Summary of Posterior Sampling with Denoising Oracles Via Tilted Transport, by Joan Bruna and Jiequn Han
Posterior Sampling with Denoising Oracles via Tilted Transport
by Joan Bruna, Jiequn Han
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR); Computation (stat.CO); Machine Learning (stat.ML)
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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 introduces score-based diffusion models that generate high-dimensional data by learning a denoising oracle from datasets. These models offer a Bayesian perspective on data generation and facilitate solving inverse problems through posterior sampling. The authors propose new methods for this task, providing quantitative guarantees needed in scientific applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create realistic images or data sets from scratch. That’s what score-based diffusion models can do! They learn how to fix noisy or low-quality data by looking at examples of good data. This is useful in many fields where we need to generate or correct data, such as medical imaging or climate modeling. |
Keywords
* Artificial intelligence * Diffusion
