  Stochastic Gradient Descent (SGD) is arguably the most popular of the machine
learning methods applied to training deep neural networks (DNN) today. It has
recently been demonstrated that SGD can be statistically biased so that certain
elements of the training set are learned more rapidly than others. In this
article, we place SGD into a feedback loop whereby the probability of selection
is proportional to error magnitude. This provides a novelty-driven oddball SGD
process that learns more rapidly than traditional SGD by prioritising those
elements of the training set with the largest novelty (error). In our DNN
example, oddball SGD trains some 50x faster than regular SGD.
