1 research outputs found
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Department of Computer Science and EngineeringAttention mechanism is effective in both focusing the deep learning models on relevant features and
interpreting them. However, attentions may be unreliable since the networks that generate them are
often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of
input-dependent uncertainty to the attention mechanism, such that it generates attention for each
feature with varying degrees of noise based on the given input, to learn larger variance on instances it
is uncertain about. We learn this Uncertainty-aware Attention (UA) mechanism using variational
inference, and validate it on various risk prediction tasks from electronic health records on which our
model significantly outperforms existing attention models. The analysis of the learned attentions
shows that our model generates attentions that comply with clinicians' interpretation, and provide
richer interpretation via learned variance. Further evaluation of both the accuracy of the uncertainty
calibration and the prediction performance with "I don't know'' decision show that UA yields networks
with high reliability as well.ope