Workshop presentation

Machine learning attracts a lot of interest in the fields of cosmology and gravitational-wave astronomy and may potentially lead to major breakthroughs. Its adoption by the scientific community is increasing dramatically but it does not yet belong to the toolbox of 'off-the-shelf' algorithms. One of the reasons is that built-in uncertainty estimation, which is core to the evaluation of any scientific measurement and analysis, is not yet common in machine learning models.

Such limitation is on the verge to be overcome by the emergence of probabilistic machine learning models and algorithms. Among them, recent models called Bayesian neural networks, which combine machine learning and Bayesian statistics, use new (deep) neural networks architectures to enable Bayesian inference, and have received a great attention from the artificial intelligence community over the past few years.

This workshop will give the participants the opportunity to learn more about these emerging methods and how to use and exploit them in their research. The workshop program includes invited lectures and tutorials from major computer science experts and contributed talk and poster session aimed at sharing experience between physicists on the practical applications of machine learning.

The workshop is intended to researchers and students that are familiar with machine learning, use this type of algorithms for their own work, and want to learn about the advanced techniques related to Bayesian deep learning.

March 4-6




Event Schedule

FRIDAY 6th March

TensorFlow Probability

Junpeng Lao & Brian Patton, Google

Microsoft France,
Paris, France



Alexander Titterton, Graphcore

Microsoft France,
Paris, France



Laurent Daudet, LightOn

Microsoft France,
Paris, France


Microsoft Azure

Alexandre Jean, Microsoft

Microsoft France,
Paris, France



Microsoft France,
Paris, France


Coffee break and networking

Microsoft France,
Paris, France

Registration & Pricing

Registrations are closed.

Submit Your Contribution Work

Contributions are closed.


Wednesday 4th March

An introduction to
Bayesian Deep Learning

Dr Frédéric Pennerath

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

Dr Imene Goumiri

Detection of GW signals from binary neutron star signals using machine learning

Marlin Benedikt Schäfer

Deep learning dark matter map reconstructions and parameter
inference with Dark Energy Survey data

Dr Niall Jeffrey

Neural networks estimation
of the dense-matter equation
of state from neutron-star observables

Filip Morawski

Bayesian analysis and Supernova Photometric Cosmology

Dr Emille Ishida

Thursday 5th March

Tutorial: Bayesian deep learning -

Dr Tom Charnock

TensorFlow Probability

Brian Patton

Deep learning for a faster Hamiltonian Monte Carlo sampler

Marc Arène

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

Mike Walmsley

Bayesian parameter estimation using conditional variational autoencoders
for gravitational wave astronomy

Hunter Gabbard

Denoising gravitational wave signals
with a variational autoencoder

Dr Philippe Bacon



Event Location

APC laboratory
10 rue Alice Domon et Léonie Duquet
75013, Paris