Summary of Accelerating Ensemble Error Bar Prediction with Single Models Fits, by Vidit Agrawal et al.
Accelerating Ensemble Error Bar Prediction with Single Models Fits
by Vidit Agrawal, Shixin Zhang, Lane E. Schultz, Dane Morgan
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci)
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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 research explores a novel approach to estimating prediction uncertainties in machine learning models without the need for an ensemble of models. Instead, a single model is fit to predicted ensemble error bar data, allowing for efficient uncertainty quantification while maintaining predictive accuracy. The method combines three models: Model A for predictive accuracy, Model AE for traditional ensemble-based error bar prediction, and Model B, trained on augmented synthetic data to estimate error bars efficiently. This approach offers a highly flexible method that can approximate the performance of ensemble methods with only a single extra model evaluation during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict something, like how strong a material will be. To get a good idea of how accurate your prediction is, you need to know how much it could vary based on different factors. One way to do this is by using many models and combining their predictions. But that can be really slow! This research shows that you can use just one model and still get a good estimate of the uncertainty. It works by training a new model on data from another model, and then using that model to predict how uncertain your original prediction is. This approach can make it much faster and more efficient to get an idea of how accurate your predictions are. |
Keywords
» Artificial intelligence » Inference » Machine learning » Synthetic data
