论文标题
通过扩散过程的熵神经最佳传输
Entropic Neural Optimal Transport via Diffusion Processes
论文作者
论文摘要
我们提出了一种新型的神经算法，用于计算样品可访问的连续概率分布之间计算熵最佳传输（EOT）计划的基本问题。我们的算法基于动态版本的EOT的鞍点重新印度，该版本被称为Schrödinger桥问题。与大规模EOT的先前方法相反，我们的算法是端到端的，由单个学习步骤组成，具有快速的推理过程，并允许处理熵正规化系数的小值，这在某些应用问题中特别重要。从经验上讲，我们在几个大规模的EOT任务上显示了该方法的性能。 https://github.com/ngushchin/entropicneuraloptimaltransport
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are accessible by samples. Our algorithm is based on the saddle point reformulation of the dynamic version of EOT which is known as the Schrödinger Bridge problem. In contrast to the prior methods for large-scale EOT, our algorithm is end-to-end and consists of a single learning step, has fast inference procedure, and allows handling small values of the entropy regularization coefficient which is of particular importance in some applied problems. Empirically, we show the performance of the method on several large-scale EOT tasks. https://github.com/ngushchin/EntropicNeuralOptimalTransport
