  We present an approach to design the grid searches for hyper-parameter
optimization for recurrent neural architectures. The basis for this approach is
the use of mutual information to analyze long distance dependencies (LDDs)
within a dataset. We also report a set of experiments that demonstrate how
using this approach, we obtain state-of-the-art results for DilatedRNNs across
a range of benchmark datasets.
