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arXiv · 2002.07501

A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Ziyu Wang·Shuyu Cheng·Yueru Li·Jun Zhu·Bo Zhang
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Abstract

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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Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang. 2020-02-18. A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models. https://arxiv.org/abs/2002.07501

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