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Recurrent neural networks can be difficult to train on long sequence data dueto the well-known vanishing gradient problem. Some architectures incorporatemethods to reduce RNN state updates, therefore allowing the network to preservememory over long temporal intervals. To address these problems of convergence,this paper proposes a timing-gated LSTM RNN model, called the Gaussian-gatedLSTM (g-LSTM). The time gate controls when a neuron can be updated duringtraining, enabling longer memory persistence and better error-gradient flow.This model captures long-temporal dependencies better than an LSTM and the timegate parameters can be learned even from non-optimal initialization values.Because the time gate limits the updates of the neuron state, the number ofcomputes needed for the network update is also reduced. By adding acomputational budget term to the training loss, we can obtain a network whichfurther reduces the number of computes by at least 10x. Finally, by employing atemporal curriculum learning schedule for the g-LSTM, we can reduce theconvergence time of the equivalent LSTM network on long sequences.

Reducing state updates via Gaussian-gated LSTMs