Summary of On the Convergence Of Locally Adaptive and Scalable Diffusion-based Sampling Methods For Deep Bayesian Neural Network Posteriors, by Tim Rensmeyer and Oliver Niggemann
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
by Tim Rensmeyer, Oliver Niggemann
First submitted to arxiv on: 13 Mar 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 Achieving robust uncertainty quantification for deep neural networks is crucial in many real-world applications, such as medical imaging, where reliable predictions are essential. Bayesian neural networks offer a promising approach to modeling uncertainties. However, generating samples from the posterior distribution of neural networks remains a significant challenge. One key advancement would be incorporating adaptive step sizes into Monte Carlo Markov chain sampling algorithms without increasing computational demand. Previous papers have introduced sampling algorithms claiming this property, but do they indeed converge to the correct distribution? Our paper demonstrates that these methods can exhibit substantial bias in the sampled distribution, even with vanishing step sizes and full batch size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is important because it helps computers make more reliable predictions. For example, in medical imaging, we need computers to accurately diagnose diseases. To do this, we need to be able to measure how sure the computer is of its answer. Bayesian neural networks are a way to do this, but there’s a big challenge: making sure our samples from these networks are correct. One idea is to use adaptive step sizes in sampling algorithms, which would help us get more accurate results without using too much computer power. However, some previous papers have shown that their methods can be biased and not give the right answer, even when we’re trying to make them better. |
