论文标题
从神经网络模型中推断：一个警示性的故事
Extrapolating from neural network models: a cautionary tale
论文作者
论文摘要
我们提出了三种不同的方法来估计使用神经网络做出的预测。所有这些都代表外推误差的下限。例如，我们不包括针对输入数据的小扰动的鲁棒性分析。 首先，我们通过简单的玩具模型说明了这些方法，然后将它们应用于与核质量有关的一些现实情况。通过使用使用液体滴度模型或Skyrme能量密度函数模拟的理论数据，我们将神经网络的外推性能基于Segrè图表的外推性能，远离用于训练和验证的区域。最后，我们讨论错误条如何帮助识别推断何时变得过于不确定，从而不可靠
We present three different methods to estimate error bars on the predictions made using a neural network. All of them represent lower bounds for the extrapolation errors. For example, we did not include an analysis on robustness against small perturbations of the input data. At first, we illustrate the methods through a simple toy model, then, we apply them to some realistic cases related to nuclear masses. By using theoretical data simulated either with a liquid-drop model or a Skyrme energy density functional, we benchmark the extrapolation performance of the neural network in regions of the Segrè chart far away from the ones used for the training and validation. Finally, we discuss how error bars can help identifying when the extrapolation becomes too uncertain and thus unreliable
