Machine learning attracts a lot of interest in the fields of cosmology and time-domain astronomy and may potentially lead to major breakthroughs. Its adoption by the scientific community has been increasing dramatically in the past few years. Current progress in the machine learning community can simultaneously bring a lot to ours and must be monitored closely.
Among developments of interest for the astronomy community, probabilistic machine learning models, especially Bayesian neural networks, bring an estimation of uncertainty and combine deep neural networks architecture with Bayesian inference.
Cosmologists also rely a lot on forward modeling and often face intractable likelihoods. Modern techniques like simulation-based inference and differentiable programming can be very valuable for model selection and model parameters inference.
Time series analyses have relied for some time on the use of recurrent neural networks, more recently on transformers. Some other analyses have been using convolutional neural networks, or graph neural networks. Could new architectures like graph transformers be relevant in some fields? Can those be made probabilistic?
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.
This workshop is part of the LSSTC Enabling Science effort and will give opportunity to younger scientists to apply for a grant covering lodging and part of the conference fees. Application can be made through a dedicated page.
AstroParticule & Cosmologie (APC)
10 rue Alice Domon et Léonie Duquet