A key challenge for deploying deep neural networks (DNNs) in safety critical
settings is the need to provide rigorous ways to quantify their uncertainty. In
this paper, we propose a novel algorithm for constructing predicted
classification confidences for DNNs that comes with provable correctness
guarantees. Our approach uses Clopper-Pearson confidence intervals for the
Binomial distribution in conjunction with the histogram binning approach to
calibrated prediction. In addition, we demonstrate how our predicted
confidences can be used to enable downstream guarantees in two settings: (i)
fast DNN inference, where we demonstrate how to compose a fast but inaccurate
DNN with an accurate but slow DNN in a rigorous way to improve performance
without sacrificing accuracy, and (ii) safe planning, where we guarantee safety
when using a DNN to predict whether a given action is safe based on visual
observations. In our experiments, we demonstrate that our approach can be used
to provide guarantees for state-of-the-art DNNs.

本文提出了一种新算法来构建深度神经网络的预测分类置信度，并证明了其正确性保证。此外，展示了如何利用预测置信度实现高速 DNN 推理和安全规划，并在实验中展示了其在最先进的 DNN 中提供保证的能力。