  Extended Kalman Filtering (EKF) can be used to propagate and quantify input
uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the
input distribution. This methodology yields results comparable to existing
methods of uncertainty propagation for DNNs while lowering the computational
overhead considerably. Additionally, EKF allows model error to be naturally
incorporated into the output uncertainty.
