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Extended Kalman Filtering (EKF) can be used to propagate and quantify inputuncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on theinput distribution. This methodology yields results comparable to existingmethods of uncertainty propagation for DNNs while lowering the computationaloverhead considerably. Additionally, EKF allows model error to be naturallyincorporated into the output uncertainty.

Uncertainty Propagation in Deep Neural Networks Using Extended Kalman Filtering