  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.
Our implementation has been made publicly available to facilitate further
research on efficient architecture search algorithms.
