Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative.   In this paper,we connect a general family of learning objectives including score matching to Wassersteingradient flows. This connection enables us to design a scalable approximation to theseobjectives, with a form similar to single-step contrastive divergence. We present applications in training implicit variational and Wasserstein auto-encoders with manifold-valued priors.
We present a scalable approximation to a wide range of EBM objectives, and applications in implicit VAEs and WAEs