Summary of A Geometry-aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space, by Zhangyu Wang et al.
A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space
by Zhangyu Wang, Lantian Xu, Zhifeng Kong, Weilong Wang, Xuyu Peng, Enyang Zheng
First submitted to arxiv on: 23 Jul 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 This paper proposes an innovative method for learning hyperbolic embeddings, which have shown promising results in tasks involving tree-like graph structures. However, previous approaches struggle when dealing with hierarchical data due to the mismatch between Euclidean and hyperbolic geometries. The authors identify three primary challenges hindering performance and develop a novel algorithm combining dilation operations and transitive closure regularization to address these issues. Experimental results on both synthetic and real-world datasets demonstrate superior performances of this geometry-aware approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to improve how computers learn from hierarchical data, which looks like a tree. Right now, the methods used for this task don’t work well because they’re based on a different kind of math than what’s needed. The authors figured out three main problems with these methods and created a new way to solve them. They tested their method on some fake data and real-world examples and found that it worked better. |
Keywords
* Artificial intelligence * Regularization
