  We describe a graph-based semi-supervised learning framework in the context
of deep neural networks that uses a graph-based entropic regularizer to favor
smooth solutions over a graph induced by the data. The main contribution of
this work is a computationally efficient, stochastic graph-regularization
technique that uses mini-batches that are consistent with the graph structure,
but also provides enough stochasticity (in terms of mini-batch data diversity)
for convergence of stochastic gradient descent methods to good solutions. For
this work, we focus on results of frame-level phone classification accuracy on
the TIMIT speech corpus but our method is general and scalable to much larger
data sets. Results indicate that our method significantly improves
classification accuracy compared to the fully-supervised case when the fraction
of labeled data is low, and it is competitive with other methods in the fully
labeled case.
