Prediction Rigidity Framework for Uncertainty Estimation in Trained Neural Networks

The authors propose a prediction rigidity framework to estimate uncertainties in trained neural networks, connecting it to Bayesian inference and introducing a last-layer approximation method for efficient uncertainty quantification.