Local Recalibration of Neural Networks for Improved Predictions and Uncertainty Quantification
The author proposes a novel method for local recalibration of neural networks to improve prediction accuracy and uncertainty quantification, addressing biases in specific regions. This approach enhances the probabilistic representation of data-generative processes.