Compression-based complexity measures have been used to construct non-vacuous generalization bounds for deep neural networks. In this talk, Branton DeMoss (University of Oxford) will discuss the relationship between compression, complexity, and the geometry of the loss landscape. Using a geometric complexity measure to track memorization and generalization in some pathological deep learning phenomena like grokking …