Neighborhood Smoothing for Calibration
The paper introduces graph smoothing as a method for training neural networks to be better calibrated during training. It proposes ΝmodelNoSpace, a regularizer that penalizes differences between neighboring samples' predictions in the learned representation space. Empirical results show this approach improves predictive quality across various benchmarks and complements post-hoc calibration techniques.