title: Boosted Residual Networks
abstract: In this paper we present a new ensemble method, called Boosted Residual Networks,
  which builds an ensemble of Residual Networks by growing the member
  network at each round of boosting. The proposed approach combines recent developements
  in Residual Networks - a method for creating very deep networks by
  including a shortcut layer between different groups of layers - with the Deep Incremental
  Boosting, which has been proposed as a methodology to train fast ensembles
  of networks of increasing depth through the use of boosting. We demonstrate
  that the synergy of Residual Networks and Deep Incremental Boosting has better
  potential than simply boosting a Residual Network of fixed structure or using the
  equivalent Deep Incremental Boosting without the shortcut layers.
authors: Alan Mosca George D. Magoulas
hIndex: George D. Magoulas 32