  Monte Carlo dropout may effectively capture model uncertainty in deep
learning, where a measure of uncertainty is obtained by using multiple
instances of dropout at test time. However, Monte Carlo dropout is applied
across the whole network and thus significantly increases the computational
complexity, proportional to the number of instances. To reduce the
computational complexity, at test time we enable dropout layers only near the
output of the neural network and reuse the computation from prior layers while
keeping, if any, other dropout layers disabled. Additionally, we leverage the
side information about the ideal distributions for various input samples to do
`error correction' on the predictions. We apply these techniques to the radio
frequency (RF) transmitter classification problem and show that the proposed
algorithm is able to provide better prediction uncertainty than the simple
ensemble average algorithm and can be used to effectively identify transmitters
that are not in the training data set while correctly classifying transmitters
it has been trained on.
