论文标题
适用于地球系统可变性的回声状态网络的层相关性传播
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability
论文作者
论文摘要
已知人工神经网络（ANN）是许多硬性问题的强大方法（例如，图像分类，语音识别或时间序列预测）。但是，这些模型倾向于产生黑盒结果，并且通常难以解释。层面上的相关性传播（LRP）是一种广泛使用的技术，可以了解ANN模型如何得出结论并了解模型学到了什么。在这里，我们将重点放在回声状态网络（ESN）作为某种类型的复发性神经网络（也称为储层计算）上。 ESN易于训练，只需要少量可训练的参数，但仍然是黑框型号。我们展示了如何将LRP应用于ESN以打开黑框。我们还展示了如何不仅可以用于时间序列预测，还可以用于图像分类：我们的ESN模型可作为来自海面温度异常的El Nino Southern振荡（ENSO）的检测器。 ENSO实际上是一个众所周知的问题，并且以前已经进行了广泛的讨论。但是在这里，我们使用这个简单的问题来证明LRP如何显着增强ESN的解释性。
Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification, speech recognition or time series prediction). However, these models tend to produce black-box results and are often difficult to interpret. Layer-wise relevance propagation (LRP) is a widely used technique to understand how ANN models come to their conclusion and to understand what a model has learned. Here, we focus on Echo State Networks (ESNs) as a certain type of recurrent neural networks, also known as reservoir computing. ESNs are easy to train and only require a small number of trainable parameters, but are still black-box models. We show how LRP can be applied to ESNs in order to open the black-box. We also show how ESNs can be used not only for time series prediction but also for image classification: Our ESN model serves as a detector for El Nino Southern Oscillation (ENSO) from sea surface temperature anomalies. ENSO is actually a well-known problem and has been extensively discussed before. But here we use this simple problem to demonstrate how LRP can significantly enhance the explainablility of ESNs.
