Summary of Learning Structured Representations with Hyperbolic Embeddings, by Aditya Sinha et al.
Learning Structured Representations with Hyperbolic Embeddings
by Aditya Sinha, Siqi Zeng, Makoto Yamada, Han Zhao
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 a novel approach, HypStructure, to learn hierarchy-informed features from real-world datasets with hierarchical label structures. The method, which combines a hyperbolic tree-based representation loss and a centering loss, can be used with any standard task loss to accurately embed the label hierarchy into learned representations. The authors demonstrate the efficacy of HypStructure on several large-scale vision benchmarks, showing reduced distortion and improved generalization performance, especially in low-dimensional scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HypStructure is a new way to learn features from data that has a natural order or hierarchy. This is important because many real-world datasets have this kind of structure, but most existing methods ignore it. The approach uses a special type of space called hyperbolic space to model these hierarchical relationships and regularizes the learned representations to preserve this structure. The results show that HypStructure can improve performance on certain tasks, especially when working with low-dimensional data. |
Keywords
» Artificial intelligence » Generalization
