Summary of Uncertainty Quantification Metrics For Deep Regression, by Simon Kristoffersson Lind et al.
Uncertainty Quantification Metrics for Deep Regression
by Simon Kristoffersson Lind, Ziliang Xiong, Per-Erik Forssén, Volker Krüger
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 A novel study proposes a framework for evaluating predictive uncertainty in deep neural networks deployed on robots or physical systems. The goal is to enable downstream modules to reason about action safety. Researchers investigate four metrics – Area Under Sparsification Error (AUSE), Calibration Error, Spearman’s Rank Correlation, and Negative Log-Likelihood (NLL) – using synthetic regression datasets. They examine how these metrics behave under different uncertainty types, stability regarding test set size, and reveal strengths and weaknesses. The results suggest that Calibration Error is the most stable and interpretable metric, while AUSE and NLL have their use cases. However, Spearman’s Rank Correlation is discouraged due to its limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to measure uncertainty in artificial intelligence models used on robots or other machines. It wants to help these machines make safer decisions by understanding when they might be wrong. Scientists looked at four ways to do this – AUSE, Calibration Error, Spearman’s Rank Correlation, and NLL. They tested these methods using fake data and found out which ones work best in different situations. The main finding is that one method, Calibration Error, is the most reliable and easy to understand. Another method, AUSE, also has its uses. But they don’t recommend using Spearman’s Rank Correlation because it doesn’t work well. |
Keywords
» Artificial intelligence » Log likelihood » Regression
