论文标题
卷积网络的神经体系结构介绍
An Introduction to Neural Architecture Search for Convolutional Networks
论文作者
论文摘要
神经体系结构搜索（NAS）是一个研究领域，涉及利用优化算法设计最佳的神经网络体系结构。关于建筑搜索空间，优化算法以及候选体系结构评估方法，有许多方法。随着该领域的增长不断增长，初学者很难辨别主要的主要内容，以及该领域随之而来的新兴方向。在这项工作中，我们为卷积网络的NAS基本概念提供了介绍，以及搜索空间，算法和评估技术的主要进步。
Neural Architecture Search (NAS) is a research field concerned with utilizing optimization algorithms to design optimal neural network architectures. There are many approaches concerning the architectural search spaces, optimization algorithms, as well as candidate architecture evaluation methods. As the field is growing at a continuously increasing pace, it is difficult for a beginner to discern between major, as well as emerging directions the field has followed. In this work, we provide an introduction to the basic concepts of NAS for convolutional networks, along with the major advances in search spaces, algorithms and evaluation techniques.
