Auto-Keras: An Efficient Neural Architecture Search System

Neural architecture search (NAS) has been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space. Extensive experiments on…

• NS
  National Science Foundation

  Awards: IIS-1718840, #IIS-1750074, IIS-1750074, 1750074, #IIS-1718840, 1718840
• UD
  U.S. Department of Defense
• DA
  Defense Advanced Research Projects Agency

  Award: FA8750