To this end, we compute the softmax normalization over all incoming edges for node i: It provides us a way to compare all incoming edges in the same manner, rather than making a local decision via a bundle of edges from node j.
this model reduces the architecture search problem to learn continuous variables {α i,j k }, which can be implemented using efficient gradient descent methods.