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 reject 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 three image classification benchmarks and two neural net architectures to consistently give competitive or superior scores with respect to multiple uncertainty quantification metrics against state-of-the-art methods explicitly tailored to one or a few of them. Our unified solution delivers an implementation-friendly and computationally efficient recipe for safety clearance and provides intellectual economy to an investigation of algorithmic roots of epistemic awareness in deep neural nets.

翻译：我们采用了一种原始的、能够提供上述所有三种基本特性的证据深层学习、神经过程和神经图变机器组合,以量化全部不确定性。 我们遵循三种图像分类基准和两种神经网结构的方法,在多种不确定程度的量化指标方面始终给予有竞争力或优异的分数,以对照明确针对其中一种或数种此类方法的先进方法,确定不同类别在目标领域困难区域(如阶级重叠)的校准概率,以及准确确定出目标领域以外的问题并予以拒绝。我们采用一种原始的、能够提供上述所有三种基本特性的的证据深层学习、神经过程和神经图变机器,以便进行全面不确定性量化。我们遵循三种图像分类基准和两种神经网结构的方法,在多种不确定程度的量化指标方面始终给予有竞争力或优异的分数。我们的统一解决方案为安全清理提供了一种便于执行的、计算高效的配方,并为深神经网内隐含意识的算学根源的调查提供了知识经济。