Neural Architecture Search Space

Neural architecture search (NAS) aims to automate the design of optimal neural network architectures, eliminating the need for manual design and potentially leading to more efficient and effective models. Current research focuses on developing more efficient search algorithms, including those employing evolutionary strategies, Bayesian optimization, and differentiable architectures, often within hierarchical or progressively reduced search spaces. These advancements address the significant computational cost of NAS, enabling the exploration of larger and more complex architectures while considering factors like resource constraints and fault tolerance for diverse applications. The ultimate goal is to create automated systems capable of designing high-performing neural networks tailored to specific tasks and hardware limitations.