We introduce Augmented Efficient BackProp, a strategy for applying the backpropagation algorithm to deep autoencoders, i.e. autoassociators with many hidden layers, without relying on a weight initialization using restricted Boltzmann machines (RBMs). This training method, benchmarked on three different types of application datasets, is an extension of Efficient BackProp, first proposed by LeCun et al. (12).
['Mark J. Embrechts', 'Blake J. Hargis', 'Jonathan D. Linton']
An Augmented Efficient Backpropagation Training Strategy for Deep Autoassociative Neural Networks