Reviews: Proximal Deep Structured Models

The paper makes a nice conceptual step of embedding proximal optimization algorithms in a deep neural network framework as an RNN. This conceptual move is new as far as I know. It is illuminating for me and I believe it will be found useful in further work. The immediate benefits are nice but not too significant: the new view enables a natural GPU implementation of proximal optimization methods, and bring small (close to insignificant) empirical result improvements. The week side of the paper is presentation quality: Some notation is used without definitions, central concepts (like the dual function) are used without definition, which makes the paper hard to read for people not very familiar with proximal methods.