A year ago, a group of us at UMD took a couple weeks to go through this paper. It's got some interesting insights, but like all theory papers right now, gaps re

A year ago, a group of us at UMD took a couple weeks to go through this paper. It's got some interesting insights, but like all theory papers right now, gaps remain. The construction they present for learning Hamiltonians of low polynomial order doesn't look a ton like any common production neural network modules, and the justification for why we would only be dealing with that Hamiltonian family in practice is unconvincing to me. That said, overall it's worth a close read. Section 2' parts A and B are the best summary of the connection between probability theory and deep learning that i have come across.