In the presence of these data, we optimize the supervised sleep phase objective, which is a Monte Carlo approximation of D KL (D labeled || q φ ).
when fitting our variational approximation q φ , we will optimize a joint objective S = S sup + γ sleep • S unsup , where S sup , to repeat, uses actual annotated lemmata and morphological tags; we balance the two parts of the objective with a scaling parameter γ sleep .