Summary of Accelerating Convergence Of Stein Variational Gradient Descent Via Deep Unfolding, by Yuya Kawamura and Satoshi Takabe
Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
by Yuya Kawamura, Satoshi Takabe
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes novel trainable algorithms that combine Stein variational gradient descent (SVGD) with deep unfolding techniques, allowing for the learning of internal SVGD parameters and accelerating its convergence speed. This approach is demonstrated through numerical simulations of three tasks: sampling a one-dimensional Gaussian mixture, performing Bayesian logistic regression, and learning Bayesian neural networks. The results show that the proposed algorithms outperform conventional SVGD variants in terms of convergence speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new ways to make Stein variational gradient descent (SVGD) work better. SVGD is a method for sampling from a target distribution, and it’s useful for machine learning tasks like Bayesian inference. The researchers combined SVGD with deep unfolding techniques to help the algorithm learn and get faster. They tested this idea on three different tasks and found that their new approach works better than the old one. |
Keywords
* Artificial intelligence * Bayesian inference * Gradient descent * Logistic regression * Machine learning
