Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
Authors: Geert-Jan Huizing, Laura Cantini, Gabriel Peyré
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we showcase Wasserstein Singular Vectors on a single-cell RNA-sequencing dataset. Section 5 demonstrates the potential of Wasserstein Singular Vectors compared to ad-hoc applications of Optimal Transport, by studying a single-cell RNA-sequencing dataset. |
| Researcher Affiliation | Academia | 1D epartement de math ematiques et applications de l Ecole Normale Sup erieure, CNRS, Ecole Normale Sup erieure, Universit e PSL, 75005, Paris, France 2Computational Systems Biology Team, Institut de Biologie de l Ecole Normale Sup erieure, CNRS, INSERM, Ecole Normale Sup erieure, Universit e PSL, 75005, Paris, France. |
| Pseudocode | No | The paper describes algorithms in prose and mathematical formulations but does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | A Python package implementing all algorithms in this paper is available at github.com/gjhuizing/wsingular. |
| Open Datasets | Yes | A commonly analyzed sc RNA-seq dataset is the PBMC 3k dataset produced by 10X Genomics, obtained through the function pbmc3k of Scanpy (Wolf et al., 2018). |
| Dataset Splits | No | The paper describes the dataset and its preprocessing but does not explicitly state train/validation/test splits or cross-validation methodology for the experiments. |
| Hardware Specification | Yes | CPU computations were performed on a Dell Latitude 5420 with an 8 core 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz CPU. GPU computations were performed on Nvidia V100 SXM2 32 Go GPUs. |
| Software Dependencies | No | The paper mentions software like 'Python', 'POT library', and 'Scikit-learn' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Wasserstein Singular Vectors (τ = 0.001, ε = 0.1), approached by 15 power iterations |