  Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep
Belif Networks(DBNs), are powerful in automatic feature extraction,unsupervised
weight initialization and density estimation. In this paper,we demonstrate that
the parameters of these neural nets can be dramatically reduced without
affecting their performance. We describe a method to reduce the parameters
required by RBM which is the basic building block for deep architectures.
Further we propose an unsupervised sparse deep architectures selection
algorithm to form sparse deep neural networks.Experimental results show that
there is virtually no loss in either generative or discriminative performance.
