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 multiarmed 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.
In REF a multi-armed bandit algorithm is used to determine a syllabus, where the rate of increase in prediction accuracy and network complexity are utilized as reward signals.
Automated Curriculum Learning for Neural Networks
ArXiv
ArXiv
Computer Science