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.
The timetable has not been filled yet.
