Implementation of batched Sinkhorn iterations for entropy-regularized Wasserstein loss
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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
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2019.
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023Neural Schr"odinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assemblycited by 10
- 2024A local squared Wasserstein-2 method for efficient reconstruction of models with uncertaintycited by 2
- 2024Machine Theory of Mind for Autonomous Cyber-Defencecited by 1
- 2024Machine Theory of Mind for Autonomous Cyber-Defence5 Dec 2024
- 2024A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty10 Jun 2024
- 2023Neural Schr"odinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly26 Jul 2023