Leveraging data-dependent gating properties of the GLN we are able to estimate prediction uncertainty with effectively zero algorithmic overhead.

This important phenomenon commonly appears in implemented neural network architectures, and also seems to emerge in epoch-wise curves during the training process.

Unfortunately,optimizing anetworkusingthe desired evaluation metric, such asF1-Score, istypically not feasible.