  Machine learning promises methods that generalize well from finite labeled
data. However, the brittleness of existing neural net approaches is revealed by
notable failures, such as the existence of adversarial examples that are
misclassified despite being nearly identical to a training example, or the
inability of recurrent sequence-processing nets to stay on track without
teacher forcing. We introduce a method, which we refer to as \emph{state
reification}, that involves modeling the distribution of hidden states over the
training data and then projecting hidden states observed during testing toward
this distribution. Our intuition is that if the network can remain in a
familiar manifold of hidden space, subsequent layers of the net should be well
trained to respond appropriately. We show that this state-reification method
helps neural nets to generalize better, especially when labeled data are
sparse, and also helps overcome the challenge of achieving robust
generalization with adversarial training.
