Uncertainty quantification for trustworthy deep learning: Methods and measures

H. Martin Gillis, Thomas Trappenberg

The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty qu…