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