title: DeepArchitect: Automatically Designing and Training Deep Architectures
abstract: In deep learning, performance is strongly affected by the choice of architecture
  and hyperparameters. While there has been extensive work on automatic hyperpa-
  rameter optimization for simple spaces, complex spaces such as the space of deep
  architectures remain largely unexplored. As a result, the choice of architecture is
  done manually by the human expert through a slow trial and error process guided
  mainly by intuition. In this paper we describe a framework for automatically
  designing and training deep models. We propose an extensible and modular lan-
  guage that allows the human expert to compactly represent complex search spaces
  over architectures and their hyperparameters. The resulting search spaces are tree-
  structured and therefore easy to traverse. Models can be automatically compiled to
  computational graphs once values for all hyperparameters have been chosen. We
  can leverage the structure of the search space to introduce different model search
  algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen-
  tial model-based optimization (SMBO). We present experiments comparing the
  different algorithms on CIFAR-10 and show that MCTS and SMBO outperform
  random search. We also present experiments on MNIST, showing that the same
  search space achieves near state-of-the-art performance with a few samples. These
  experiments show that our framework can be used effectively for model discov-
  ery, as it is possible to describe expressive search spaces and discover competitive
  models without much effort from the human expert. Code for our framework and
  experiments has been made publicly available
authors: Renato Negrinho Geoff Gordon
hIndex: Geoff Gordon 58