Neural Architecture Search for Model Design: Exploring neural architecture search methods for automatically designing optimal architectures for machine learning models

Abstract

Neural Architecture Search (NAS) has emerged as a powerful technique for automatically designing optimal architectures for machine learning models. This paper provides a comprehensive overview of NAS methods, discussing their principles, advantages, and challenges. We survey the landscape of NAS algorithms, including reinforcement learning-based, evolutionary-based, and gradient-based approaches. We also examine recent advancements in NAS, such as efficient neural architecture representations and search space constraints. Additionally, we discuss the application of NAS in various domains, highlighting its impact on model performance and efficiency. Finally, we present open challenges and future directions for research in NAS, aiming to inspire further advancements in this exciting field.