The content introduces the problem of Neural Architecture Retrieval (NAR) to efficiently find similar neural architectures. It proposes a new framework for graph representation learning that considers motifs in neural architectures. Extensive evaluations show the superiority of the proposed algorithm over baselines, leading to the creation of a dataset with 12k real-world network architectures.
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