# Implementation of batched Sinkhorn iterations for entropy-regularized Wasserstein loss

arXiv: https://arxiv.org/abs/1907.01729 · HTML: https://mlsanity.com/paper/1907.01729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Neural Schr"odinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly](https://mlsanity.com/paper/2307.14442/) (2023)
- [A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty](https://mlsanity.com/paper/2406.06825/) (2024)
- [Machine Theory of Mind for Autonomous Cyber-Defence](https://mlsanity.com/paper/2412.04367/) (2024)

## Abstract (arXiv)

In this report, we review the calculation of entropy-regularised Wasserstein loss introduced by Cuturi and document a practical implementation in PyTorch. Code is available at https://github.com/t-vi/pytorch-tvmisc/blob/master/wasserstein-distance/Pytorch_Wasserstein.ipynb