  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.
