Variational Wasserstein gradient flow. (arXiv:2112.02424v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2112.02424

Wasserstein gradient flow has emerged as a promising approach to solve
optimization problems over the space of probability distributions. A recent
trend is to use the well-known JKO scheme in combination with input convex
neural networks to numerically implement the proximal step. The most
challenging step, in this setup, is to evaluate functions involving density
explicitly, such as entropy, in terms of samples. This paper builds on the
recent works with a slight but crucial difference: we propose to utilize …

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