  We propose zoneout, a novel method for regularizing RNNs. At each timestep,
zoneout stochastically forces some hidden units to maintain their previous
values. Like dropout, zoneout uses random noise to train a pseudo-ensemble,
improving generalization. But by preserving instead of dropping hidden units,
gradient information and state information are more readily propagated through
time, as in feedforward stochastic depth networks. We perform an empirical
investigation of various RNN regularizers, and find that zoneout gives
significant performance improvements across tasks. We achieve competitive
results with relatively simple models in character- and word-level language
modelling on the Penn Treebank and Text8 datasets, and combining with recurrent
batch normalization yields state-of-the-art results on permuted sequential
MNIST.

Neural and Evolutionary Computing;Computation and Language;Machine Learning