  The past year saw the introduction of new architectures such as Highway
networks and Residual networks which, for the first time, enabled the training
of feedforward networks with dozens to hundreds of layers using simple gradient
descent. While depth of representation has been posited as a primary reason for
their success, there are indications that these architectures defy a popular
view of deep learning as a hierarchical computation of increasingly abstract
features at each layer.
  In this report, we argue that this view is incomplete and does not adequately
explain several recent findings. We propose an alternative viewpoint based on
unrolled iterative estimation -- a group of successive layers iteratively
refine their estimates of the same features instead of computing an entirely
new representation. We demonstrate that this viewpoint directly leads to the
construction of Highway and Residual networks. Finally we provide preliminary
experiments to discuss the similarities and differences between the two
architectures.
