Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
arxiv(2024)
摘要
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.
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