  We show how deep learning methods can be applied in the context of
crowdsourcing and unsupervised ensemble learning. First, we prove that the
popular model of Dawid and Skene, which assumes that all classifiers are
conditionally independent, is {\em equivalent} to a Restricted Boltzmann
Machine (RBM) with a single hidden node. Hence, under this model, the posterior
probabilities of the true labels can be instead estimated via a trained RBM.
Next, to address the more general case, where classifiers may strongly violate
the conditional independence assumption, we propose to apply RBM-based Deep
Neural Net (DNN). Experimental results on various simulated and real-world
datasets demonstrate that our proposed DNN approach outperforms other
state-of-the-art methods, in particular when the data violates the conditional
independence assumption.
