Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.

翻译：神经网络（NNs）在计算机视觉、语音识别和自然语言处理等领域的诸多具有挑战性的机器学习任务（如检测、回归与分类）中，已提供了最先进的结果。尽管取得了成功，但神经网络通常以频率学派方式实现，这意味着其无法对其预测中的不确定性进行推理。本文介绍了贝叶斯神经网络（BNNs）及关于其实现的开创性研究。本文比较了不同的近似推断方法，并用其来指明未来研究可在哪些方面改进现有方法。