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Now, neural architecture search (NAS) studies are exploring the\njoint optimization of wiring and operation types, however, the space of\npossible wirings is constrained and still driven by manual design despite being\nsearched. In this paper, we explore a more diverse set of connectivity patterns\nthrough the lens of randomly wired neural networks. To do this, we first define\nthe concept of a stochastic network generator that encapsulates the entire\nnetwork generation process. Encapsulation provides a unified view of NAS and\nrandomly wired networks. Then, we use three classical random graph models to\ngenerate randomly wired graphs for networks. The results are surprising:\nseveral variants of these random generators yield network instances that have\ncompetitive accuracy on the ImageNet benchmark. 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