Summary of Are Uncertainty Quantification Capabilities Of Evidential Deep Learning a Mirage?, by Maohao Shen et al.
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
by Maohao Shen, J. Jon Ryu, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory W. Wornell
First submitted to arxiv on: 9 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 In this paper, researchers question the effectiveness of a popular predictive uncertainty quantification approach called evidential deep learning (EDL). Despite its strong empirical performance, recent studies have identified limitations in EDL’s learned epistemic uncertainties. This study provides a deeper understanding of EDL methods by unifying various objective functions and reveals that they can be better interpreted as out-of-distribution detection algorithms based on energy-based models. The authors also conduct extensive ablation studies to assess the empirical effectiveness of EDL methods with real-world datasets. Overall, this research suggests that incorporating model uncertainty can help EDL methods accurately quantify uncertainties and improve performance on downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a special way computers learn called evidential deep learning (EDL). It’s like trying to guess how good or bad something will be, but sometimes it’s not very accurate. Scientists want to know if this method is really working well or just seeming that way because of some issues with the way they’re doing it. They found out that even when EDL seems to work great on some tasks, it’s actually not very good at guessing how sure it is about its answers. But if you use another way called model uncertainty, you can make EDL better and more accurate. |
Keywords
* Artificial intelligence * Deep learning
