title: Simple and efficient architecture search for Convolutional Neural Networks
abstract: Neural networks have recently had a lot of success for many tasks. However, neural
  network architectures that perform well are still typically designed manually
  by experts in a cumbersome trial-and-error process. We propose a new method
  to automatically search for well-performing CNN architectures based on a simple
  hill climbing procedure whose operators apply network morphisms, followed
  by short optimization runs by cosine annealing. Surprisingly, this simple method
  yields competitive results, despite only requiring resources in the same order of
  magnitude as training a single network. E.g., on CIFAR-10, our method designs
  and trains networks with an error rate below 6% in only 12 hours on a single GPU;
  training for one day reduces this error further, to almost 5%.
authors: Thomas Elsken Jan Hendrik Metzen Frank Hutter
hIndex: Frank Hutter 66