Papers › A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang
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 present a scalable approximation to a general family of learning objectives including score matching, by observing a new connection between these objectives and Wasserstein gradient flows. We present applications with promise in learning neural density estimators on manifolds, and training implicit variational and Wasserstein auto-encoders with a manifold-valued prior.
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