Summary of Interface Laplace Learning: Learnable Interface Term Helps Semi-supervised Learning, by Tangjun Wang et al.
Interface Laplace Learning: Learnable Interface Term Helps Semi-Supervised Learning
by Tangjun Wang, Chenglong Bao, Zuoqiang Shi
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 A novel framework for graph-based semi-supervised learning called Interface Laplace learning is introduced. This approach challenges the assumption that functions are smooth at all unlabeled points by incorporating an interface term. A practical algorithm is developed to approximate the interface positions using k-hop neighborhood indices and learn the interface term from labeled data without artificial design. The method is efficient and effective, achieving better performance than recent semi-supervised learning approaches on MNIST, FashionMNIST, and CIFAR-10 datasets at extremely low label rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to learn graphs with some information, called Interface Laplace learning. It’s like trying to find the tricky parts of a picture where different things meet. The old idea was that everything in the picture should be smooth, but this approach says no, there are special places where it gets rough. They developed an easy-to-use formula to find these tricky places and make the model learn from them. This new way works well on pictures like MNIST, FashionMNIST, and CIFAR-10. |
Keywords
» Artificial intelligence » Semi supervised
