Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
Authors: Meyer Scetbon, Gabriel Peyré, Marco Cuturi
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach yields similar results, yet orders of magnitude faster computation than the So TA entropic GW approaches, on both simulated and real data. |
| Researcher Affiliation | Collaboration | Meyer Scetbon 1 CREST-ENSAE Gabriel Peyr e 2 CNRS and ENS, PSL Marco Cuturi 3 CREST-ENSAE, work partly done at Google, currently at Apple. Correspondence to: meyer scetbon <meyer.scetbon@ensae.fr>. |
| Pseudocode | Yes | Algorithm 1: Entropic-GW |
| Open Source Code | Yes | The code is available at https://github.com/meyerscetbon/Linear Gromov. |
| Open Datasets | Yes | Experiments were run on a Mac Book Pro 2019 laptop, and data from github.com/rsinghlab/SCOT. The code is available at https://github.com/meyerscetbon/Linear Gromov. |
| Dataset Splits | No | The paper mentions synthetic and real datasets, and specific sample sizes for experiments (e.g., 'n = m = 1000 samples'), but it does not provide explicit training/validation/test split percentages or sample counts for these splits. |
| Hardware Specification | Yes | Experiments were run on a Mac Book Pro 2019 laptop |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | In all other experiments, we always set γ = 100 and α = 10 10 for our methods, and only focus on rank r. |