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.

本文提出一种新颖的网络架构搜索(NAS)方法，用于优化Bayesian神经网络（BNN）的精度和不确定性，同时减少推理延迟。与传统的NAS不同，该方法使用来自内部和外部分布的数据搜索模型的不确定性性能，从而可以在网络中搜索贝叶斯层的正确位置，并且相对于流行的BNN基线，使用的运行时仅为其一小部分，将推理运行时成本分别与MCDropout和deep ensemble相比在CIFAR10数据集上分别降低了2.98倍和2.92倍。

贝叶斯神经网络的模型架构调整