Summary of Quantifying Calibration Error in Modern Neural Networks Through Evidence Based Theory, by Koffi Ismael Ouattara
Quantifying calibration error in modern neural networks through evidence based theory
by Koffi Ismael Ouattara
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Logic (math.LO)
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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 paper proposes a novel framework for evaluating the trustworthiness of neural networks by incorporating subjective logic into the calculation of Expected Calibration Error (ECE). This approach provides a comprehensive measure of trust, disbelief, and uncertainty by clustering predicted probabilities and fusing opinions. The method is demonstrated on MNIST and CIFAR-10 datasets, showing improved trustworthiness after calibration. The framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make neural networks better at telling when they’re wrong. Right now, we just measure how well they do, but that doesn’t tell us much about how confident or uncertain they are. The researchers came up with a new way to look at this by using “subjective logic” and “Expected Calibration Error”. They tested it on some pictures of numbers and objects and found that it worked pretty well. |
Keywords
» Artificial intelligence » Clustering
