Transfer Learning with Neural AutoML

Understand

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.