A Novel Regression Loss for Non-Parametric Uncertainty Optimization

Understand

Quantification of uncertainty is one of the most promising approaches to establish safe machine learning.

• Despite its importance, it is far from being generally solved, especially for neural networks.
• One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.
• However, it can underestimate the uncertainty.