As we advance in the fast-growing era of Machine Learning, various new and more complex neural architectures are arising to tackle problem more efficiently. On the one hand their efficient usage requires advanced knowledge and expertise, which is most of the time difficult to find on the labor market. On the other hand, searching for an optimized neural architecture is a time-consuming task when it is performed manually using a trial and error approach. Hence, a method and a tool support is needed to assist users of neural architectures, leading to an eagerness in the field of Automatic Machine Learning (AutoML). When it comes to Deep Learning, an important part of AutoML is the Neural Architecture Search (NAS). In this paper, we propose a novel cell-based hierarchical search space, easy to comprehend and manipulate. The objectives of the proposed approach are to optimize the search-time and to be general enough to handle most of state of the art Convolutional Neural Networks (CNN) architectures.

翻译：随着机器学习领域进入快速发展期，各类新型复杂神经网络架构不断涌现以更高效地解决问题。然而，其高效应用需要具备高级知识与专业技能，这在劳动力市场中往往难以寻觅。另一方面，采用试错法手动搜索优化神经网络架构是一项耗时的任务。因此，亟需一种方法及配套工具来辅助神经网络架构使用者，这推动了自动机器学习领域的蓬勃发展。在深度学习中，神经网络架构搜索作为自动机器学习的重要组成部分，本研究提出了一种新型基于细胞的分层搜索空间，该空间易于理解与操作。该方法旨在优化搜索时间，并具备足够通用性以覆盖当前主流的卷积神经网络架构。