Summary of Improved Depth Estimation Of Bayesian Neural Networks, by Bart Van Erp and Bert De Vries
Improved Depth Estimation of Bayesian Neural Networks
by Bart van Erp, Bert de Vries
First submitted to arxiv on: 14 Oct 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 proposed method improves upon earlier work for estimating the depth of Bayesian neural networks. By introducing a discrete truncated normal distribution over network depth, the approach independently learns the mean and variance of this distribution. The variational free energy is minimized to infer posterior distributions, balancing model complexity and accuracy. As a result, the method demonstrates improved test accuracy on the spiral dataset and reduced variance in posterior depth estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make better predictions about how deep neural networks should be for specific tasks. To do this, it uses a new way of thinking about network depth that lets it learn both the average and amount of uncertainty around this depth. This approach is tested on some sample data and shows improvements in accuracy and reduced uncertainty. |
