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）已在计算机视觉、语音识别和自然语言处理等领域为诸多具有挑战性的机器学习任务（如检测、回归与分类）提供了最先进的结果。尽管取得了成功，这些网络通常基于频率学派框架实现，这意味着它们无法对其预测中的不确定性进行推理。本文介绍贝叶斯神经网络（BNNs）及其实现方面的开创性研究。文中比较了不同的近似推断方法，并以此揭示未来研究可在哪些方面对现有方法进行改进。