An Augmented Efficient Backpropagation Training Strategy for Deep Autoassociative Neural Networks
Mark J. Embrechts, Blake J. Hargis, Jonathan D. Linton · 2010
Abstract. 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]. 1