  The recent advent of automated neural network architecture search led to
several methods that outperform state-of-the-art human-designed architectures.
However, these approaches are computationally expensive, in extreme cases
consuming GPU years. We propose two novel methods which aim to expedite this
optimization problem by transferring knowledge acquired from previous tasks to
new ones. First, we propose a novel neural architecture selection method which
employs this knowledge to identify strong and weak characteristics of neural
architectures across datasets. Thus, these characteristics do not need to be
rediscovered in every search, a strong weakness of current state-of-the-art
searches. Second, we propose a method for learning curve extrapolation to
determine if a training process can be terminated early. In contrast to
existing work, we propose to learn from learning curves of architectures
trained on other datasets to improve the prediction accuracy for novel
datasets. On five different image classification benchmarks, we empirically
demonstrate that both of our orthogonal contributions independently lead to an
acceleration, without any significant loss in accuracy.
