Summary of Diffusive Gibbs Sampling, by Wenlin Chen et al.
Diffusive Gibbs Sampling
by Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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 This paper addresses the challenge of effectively sampling from multi-modal distributions using conventional Markov Chain Monte Carlo (MCMC) methods. The authors propose Diffusive Gibbs Sampling (DiGS), a family of innovative sampling methods that integrate recent developments in diffusion models to create an auxiliary noisy distribution bridging isolated modes. DiGS combines Gaussian convolution with Gibbs sampling, applying a novel Metropolis-within-Gibbs scheme to enhance mixing in the denoising step. The authors demonstrate improved performance across various tasks, including mixtures of Gaussians and Bayesian neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in science: how to efficiently sample from very complex data sets with many different patterns or modes. Current methods can get stuck in one mode and not explore the whole space. To fix this, the authors created a new way called Diffusive Gibbs Sampling (DiGS). It works by creating a fake version of the data that helps the sampling process jump between modes. The results show that DiGS is much better than current methods at finding good samples from these complex distributions. |
Keywords
* Artificial intelligence * Multi modal
