text:   Learning Encoder for Galaxy Cluster Cosmology",4,"['We have a new paper out today!  tl;dr: if we’re careful about how we engineer our deep learning architectures, we can build models that are inherently more interpretable and trustworthy. \n\n<LINK>', 'We explore an ML approach to infer cosmology from cluster mock observations.  Figure 5 shows our architecture.  It mimics a human approach to the same problem, which makes our model inherently more interpretable! https://t.co/lB0x0OfkA8', 'We spend most of the paper interpreting.  We show that ML can be used to make a new discovery, rather than just being a tool to black-box your way from point A to point B.  Our model pointed us to a new self-calibration mode for x-ray surveys of galaxy clusters, in S 5.2.1', ""This is my favorite research thread I've ever pursued, and as a result, the paper long, both in time invested and ink spilt.  Good collaborators are hard to find, and it was a tremendous pleasure to work with @AlexeyVikhlinin on this research!""]",21,12,926