Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is however challenging to estimate efficiently for large neural networks. To this extent we investigate Delta Variances, a family of algorithms for epistemic uncertainty quantification, that is computationally efficient and convenient to implement. It can be applied to neural networks and more general functions composed of neural networks. As an example we consider a weather simulator with a neural-network-based step function inside -- here Delta Variances empirically obtain competitive results at the cost of a single gradient computation. The approach is convenient as it requires no changes to the neural network architecture or training procedure. We discuss multiple ways to derive Delta Variances theoretically noting that special cases recover popular techniques and present a unified perspective on multiple related methods. Finally we observe that this general perspective gives rise to a natural extension and empirically show its benefit.

翻译：决策者可能会因数据有限而产生的不确定性而受到影响。通过考虑认知不确定性可以缓解这一问题，但对于大型神经网络而言，高效估计认知不确定性具有挑战性。为此，我们研究了Delta方差——一种用于认知不确定性量化的算法族，该算法计算高效且易于实现。它可应用于神经网络以及由神经网络组成的更一般函数。作为示例，我们考虑一个内部包含基于神经网络阶跃函数的天气模拟器——在此场景下，Delta方差以单次梯度计算为代价，在经验上取得了具有竞争力的结果。该方法的便利之处在于无需改变神经网络架构或训练流程。我们从理论上探讨了推导Delta方差的多种方式，指出其特例可恢复流行的技术，并呈现了多种相关方法的统一视角。最后，我们观察到这一通用视角自然衍生出一种扩展方法，并通过实验证明了其优势。