Summary of Younger: the First Dataset For Artificial Intelligence-generated Neural Network Architecture, by Zhengxin Yang et al.
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
by Zhengxin Yang, Wanling Gao, Luzhou Peng, Yunyou Huang, Fei Tang, Jianfeng Zhan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: This paper introduces Younger, a pioneering dataset aimed at advancing the goal of automatically generating neural network architectures from scratch. The dataset is derived from over 174K real-world models across various public model hubs, featuring 7,629 unique architectures represented as directed acyclic graphs with detailed operator-level information. Younger enables two primary design paradigms: global for creating complete architectures and local for refining architecture components. The paper explores the potential and effectiveness of Younger for automated architecture generation and demonstrates its use as a benchmark dataset for graph neural networks. By releasing the dataset and code publicly, this research aims to lower barriers and encourage further investigation in this challenging area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This study creates a special dataset called Younger that helps with designing new artificial intelligence models called neural networks. Currently, making these models requires a lot of expertise and manual work. The researchers want to make it easier by creating a way for computers to automatically generate model designs. They made this dataset from over 174K real-world AI models and added details about each one. This will help other scientists and engineers develop better AI models and make progress in the field. |
Keywords
» Artificial intelligence » Neural network
