Calibration-Aware Bayesian Neural Networks for Reliable Machine Learning Predictions

This paper proposes an integrated framework, referred to as calibration-aware Bayesian neural networks (CA-BNNs), that applies both data-dependent and data-independent regularizers to optimize a variational distribution in Bayesian learning, in order to enhance the calibration of neural network predictions.