Description
Chair: Shirley Ho
The application of Machine Learning in Cosmology nowadays is
pervasive (CNN-based classification, Bayesian ML, normalizing-flow based
inference, diffusion models, ...). Some concepts cherished by (parts
of) the ML community might not (yet?) have found their way to
astrophysics. In this talk, I will describe two of them, adversarial
robustness and dataset distillation, to motivate their possible utility
with the example of turbulence simulations and galaxy morphology extraction.
