Abstract: A method for an application provides weights for a neural network configured to dynamically generate a training for the neural network to detect uncertainty with regards to data input to the neural network. A training loss is determined for the neural network to minimize an expected Lp norm of a prediction error, wherein prediction probabilities follow a Dirichlet distribution. A closed-form approximation to the training loss is derived. The neural network is trained to infer parameters of the Dirichlet distribution, wherein the neural network learns distributions over class probability vectors. The Dirichlet distribution is regularized via an information divergence. A maximum entropy penalty is applied to an adversarial example to maximize uncertainty near an edge of the Dirichlet distribution.