Augmented efficient BackProp for backpropagation learning in deep autoassociative neural networks (original) (raw)

2010, Proceedings of the International Joint Conference on Neural Networks

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].

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