Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences. However, understanding how RNN models produce final predictions remains a major challenge. Existing work on interpreting RNN models for sequence predictions often focuses on explaining predictions for individual data instances (e.g., patients or students). Because state-of-the-art predictive models are formed with millions of parameters optimized over millions of instances, explaining predictions for single data instances can easily miss a bigger picture. Besides, many outperforming RNN models use multi-hot encoding to represent the presence/absence of features, where the interpretability of feature value attribution is missing. We present ViSFA, an interactive system that visually summarizes feature attribution over time for different feature values. ViSFA scales to large data such as the MIMIC dataset containing the electronic health records of 1.2 million high-dimensional temporal events. We demonstrate that ViSFA can help us reason RNN prediction and uncover insights from data by distilling complex attribution into compact and easy-to-interpret visualizations.
翻译:深度经常性神经网络(RNN)越来越多地用于与时间序列有关的决策。然而,了解RNN模型如何产生最终预测仍然是一个重大挑战。关于解释RNN模型用于序列预测的现有工作往往侧重于解释个别数据实例(如病人或学生)的预测。由于最先进的预测模型是用数百万个实例中最优化的数以百万计的参数组成的,解释单一数据实例的预测很容易错开一个大局。此外,许多优秀的RNN模型使用多热编码来代表特征的存在/缺乏,而特征值属性特性属性的可解释性缺失。我们介绍VisFA,这是一个交互系统,直观地概括不同特征值的属性。VisFA对包含120万个高位时间事件电子健康记录等大型数据的天平比。我们证明,VISFA通过将复杂属性转换为紧凑和易于互动的可视化,可以帮助我们为RNN的预测和从数据中获取洞见。