The model uses an n-dimensional entity embedding as its input which is mapped through a tanh nonlinearity onto an h-dimensional hidden layer, which in turn maps onto an |N |-dimensional output layer (where |N | is the number of the numeric attributes) using a sigmoid nonlinearity.
each unit in the output layer corresponds to one numeric attribute, and the model predicts all numeric attributes simultaneously.