  We introduce the SaaS Algorithm for semi-supervised learning, which uses
learning speed during stochastic gradient descent in a deep neural network to
measure the quality of an iterative estimate of the posterior probability of
unknown labels. Training speed in supervised learning correlates strongly with
the percentage of correct labels, so we use it as an inference criterion for
the unknown labels, without attempting to infer the model parameters at first.
Despite its simplicity, SaaS achieves state-of-the-art results in
semi-supervised learning benchmarks.
