The paper provides a comprehensive overview of Neural Architecture Search
(NAS), emphasizing its evolution from manual design to automated,
computationally-driven approaches. It covers the inception and growth of NAS,
highlighting its application across various domains, including medical imaging
and natural language processing. The document details the shift from
expert-driven design to algorithm-driven processes, exploring initial
methodologies like reinforcement learning and evolutionary algorithms. It also
discusses the challenges of computational demands and the emergence of
efficient NAS methodologies, such as Differentiable Architecture Search and
hardware-aware NAS. The paper further elaborates on NAS's application in
computer vision, NLP, and beyond, demonstrating its versatility and potential
for optimizing neural network architectures across different tasks. Future
directions and challenges, including computational efficiency and the
integration with emerging AI domains, are addressed, showcasing NAS's dynamic
nature and its continued evolution towards more sophisticated and efficient
architecture search methods.

神经架构搜索（NAS）从手动设计到自动化，提供了全面的概述，强调其在不同领域中的应用，包括医学成像和自然语言处理。该文档详细介绍了从专家驱动设计到算法驱动过程的转变，探索了初步的方法，例如强化学习和进化算法。还讨论了计算需求和高效 NAS 方法的挑战，如可微分架构搜索和硬件感知 NAS。本文进一步阐述了 NAS 在计算机视觉、自然语言处理等领域的应用，展示了其在不同任务中优化神经网络架构的多样性和潜力。未来的方向和挑战包括计算效率和与新兴 AI 领域的整合，展示了 NAS 的动态特性和其朝着更复杂、高效的架构搜索方法的持续演化。