论文标题
贝叶斯神经网络：介绍和调查
Bayesian Neural Networks: An Introduction and Survey
论文作者
论文摘要
神经网络（NNS）为许多具有挑战性的机器学习任务提供了最先进的结果，例如在计算机视觉，语音识别和自然语言处理领域的检测，回归和分类。尽管他们的成功，但他们经常以常见的计划实施，这意味着他们无法理解预测的不确定性。本文介绍了贝叶斯神经网络（BNNS）和有关其实施的开创性研究。比较了不同的近似推理方法，并用于突出显示未来研究可以改善当前方法的地方。
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.
