Summary of Estimating Uncertainty with Implicit Quantile Network, by Yi Hung Lim
Estimating Uncertainty with Implicit Quantile Network
by Yi Hung Lim
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 an alternative approach to uncertainty quantification in performance-critical applications, leveraging Implicit Quantile Networks to directly model the loss distribution and estimate prediction uncertainty. This method is demonstrated on MNIST and CIFAR datasets, showcasing a mean estimated loss of 2x higher for incorrect predictions. By removing high-uncertainty data from the test set, model accuracy increases by up to 10%. This simple-to-implement approach offers valuable insights for applications where accurate predictions are crucial, such as deep learning in healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by figuring out when our models might be wrong. It uses a new way of thinking about uncertainty, called an Implicit Quantile Network, to calculate how sure or unsure a model is about its guesses. The results show that this method can help us get rid of data that the model isn’t very confident about, which makes it better at making accurate predictions. This could be really important for things like using deep learning to make medical diagnoses. |
Keywords
» Artificial intelligence » Deep learning
