Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quantify uncertainty while making decisions is essential for understanding when the model is over-/under-confident, and hence BNNs are attracting interest in safety-critical applications, such as autonomous driving, healthcare, and robotics. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their increased memory and compute costs. In this work, we investigate quantisation of BNNs by compressing 32-bit floating-point weights and activations to their integer counterparts, that has already been successful in reducing the compute demand in standard pointwise neural networks. We study three types of quantised BNNs, we evaluate them under a wide range of different settings, and we empirically demonstrate that a uniform quantisation scheme applied to BNNs does not substantially decrease their quality of uncertainty estimation.
翻译:Bayesian神经网络(BNNs)在许多研究领域正在取得显著进展,这些领域的决策需要伴有不确定性的估计。能够量化不确定性,同时作出决定对于理解模型过度/不自信时的理解至关重要,因此BNNs吸引了对安全关键应用的兴趣,例如自主驾驶、保健和机器人。然而,BNNs在工业实践中没有被广泛使用,主要是因为其记忆和计算成本增加。在这项工作中,我们通过压缩32位浮点重量和激活其整数对等单位来调查BNs的量化,这在降低标准点神经网络的计算需求方面已经取得了成功。我们研究了三种四分化的BNNs类型,在不同的环境下对它们进行了评估,我们从经验上证明,适用于BNs的统一的量化计划不会大幅降低其不确定性估计的质量。