title: Generalization bounds for deep convolutional neural networks
abstract: We prove bounds on the generalization error of convolutional networks.
  The bounds are in terms of the training loss, the number of
  parameters, the Lipschitz constant of the loss and the distance from
  the weights to the initial weights.  They are independent of the
  number of pixels in the input, and the height and width of hidden
  feature maps.
  We present experiments using CIFAR-10 with varying
  hyperparameters of a deep convolutional network, comparing our bounds
  with practical generalization gaps.
authors: Philip M. Long Hanie Sedghi
hIndex: Philip M. Long 42