Summary of On Uniform, Bayesian, and Pac-bayesian Deep Ensembles, by Nick Hauptvogel et al.
On Uniform, Bayesian, and PAC-Bayesian Deep Ensembles
by Nick Hauptvogel, Christian Igel
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper challenges the common practice of combining deep neural networks into Bayesian ensembles, arguing that neither sampling nor weighting models are well-suited for increasing generalization performance. Instead, the authors suggest a weighted average of models, where weights are optimized by minimizing a second-order PAC-Bayesian generalization bound, taking correlations between models into account. This approach, dubbed the tandem loss, requires hold-out data to estimate error correlations and can increase robustness against correlated models or those with lower performance in an ensemble. The authors demonstrate that this method outperforms state-of-the-art Bayesian ensembles in terms of classification accuracy and provides non-vacuous generalization guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that combining deep neural networks into ensembles isn’t always the best approach. Instead, it suggests a new way to combine models by weighing them according to how well they perform. This method is better than just averaging the models together and can even improve their performance. The authors tested this approach on some datasets and found that it works really well. |
Keywords
» Artificial intelligence » Classification » Generalization
