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Recurrent Neural Networks (RNNs) continue to show outstanding performance insequence modeling tasks. However, training RNNs on long sequences often facechallenges like slow inference, vanishing gradients and difficulty in capturinglong term dependencies. In backpropagation through time settings, these issuesare tightly coupled with the large, sequential computational graph resultingfrom unfolding the RNN in time. We introduce the Skip RNN model which extendsexisting RNN models by learning to skip state updates and shortens theeffective size of the computational graph. This model can also be encouraged toperform fewer state updates through a budget constraint. We evaluate theproposed model on various tasks and show how it can reduce the number ofrequired RNN updates while preserving, and sometimes even improving, theperformance of the baseline RNN models. Source code is publicly available athttps://imatge-upc.github.io/skiprnn-2017-telecombcn/ .

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks