Neural Architecture Search: Automating the Design of Deep Networks

Abstract

Neural Architecture Search (NAS) is an emerging area of research that focuses on automating the design of deep neural networks (DNNs). With the increasing complexity and performance requirements of machine learning models, manually designing architectures is becoming infeasible. NAS offers a promising solution by automating this process, enabling the discovery of high-performance architectures tailored to specific tasks. This paper explores the fundamental concepts, methodologies, and applications of NAS, highlighting the state-of-the-art algorithms and challenges in the field. The paper also investigates the role of NAS in advancing AI technologies, its practical applications in various domains, and its future directions in terms of scalability, efficiency, and integration with other machine learning paradigms.