  We focus on using the predictive uncertainty signal calculated by Bayesian
neural networks to guide learning in the self-same task the model is being
trained on. Not opting for costly Monte Carlo sampling of weights, we propagate
the approximate hidden variance in an end-to-end manner, throughout a
variational Bayesian adaptation of a ResNet with attention and
squeeze-and-excitation blocks, in order to identify data samples that should
contribute less into the loss value calculation. We, thus, propose
uncertainty-aware, data-specific label smoothing, where the smoothing
probability is dependent on this epistemic uncertainty. We show that, through
the explicit usage of the epistemic uncertainty in the loss calculation, the
variational model is led to improved predictive and calibration performance.
This core machine learning methodology is exemplified at wildlife call
detection, from audio recordings made via passive acoustic monitoring equipment
in the animals' natural habitats, with the future goal of automating large
scale annotation in a trustworthy manner.
