论文标题
ANN中的可解释模型
Interpretable Models in ANNs
论文作者
论文摘要
人工神经网络通常非常复杂，太深了，无法理解。结果，它们通常被称为黑匣子。对于许多现实世界中的问题，基本模式本身非常复杂，因此不存在分析解决方案。但是，在某些情况下，例如，物理定律可以通过相对简单的数学表达式来描述该模式。在这种情况下，我们希望获得一个可读的方程，而不是黑匣子。在本文中，我们试图找到一种解释网络并提取描述模型的人类可读方程的方法。
Artificial neural networks are often very complex and too deep for a human to understand. As a result, they are usually referred to as black boxes. For a lot of real-world problems, the underlying pattern itself is very complicated, such that an analytic solution does not exist. However, in some cases, laws of physics, for example, the pattern can be described by relatively simple mathematical expressions. In that case, we want to get a readable equation rather than a black box. In this paper, we try to find a way to explain a network and extract a human-readable equation that describes the model.
