Reviews: Transfer Learning with Neural AutoML

This paper applies both multi-task training and transfer learning to AutoML. The paper extends the ideas presented in the Neural Architectura Search (NAS) technique (Barret Zoph and Quoc V. Le. The authors maintain the two-layer solution, with one network "the controller" choosing the architectural parameters for the "child" network which is used to solve the targeted task. The performance of the child network is fed back to the controller network to influence its results. The novelty of this paper is in the way this two-layer solution is used.