Predictive Uncertainty Quantification with Compound Density Networks

Understand

Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem.

• Bayesian neural networks are one of the most popular approaches to uncertainty quantification.
• On the other hand, it was recently shown that ensembles of NNs, which belong to the class of mixture models, can be used to quantify prediction uncertainty.
• In this paper, we build upon these two approaches.