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.