Summary of Pre-training Identification Of Graph Winning Tickets in Adaptive Spatial-temporal Graph Neural Networks, by Wenying Duan et al.
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
by Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The novel method presented in this paper enhances the computational efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing the Graph Winning Ticket (GWT), inspired by the Lottery Ticket Hypothesis (LTH). The GWT is a pre-determined star topology that balances edge reduction with efficient information propagation, reducing computational demands while maintaining high model performance. This approach streamlines ASTGNN deployment by eliminating exhaustive training, pruning, and retraining cycles, achieving comparable performance to full models at substantially lower costs. The paper also explores the effectiveness of GWT from a spectral graph theory perspective, providing theoretical support for the existence of efficient sub-networks within ASTGNNs. The authors demonstrate their approach on various datasets, achieving state-of-the-art performance and overcoming out-of-memory issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make computer models faster and more efficient. These models, called Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs), are used for tasks like analyzing data from sensors or cameras. The authors found that by using a special kind of “ticket” (called the Graph Winning Ticket, or GWT) during training, they could make these models work faster and with less memory needed. This is important because it means we can use these models on devices with limited resources, like smartphones or smart home devices. The paper also explains why this approach works from a technical standpoint. |
Keywords
» Artificial intelligence » Pruning
