alex Malz (German Centre of Cosmological Lensing)
    05/03/2020 16:50
  16. 28. TensorFlow Probability - Brian Patton, Junpeng Lao (TensorFlow Probability - Google)
    06/03/2020 09:30
  17. 29. TensorFlow Probability - Brian Patton, Junpeng Lao (TensorFlow Probability - Google)
    06/03/2020 11:10
  18. 42. Graphcore: Innovation in Machine Intelligence Hardware & Software
    Alexander Titterton (Graphcore)
    06/03/2020 14:00
  19. 44. Large-scale Machine Learning on LightOn’s Optical Processing Units
    Laurent Daudet (Light On)
    06/03/2020 14:45
  20. 43. Microsoft Azure
    Alexandre Jean (Microsoft)
    06/03/2020 15:15
  21. 51. Organizers wrap-up
    06/03/2020 15:35
  22. 45. Contrib 5
  23. 49. logistics information
  24. 14. round table: Variational inference VS. MCMC
  25. 13. Tutorial: overview of the differences between MCMC and Variational Inference
    Dr Tom Charnock (IAP)