Deep Neural Networks (DNNs) have proven very effective for classification and generative tasks, and are widely adapted in a variety of fields including vision, robotics, speech processing, and more. Specifically, Deep Belief Networks (DBNs), are graphical model constructed of multiple layers of nodes connected as Markov random fields, have been successfully implemented for tackling such tasks. However, because of the numerous connections between nodes in the networks, DBNs suffer a drawback of being computational intensive. In this work, we exploit an alternative approach based on computation on probabilistic unary streams for designing a more efficient deep neural network architecture for classification.
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