Summary of Enabling Uncertainty Estimation in Iterative Neural Networks, by Nikita Durasov et al.
Enabling Uncertainty Estimation in Iterative Neural Networks
by Nikita Durasov, Doruk Oner, Jonathan Donier, Hieu Le, Pascal Fua
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper proposes a novel approach to uncertainty estimation by leveraging the convergence rate of iterative neural network models. The authors show that the rate at which these models converge is highly correlated with their accuracy, allowing them to use this metric as a proxy for uncertainty. This method provides state-of-the-art estimates at a significantly lower computational cost than existing techniques like Ensembles, without requiring any modifications to the original model. The proposed approach is demonstrated in two application domains: road detection in aerial images and estimation of aerodynamic properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make neural networks more useful by measuring how well they’re doing while they’re working. It shows that if a network is good at what it’s trying to do, its output will get better and better as it works through the problem. This means we can use how fast the network gets better as a measure of how sure it is about its answer. This new way of measuring uncertainty is really good and uses less computer power than other methods that do the same thing. The authors show this method works well in two real-world applications: finding roads on aerial photos and calculating the properties of shapes. |
Keywords
» Artificial intelligence » Neural network
