Summary of Topology-aware Embedding Memory For Continual Learning on Expanding Networks, by Xikun Zhang et al.
Topology-aware Embedding Memory for Continual Learning on Expanding Networks
by Xikun Zhang, Dongjin Song, Yixin Chen, Dacheng Tao
First submitted to arxiv on: 24 Jan 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 This paper explores the challenges of continual learning on expanding networks, where traditional techniques can lead to a “memory explosion” problem. To address this issue, the authors propose a novel framework called Parameter Decoupled Graph Neural Networks (PDGNNs) with Topology-aware Embedding Memory (TEM). This framework reduces memory space complexity from O(nd^L) to O(n) and fully utilizes topological information for memory replay. The authors also discover a “pseudo-training effect” in continual learning on expanding networks, which motivates the development of a novel coverage maximization sampling strategy. Empirical studies demonstrate that PDGNNs with TEM outperform state-of-the-art techniques, especially in class-incremental settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to keep learning new things without running out of memory. Right now, our brains can’t learn new information as fast as we get older because they don’t have enough space. The authors came up with a way to make computers learn new things faster by using less memory and still getting good results. They also found that sometimes, even if we’re not actively learning something, our brains are still processing the information in some way. This helps us understand how to make computers work better when they have limited space. |
Keywords
* Artificial intelligence * Continual learning * Embedding
