  Neural networks are powerful models that have a remarkable ability to extract
patterns that are too complex to be noticed by humans or other machine learning
models. Neural networks are the first class of models that can train end-to-end
systems with large learning capacities. However, we still have the difficult
challenge of designing the neural network, which requires human experience and
a long process of trial and error. As a solution, we can use a neural
architecture search to find the best network architecture for the task at hand.
Existing NAS algorithms generally evaluate the fitness of a new architecture by
fully training from scratch, resulting in the prohibitive computational cost,
even if operated on high-performance computers. In this paper, an end-to-end
offline performance predictor is proposed to accelerate the evaluation of
sampled architectures.
  Index Terms- Learning Curve Prediction, Neural Architecture Search,
Reinforcement Learning.
