A key step in many bioinformatics analysis pipelines is the identification of regions of similarity between pairs of DNA sequencing reads. This task, known aspairwise sequence alignment, is a heavy computational burden, particularly in the context of third-generation long-read sequencing technologies,whichproducenoisyreads[45].

Efficient methods that reliably quantify a deep neural network (DNN)'s predictive uncertainty are important for industrial-scale, real-world applications, which include examples such as object recognition in autonomous driving [22], ad click prediction in online advertising [76], and intent understanding inaconversational system [84].

Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology.