Summary of A Structured Review Of Literature on Uncertainty in Machine Learning & Deep Learning, by Fahimeh Fakour et al.
A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning
by Fahimeh Fakour, Ali Mosleh, Ramin Ramezani
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper explores the crucial concern of understanding and quantifying uncertainty in Machine Learning (ML) applications, particularly in risk-sensitive domains. The authors provide a structured review of the literature on uncertainty, categorizing it into aleatoric and epistemic types, discussing sources such as data and model uncertainty, and assessing quantification techniques like Ensembles and Bayesian Neural Networks. They also cover metrics for single-sample and dataset-level uncertainty quantification, calibration, and decision-making under uncertainty. The review focuses on Deep Learning (DL) but provides a broader context for ML in general. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make sure machine learning is working correctly and making good decisions. It talks about the different types of uncertainty, like when there’s randomness involved or when we just don’t know something. The authors discuss where this uncertainty comes from, such as with the data used to train the model or with the model itself. They also cover ways to measure how uncertain our predictions are and how to make good decisions based on that uncertainty. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
