Summary of Enhance Hyperbolic Representation Learning Via Second-order Pooling, by Kun Song et al.
Enhance Hyperbolic Representation Learning via Second-order Pooling
by Kun Song, Ruben Solozabal, Li hao, Lu Ren, Moloud Abdar, Qing Li, Fakhri Karray, Martin Takac
First submitted to arxiv on: 29 Oct 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 The paper introduces second-order pooling into hyperbolic representation learning to address the issue of increased Lipschitz constant caused by the hyperbolic discriminant objective. This is achieved by naturally increasing the distance between samples without compromising the generalization ability of the input features. The proposed method utilizes kernel approximation regularization to enable low-dimensional bilinear features to approximate the kernel function well in low-dimensional space. Experimental results on graph-structured datasets demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new way to represent data that helps computers learn better from hierarchical information. This can be useful for tasks like image recognition and natural language processing. The problem is that this representation makes it harder for models to generalize well, so the researchers propose a new technique called second-order pooling to solve this issue. They also develop a way to make low-dimensional bilinear features work well with this new representation. |
Keywords
» Artificial intelligence » Generalization » Natural language processing » Regularization » Representation learning
