  We reduce the computational cost of Neural AutoML with transfer learning.
AutoML relieves human effort by automating the design of ML algorithms. Neural
AutoML has become popular for the design of deep learning architectures,
however, this method has a high computation cost. To address this we propose
Transfer Neural AutoML that uses knowledge from prior tasks to speed up network
design. We extend RL-based architecture search methods to support parallel
training on multiple tasks and then transfer the search strategy to new tasks.
On language and image classification tasks, Transfer Neural AutoML reduces
convergence time over single-task training by over an order of magnitude on
many tasks.
