  In this paper, we explore ordinal classification (in the context of deep
neural networks) through a simple modification of the squared error loss which
not only allows it to not only be sensitive to class ordering, but also allows
the possibility of having a discrete probability distribution over the classes.
Our formulation is based on the use of a softmax hidden layer, which has
received relatively little attention in the literature. We empirically evaluate
its performance on the Kaggle diabetic retinopathy dataset, an ordinal and
high-resolution dataset and show that it outperforms all of the baselines
employed.
