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Anirudh Goyal

Alumni

Publications

Professor Forcing: A New Algorithm for Training Recurrent Networks
Anirudh Goyal
Alex Lamb
Ying Zhang
Saizheng Zhang
Aaron Courville
Yoshua Bengio
The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the networ… (voir plus)k’s own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.
2015-12-31
Advances in Neural Information Processing Systems 29 (NIPS 2016) (publié)
doi.org
arxiv.org
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
Hugo Larochelle
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.
2015-12-31
arXiv (prépublication)
doi.org
arxiv.org