  Nontrivial connectivity has allowed the training of very deep networks by
addressing the problem of vanishing gradients and offering a more efficient
method of reusing parameters. In this paper we make a comparison between
residual networks, densely-connected networks and highway networks on an image
classification task. Next, we show that these methodologies can easily be
deployed into automatic speech recognition and provide significant improvements
to existing models.
