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János Kramár

Alumni

Publications

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
David Krueger
Tegan Maharaj
János Kramár
Mohammad Pezeshki
Nicolas Ballas
Nan Rosemary Ke
Anirudh Goyal
Yoshua Bengio
Aaron Courville
Christopher Pal
We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (voir plus)r 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.
2016-12-31
International Conference on Learning Representations (publié)
doi.org
arxiv.org