For clarity, we focus on K-way classification, where Y = ∆ K is the K-dimensional probability simplex and y ∈ {0, 1} K ⊂ Y is a one-hot encoding of the class label.
our method specification can straightforwardly be applied to other contexts such as regression and sequence learning (e.g., NER tagging, which is a sequence of classification decisions).