Uncertainty quantification for trustworthy deep learning: Methods and measures
The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification. This survey provides a structured, critical review of methods for Uncertainty Quantification (UQ) in deep learning, scoped to ensemble-based and approximate Bayesian approaches and the measures used to summarize their outputs. Relative to existing UQ surveys, our contribution is depth on efficient ensemble approximations and single-pass methods, and a unified treatment that separates the method producing a predictive distribution from the measure that summarizes its uncertainty. We organize methods...
| Authors | H. Martin Gillis, Thomas Trappenberg |
|---|---|
| Primary category | stat.ML |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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