  To model time-varying nonlinear temporal dynamics in sequential data, a
recurrent network capable of varying and adjusting the recurrence depth between
input intervals is examined. The recurrence depth is extended by several
intermediate hidden state units, and the weight parameters involved in
determining these units are dynamically calculated. The motivation behind the
paper lies on overcoming a deficiency in Recurrent Highway Networks and
improving their performances which are currently at the forefront of RNNs: 1)
Determining the appropriate number of recurrent depth in RHN for different
tasks is a huge burden and just setting it to a large number is computationally
wasteful with possible repercussion in terms of performance degradation and
high latency. Expanding on the idea of adaptive computation time (ACT), with
the use of an elastic gate in the form of a rectified exponentially decreasing
function taking on as arguments as previous hidden state and input, the
proposed model is able to evaluate the appropriate recurrent depth for each
input. The rectified gating function enables the most significant intermediate
hidden state updates to come early such that significant performance gain is
achieved early. 2) Updating the weights from that of previous intermediate
layer offers a richer representation than the use of shared weights across all
intermediate recurrence layers. The weight update procedure is just an
expansion of the idea underlying hypernetworks. To substantiate the
effectiveness of the proposed network, we conducted three experiments:
regression on synthetic data, human activity recognition, and language modeling
on the Penn Treebank dataset. The proposed networks showed better performance
than other state-of-the-art recurrent networks in all three experiments.
