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Submitted by Assigned_Reviewer_1 Q1 This paper proposes to apply a recent method for deep unsupervised learning called ladder neural network to supervised learning tasks, by combining the original objectives with an additional supervised objective applied at the top of the ladder network. The ladder neural network idea consists of learning as many denoising autoencoding criterions as there are layers in the network, and where the denoising uses the representation at the given layer, and in the next layer. The method is simple and straightforward, and can be graphically depicted as a neural network (as it is done in Figure 1). Particular attention is dedicated to the choice of the denoising architecture, where the multiplicative interaction between the lateral and top-down connections are made explicit in the model. However, authors show that the choice of denoising model is not crucial, and good results can also be obtained with a variety of denoising models.