Summary of Pseudo-non-linear Data Augmentation Via Energy Minimization, by Pingbang Hu et al.
Pseudo-Non-Linear Data Augmentation via Energy Minimization
by Pingbang Hu, Mahito Sugiyama
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel data augmentation method combines energy-based modeling and information geometry principles to provide interpretable transformations. Unlike deep neural networks, the approach replaces non-interpretable transformations with theoretically grounded ones, ensuring strong guarantees like energy minimization. The backward projection algorithm is a key component, reversing dimension reduction to generate new data. While achieving competitive performance with black-box generative models, this method offers greater transparency and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on a way to make computer programs better at understanding information by using special math concepts called energy-based modeling and information geometry. Instead of using complicated neural networks that are hard to understand, we want to create simpler methods that work well and can be explained easily. This new method is like a tool that takes in old data and changes it into new, interesting versions. It does this by reversing the way we shrink down big datasets, which makes sense because it’s like taking a picture of something and then zooming out to show the whole thing. This new way of doing things works just as well as the complicated methods but is much easier to understand. |
Keywords
» Artificial intelligence » Data augmentation
