Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
<|reference_start|>Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture: 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.<|reference_end|>
@article{yang2024younger:,
    title={Younger: The First Dataset for Artificial Intelligence-Generated Neural
  Network Architecture},
    author={Zhengxin Yang, Wanling Gao, Luzhou Peng, Yunyou Huang, Fei Tang,
  Jianfeng Zhan},
    journal={arXiv preprint arXiv:2406.15132},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15132},
    primaryClass={cs.LG cs.AI}
}