Title: Neural Architecture Search: A Survey,
Abstract: Deep Learning has enabled remarkable progress over the last years on a
variety of tasks, such as image recognition, speech recognition, and machine
translation. One crucial aspect for this progress are novel neural
architectures. Currently employed architectures have mostly been developed
manually by human experts, which is a time-consuming and error-prone process.
Because of this, there is growing interest in automated neural architecture
search methods. We provide an overview of existing work in this field of
research and categorize them according to three dimensions: search space,
search strategy, and performance estimation strategy.