23, Z must be such that p j sums to 1, yielding the well-known softmax expression.
in the case of sparsemax, note that for any p ∈ △ d , we have B Backward pass for generalized sparse attention mappings When a mapping π Ω is used inside the computation graph of a neural network, the Jacobian of the mapping has the important role of showing how to propagate error information, necessary when training with gradient methods.