Summary of Hycube: Efficient Knowledge Hypergraph 3d Circular Convolutional Embedding, by Zhao Li et al.
HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
by Zhao Li, Xin Wang, Jun Zhao, Wenbin Guo, Jianxin Li
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposes an end-to-end efficient knowledge hypergraph embedding model called HyCubE, which achieves a better trade-off between effectiveness and efficiency by adaptively adjusting its structure to handle n-ary knowledge tuples. The model uses a novel 3D circular convolutional neural network and the alternate mask stack strategy to extract feature information comprehensively. Additionally, it employs a knowledge hypergraph 1-N multilinear scoring way to accelerate training efficiency. Compared to state-of-the-art baselines, HyCubE outperforms them by an average of 8.22% and up to 33.82%, while being 6.12x faster, using 52.67% less GPU memory, and having 85.21% fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a special kind of computer model called HyCubE that helps us understand complex information better. The model is very efficient and can learn new things quickly. It does this by using a unique way to look at data and adjusting its own structure as it learns. This means it can be used with big datasets and still work well. The results show that HyCubE is much better than other models, making it an important tool for anyone who works with complex information. |
Keywords
» Artificial intelligence » Embedding » Mask » Neural network
