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

Session

Contribution talks

4 mars 2020, 15:10

Pierre-Gilles de Gennes Amphitheater (PCCP, APC laboratory, Université de Paris)

32. Star-galaxy separation via Gaussian Processes with Neural Network Dual Kernels

Dr Imene Goumiri (llnl)

04/03/2020 15:10

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

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

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

34. Deep learning for a faster Hamiltonian Monte Carlo sampler

M. Marc Arène (APC)

05/03/2020 14:40

35. Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

M. Mike Walmsley (University of Oxford)

05/03/2020 15:00

36. Bayesian parameter estimation using conditional variational autoencoders for gravitational wave astronomy

M. Hunter Gabbard (University of Glasgow)

05/03/2020 15:20

37. Denoising gravitational wave signals with a variational autoencoder

Dr Philippe BACON (Laboratoire Astroparticule et Cosmologie)

05/03/2020 16:10

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

39. The devil is in the details: interpreting probabilities from machine learning

Alex Malz (German Centre of Cosmological Lensing)

05/03/2020 16:50

45. Contrib 5