Summary of How Disentangled Are Your Classification Uncertainties?, by Ivo Pascal De Jong et al.
How disentangled are your classification uncertainties?
by Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-Toro
First submitted to arxiv on: 22 Aug 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 proposes a novel approach to evaluate the disentanglement of aleatoric (data-driven) and epistemic (model-driven) uncertainty in machine learning. By comparing two competing formulations, Information Theoretic and Gaussian Logits approaches, the authors show that current methods are not reliable for separating these types of uncertainty. The results highlight the need for improved uncertainty quantification techniques that can accurately predict the source of uncertainty in a prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make machine learning models more accurate by figuring out why they’re making mistakes. Right now, there are two kinds of mistakes: ones caused by the data being noisy or incomplete (aleatoric), and ones caused by the model itself not being perfect (epistemic). The authors want to know if we can separate these two types of mistakes, and which methods work best for doing so. They test two different approaches and find that current methods are not good enough at separating the mistakes. This means we need to come up with better ways to understand where our models are going wrong. |
Keywords
» Artificial intelligence » Logits » Machine learning
