Online Sinkhorn: Optimal Transport distances from sample streams
Authors: Arthur Mensch, Gabriel Peyré
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on synthetic 1-d to 10-d data and on real 3-d shape data. |
| Researcher Affiliation | Academia | Arthur Mensch PSL University CNRS, ENS, DMA Paris, France arthur.mensch@m4x.org Gabriel Peyré PSL University CNRS, ENS, DMA Paris, France gabriel.peyre@ens.fr |
| Pseudocode | Yes | Algorithm 1 Online Sinkhorn |
| Open Source Code | No | The paper does not provide a concrete access link or statement about open-sourcing the code for the methodology. |
| Open Datasets | Yes | Stanford 3D scans Turk and Levoy, 1994 |
| Dataset Splits | No | The paper does not specify exact split percentages or methods for validation datasets beyond general use of samples. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We report quantitative results for ε = 10 2 and non fully-corrective online Sinkhorn in the main text, and all other curves in Supp. Fig. 4. In Supp. Fig. 7, we also report results for OT between Gaussians, which is a simpler and less realistic setup, but for which closed-form expressions of the potentials are known Janati et al., 2020. ... We use n(t) = N 100(1 + 0.1t)1/2 results vary little with the exponent. |