Progressive Neural Architecture Search

Progressive Neural Architecture Search (PNAS) automates the design of efficient and accurate neural networks by iteratively building upon simpler architectures. Current research focuses on improving the efficiency of PNAS algorithms, such as through differentiable search methods (like DARTS and its variants) and multi-objective optimization techniques (incorporating factors beyond accuracy, like latency and resource consumption). These advancements are significant because they enable the creation of optimized neural networks for resource-constrained environments (e.g., edge devices) and diverse applications, ranging from autonomous driving to scientific data analysis.