  We introduce a method for automatically selecting the path, or syllabus, that
a neural network follows through a curriculum so as to maximise learning
efficiency. A measure of the amount that the network learns from each data
sample is provided as a reward signal to a nonstationary multi-armed bandit
algorithm, which then determines a stochastic syllabus. We consider a range of
signals derived from two distinct indicators of learning progress: rate of
increase in prediction accuracy, and rate of increase in network complexity.
Experimental results for LSTM networks on three curricula demonstrate that our
approach can significantly accelerate learning, in some cases halving the time
required to attain a satisfactory performance level.
