Summary of Pathwise Gradient Variance Reduction with Control Variates in Variational Inference, by Kenyon Ng et al.
Pathwise Gradient Variance Reduction with Control Variates in Variational Inference
by Kenyon Ng, Susan Wei
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
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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 explores methods for improving the estimation of gradients in Bayesian deep learning when a closed-form solution is not available. The focus is on pathwise and score-function gradient estimators, which are commonly used due to their lower variance. However, recent research suggests that even pathwise estimators can benefit from variance reduction techniques. To address this limitation, the paper proposes using zero-variance control variates with pathwise gradient estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers looked at ways to reduce the noise in a type of mathematical calculation used in artificial intelligence and machine learning. They’re talking about something called “variational inference” which is important for things like self-driving cars and medical diagnosis. The problem they’re trying to solve is that sometimes these calculations get really messy and hard to do accurately. To make it better, they want to find ways to reduce the noise in these calculations so they can be more accurate. |
Keywords
» Artificial intelligence » Deep learning » Inference » Machine learning
