This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.

翻译：本文通过以不同的方式制定任务来解决建筑搜索的可扩展性挑战。 与在离散和无差异的搜索空间上应用进化或强化学习的传统方法不同,我们的方法以不断放松建筑代表制为基础,从而能够利用梯度下降有效搜索建筑。 关于CIFAR-10、图像网、Penn Treebank和WikitText-2的广泛实验显示,我们的算法在发现高性能的图像分类革命性架构和语言建模的经常性架构方面优异,同时比最先进的非差异性技术要快得多。