论文标题
学习从熵肯塔托维奇电势从归一流的流动
Learning normalizing flows from Entropy-Kantorovich potentials
论文作者
论文摘要
我们从双重视角进行学习的问题，该问题是由熵登记的最佳运输动机的，其中连续归一化流是标量电势函数的梯度。该公式使我们能够训练仅由标量电势函数组成的双重目标，并消除了在训练过程中明确计算归一流流量的负担。训练后，很容易从潜在功能中恢复归一化流量。
We approach the problem of learning continuous normalizing flows from a dual perspective motivated by entropy-regularized optimal transport, in which continuous normalizing flows are cast as gradients of scalar potential functions. This formulation allows us to train a dual objective comprised only of the scalar potential functions, and removes the burden of explicitly computing normalizing flows during training. After training, the normalizing flow is easily recovered from the potential functions.
