Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to quantify the uncertainty of model predictions but come with a prohibitive computation cost for both training and inference. In this work, we show a novel network architecture search (NAS) that optimizes BNNs for both accuracy and uncertainty while having a reduced inference latency. Different from canonical NAS that optimizes solely for in-distribution likelihood, the proposed scheme searches for the uncertainty performance using both in- and out-of-distribution data. Our method is able to search for the correct placement of Bayesian layer(s) in a network. In our experiments, the searched models show comparable uncertainty quantification ability and accuracy compared to the state-of-the-art (deep ensemble). In addition, the searched models use only a fraction of the runtime compared to many popular BNN baselines, reducing the inference runtime cost by $2.98 \times$ and $2.92 \times$ respectively on the CIFAR10 dataset when compared to MCDropout and deep ensemble.

翻译：Bayesian Neural Networks (BNNs) 提供了一个数学基础框架,用于量化模型预测的不确定性,但对于培训和推论而言,计算成本都令人望而却步。在这项工作中,我们展示了一种新的网络结构搜索(NAS),在精确性和不确定性方面优化BNS,同时降低推导时间。不同于仅仅为分布可能性优化的Canonical NAS, 拟议的计划搜索,利用分布和发送之外的数据来寻找不确定性的性能。我们的方法是能够寻找Bayesian层在网络中的正确位置。在我们的实验中,搜索模型显示与最先进的(深合金)相比具有可比的不确定性量化能力和准确性。此外,搜索模型只使用了运行时间的一小部分,而与许多流行的BNNN基线相比,在CIFAR10数据集中,与MCDropout和深制构件相比,将预测时间成本分别减少2.98美元和2.92美元。