Summary of Why Are Hyperbolic Neural Networks Effective? a Study on Hierarchical Representation Capability, by Shicheng Tan et al.
Why are hyperbolic neural networks effective? A study on hierarchical representation capability
by Shicheng Tan, Huanjing Zhao, Shu Zhao, Yanping Zhang
First submitted to arxiv on: 4 Feb 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 challenges the widespread application of Hyperbolic Neural Networks (HNNs) in recent years. Despite their popularity, there is no empirical evidence to support the idea that HNNs can achieve the theoretical optimal embedding in hyperbolic space, which is crucial for preserving data hierarchical relationships. To address this gap, the authors propose a benchmark for evaluating Hierarchical Representation Capability (HRC) and conduct large-scale experiments to analyze why HNNs are effective. The results reveal that optimization objectives and hierarchical structures significantly impact HRC, and that pre-training strategies can enhance HRC and improve downstream task performance. Notably, the experiments show that HNNs cannot achieve the theoretical optimal embedding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper looks at a type of artificial intelligence called Hyperbolic Neural Networks (HNNs). For years, people have been using HNNs because they can help preserve important relationships in data. But some researchers questioned whether this is really true. To find out, the authors created a way to test how well HNNs do this and ran lots of experiments. They found that the quality of these relationships depends on how the AI is trained and what kind of data it looks at. The results showed that HNNs don’t actually live up to their promises. Instead, they can still be useful if we train them correctly. |
Keywords
* Artificial intelligence * Embedding * Optimization
