  A probabilistic classifier with reliable predictive uncertainties i) fits
successfully to the target domain data, ii) provides calibrated class
probabilities in difficult regions of the target domain (e.g.\ class overlap),
and iii) accurately identifies queries coming out of the target domain and
rejects them. We introduce an original combination of Evidential Deep Learning,
Neural Processes, and Neural Turing Machines capable of providing all three
essential properties mentioned above for total uncertainty quantification. We
observe our method on five classification tasks to be the only one that can
excel all three aspects of total calibration with a single standalone
predictor. Our unified solution delivers an implementation-friendly and compute
efficient recipe for safety clearance and provides intellectual economy to an
investigation of algorithmic roots of epistemic awareness in deep neural nets.
