Neural Architecture Search (NAS) is a field focused on automating the design of high-performance neural network architectures. It typically involves three main components: a search space defining possible operations and connections, a search algorithm to sample candidate architectures, and an evaluation strategy to assess their performance. Early NAS methods, like those by Zoph & Le and Baker et al., used sequential layer-wise operations, which were computationally intensive, requiring hundreds of GPUs for extended periods. More recent approaches, inspired by successful modular designs, employ cell-based representations to improve efficiency. AI
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RANK_REASON The item is a blog post summarizing academic research papers on Neural Architecture Search.
