Summary of Score Change Of Variables, by Stephen Robbins
Score Change of Variables
by Stephen Robbins
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Probability (math.PR)
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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 proposed paper derives a general change of variables formula for score functions, showing that transformed score functions can be expressed in terms of original log-probabilities. This result is used to develop two applications: reverse-time Itô lemma for score-based diffusion models and generalized sliced score matching, which enables decoupling forward and reverse processes in density estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to use a smooth transformation to express the score function of one probability distribution in terms of another. This allows for more flexible density estimation and can be used with different types of transformations. The authors also apply their method to a specific problem involving diffusion on a probability simplex, and compare it to traditional methods. |
Keywords
» Artificial intelligence » Density estimation » Diffusion » Probability
