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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper described a supervised version on a recently proposed unsupervised generative stochastic networks. The trick is to put a loss on the difference between the label highest-level hidden units, and jointly train on the supervised and unsupervised tasks. The user did extensive experiments on hyper-parameter analysis and achieved near state-of-art performance on MNIST dataset. The paper is very well written and is easy to follow.