# DAVIDSTUTZ

07thAUGUST2018

$\frac{1}{T} \sum_{i=1}^T \hat{y}_i^T \hat{y}_i - \left(\sum_{i=1}^T \hat{y}_i\right)\left(\sum_{i=1}^T \hat{y}_i\right)$
where $\{\hat{y}_1,\ldots,\hat{y}_T\}$ is a set of stochastic predictions (i.e. predictions with different noise patterns in the dropout layers). Here, is can easily be seen that this measure corresponds to a variance computation where the first term is correlation and the second term is the product of expectations. In Figure 1, the authors illustrate the distributions of this uncertainty measure for regular training samples, adversarial samples and noisy samples for two attacks (BIM and JSMA, see paper for details).