Designing and optimizing neural network architectures typically requires
extensive expertise, starting with handcrafted designs and then manual or
automated refinement. This dependency presents a significant barrier to rapid
innovation. Recognizing the complexity of automatically generating neural
network architecture from scratch, we introduce Younger, a pioneering dataset
to advance this ambitious goal. Derived from over 174K real-world models across
more than 30 tasks from various public model hubs, Younger includes 7,629
unique architectures, and each is represented as a directed acyclic graph with
detailed operator-level information. The dataset facilitates two primary design
paradigms: global, for creating complete architectures from scratch, and local,
for detailed architecture component refinement. By establishing these
capabilities, Younger contributes to a new frontier, Artificial
Intelligence-Generated Neural Network Architecture (AIGNNA). Our experiments
explore the potential and effectiveness of Younger for automated architecture
generation and, as a secondary benefit, demonstrate that Younger can serve as a
benchmark dataset, advancing the development of graph neural networks. We
release the dataset and code publicly to lower the entry barriers and encourage
further research in this challenging area.

从超过 174K 个真实世界模型中获取的 Younger 数据集，其中包括 7,629 个唯一的网络结构，通过提供全局和局部的设计模式，推动了人工智能生成神经网络架构的新领域发展，并作为基准数据集进一步发展图神经网络的研究。