Reviews: Efficient Forward Architecture Search

This paper proposes novel neural architecture search method dubbed Petridish which is based on gradient boosting of "weak learners" (i.e. Originality: The main contribution of the paper is applying basic ideas from gradient-boosting of weak learners to the task of neural architecture search. This is an original idea, which allows a more guided exploration of the space of neural architectures compared to the random steps done, e.g. in evolutionary algorithms. Most related work is adequately discussed. The connection/differences to NAS methods combining network morphisms with evolutionary algorithms should be discussed in more detail as these explore the search space based on similar steps (modifying a model by small incremental additions) but select steps randomly and not based on gradient boosting.