Reviews: Deep Active Learning with a Neural Architecture Search

This paper proposed a method for doing active learning (AL) where in each AL iteration the optimization is done over network architecture and the underlying parameters, as opposed to other methods which fixes the architecture and only optimizes the parameters. These two optimizations are done separately, by first performing a local search among models of monotonically increasing complexity and then optimizing parameters of the obtained architecture. The authors used this method with three different active learning algorithms and showed that their method improved performance of these ALs. The paper is very well-written and clear. The problem of architectural optimization is also of great importance in the field.