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.
翻译:人造神经网络往往非常复杂,而且太深,无法让人类理解。因此,它们通常被称为黑盒。对于许多现实世界的问题,其基本模式本身非常复杂,因此不存在分析解决方案。然而,在某些情况下,物理定律(例如,物理定律)可以用相对简单的数学表达法来描述该模式。在这种情况下,我们想获得一个可读的方程式,而不是黑盒。在本文中,我们试图找到一种解释一个网络的方法,并提取一个描述模型的可读方程式。