architecture search is the process of automatically learning the neural model
or cell structure that best suits the given task. Recently, this approach has
shown promising performance improvements (on language modeling and image
classification) with reasonable training speed, using a w
研究使用神经结构搜索(NAS)算法通过对 loss landscape 以及 gradient information 的分析,发现其倾向于选择使用 wide and shallow cell structures 的架构,虽然这些架构具有快速收敛的优势,但不一定能获得比其他架构更好的泛化性能。因此,有必要修正现有的 NAS 算法。