  We introduce a novel way to incorporate prior information into (semi-)
supervised non-negative matrix factorization, which we call differentiable
dictionary search. It enables general, highly flexible and principled modelling
of mixtures where non-linear sources are linearly mixed. We study its behavior
on an audio decomposition task, and conduct an extensive, highly controlled
study of its modelling capabilities.
