Summary of Scalable Diffusion Posterior Sampling in Infinite-dimensional Inverse Problems, by Fabian Schneider and Duc-lam Duong and Matti Lassas and Maarten V. De Hoop and Tapio Helin
Scalable diffusion posterior sampling in infinite-dimensional inverse problems
by Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop, Tapio Helin
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Analysis of PDEs (math.AP); Numerical Analysis (math.NA); Probability (math.PR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Scalable Diffusion Posterior Sampling (SDPS) method addresses the computational costs of Bayesian inverse problems by shifting effort from sampling to offline training. It learns a task-dependent score based on the forward mapping, allowing for efficient sampling without additional conditional score approximations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new approach uses score-based diffusion models to sample from posterior distributions in Bayesian inverse problems. By learning a task-dependent score and deriving an affine transformation of the conditional posterior score, SDPS reduces computational costs while maintaining accuracy. The method is validated through rigorous analysis and high-dimensional imaging experiments, showing its potential for large-scale applications. |
Keywords
» Artificial intelligence » Diffusion
