Summary of Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-generation Neural Networks, by Giorgio Morales et al.
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks
by Giorgio Morales, John Sheppard
First submitted to arxiv on: 13 Dec 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 This paper presents an adaptive sampling approach designed to reduce epistemic uncertainty in predictive models, which is crucial for making informed decisions in various scientific and engineering domains. The primary contribution is the development of a metric that estimates potential epistemic uncertainty leveraging prediction interval-generation neural networks. This metric relies on the distance between predicted upper and lower bounds and observed data at tested positions and their neighboring points. Additionally, the paper proposes a batch sampling strategy based on Gaussian processes (GPs), which uses a GP as a surrogate model of the networks trained at each iteration of the adaptive sampling process. The authors design an acquisition function that selects a combination of sampling locations to maximize the reduction of epistemic uncertainty across the domain. The results demonstrate that this approach consistently converges faster to minimum epistemic uncertainty levels compared to Normalizing Flows Ensembles, MC-Dropout, and simple GPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make better predictions by reducing uncertainty in model decisions. It’s like trying to figure out which fertilizer to use on a farm: you want to be sure the best choice is made. The authors came up with a new way to do this using special kinds of computer models and statistics. They tested it on some fake data and real farm data, and it worked better than other methods. This could be important for people who make decisions based on predictions, like farmers or scientists. |
Keywords
» Artificial intelligence » Dropout
