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C.1 2DSyntheticBenchmark For both benchmarks, we sample 500 observationsxi=(x1i,x2i)from each of the twoin-domain classes (orange and blue), and consider a deep architecture ResFFN-12-128, which contains 12 residual feedforward layers with 128 hidden units and dropout rate 0.01.
543e83748234f7cbab21aa0ade66565f-Paper.pdf
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].
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