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Deep Learning using Robust Interdependent Codes

Hugo Larochelle, Dumitru Erhan, Pascal Vincent; JMLR W&CP 5:312-319, 2009.

Abstract

We investigate a simple yet effective method to introduce inhibitory and excitatory interactions between units in the layers of a deep neural network classifier. The method is based on the greedy layer-wise procedure of deep learning algorithms and extends the denoising autoencoder of Vincent~et~al.~\cite{VincentPLarochelleH2008-small} by adding asymmetric lateral connections between its hidden coding units, in a manner that is much simpler and computationally more efficient than previously proposed approaches.We present experiments on two character recognition problems which show for the first time that lateral connections can significantly improve the classification performance of deep networks.



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