Neural Architecture Search - Recent Trends and Challenges: Analyzing recent trends and challenges in neural architecture search (NAS) for automating the design of deep learning architectures

Abstract

Neural Architecture Search (NAS) has emerged as a promising approach for automating the design of deep learning architectures. This paper provides a comprehensive analysis of recent trends and challenges in NAS. We discuss various NAS methods, including reinforcement learning-based approaches, evolutionary algorithms, and gradient-based methods. We also highlight the importance of benchmarking and evaluation metrics in NAS research. Furthermore, we address the challenges faced by NAS, such as scalability, sample efficiency, and the need for better exploration-exploitation strategies. This paper aims to provide researchers and practitioners with insights into the current state of NAS and potential future directions.