Paper

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems. Traditional approaches, such as dropout and ensemble methods, estimate uncertainty by sampling probability predictions from different submodels, which leads to slow uncertainty estimation at inference time... (read more)

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