论文标题
不确定性分解的介绍和示例
Introduction and Exemplars of Uncertainty Decomposition
论文作者
论文摘要
不确定性在机器学习领域起着至关重要的作用。模型可信度和性能都需要了解不确定性，尤其是对于在高速应用中使用的模型，错误会导致灾难性后果，例如医疗诊断和自动驾驶。因此，近年来，不确定性分解和量化引起了越来越多的关注。这份简短的报告旨在通过介绍两种类型的不确定性和几个分解示例，包括最大似然估计，高斯过程，深度神经网络和集合学习，来揭开不确定性分解的概念。最后，在本研讨会中与其他主题的交叉连接，并提供了两个结论。
Uncertainty plays a crucial role in the machine learning field. Both model trustworthiness and performance require the understanding of uncertainty, especially for models used in high-stake applications where errors can cause cataclysmic consequences, such as medical diagnosis and autonomous driving. Accordingly, uncertainty decomposition and quantification have attracted more and more attention in recent years. This short report aims to demystify the notion of uncertainty decomposition through an introduction to two types of uncertainty and several decomposition exemplars, including maximum likelihood estimation, Gaussian processes, deep neural network, and ensemble learning. In the end, cross connections to other topics in this seminar and two conclusions are provided.
