Unbalanced Sobolev Descent

Pytorch source code for paper

Youssef Mroueh and Mattia Rigotti, "Unbalanced Sobolev Descent", in Advances in Neural Information Processing Systems 34 (NeurIPS), Dec. 2020 [arXiv:2009.14148]

Requirements

• Python 3.6 or above
• PyTorch 1.6.0
• Numpy 1.19.1
• SciPy 1.5.2
• Matplotlib 3.3.1
• PIL 7.2.0

These can be installed using pip by running:

>> pip install -r requirements.txt

Usage

Synthetic data experiments

To reproduce the the flow simulations between synthetic distributions (Figs 1, 2, 5 and 6) first run the bash script experiments.bash, which calls the main python scripts with the appropriate paramters to reproduce the figure:

>> bash experiments.bash

The results will be saved in the folder final_outputs and will be used by the notebook plot_synthetic_data.ipynb to generate the Figures 1, 2, 5 and 6 in the paper.

Interpolation analysis of scRNA-seq data

The notebook wot_comparison.ipynb reproduces the interpolation analysis of single-cell RNA sequencing data and generates the relative plots (Figs 4 and 8 in the paper). Please, refer to the instruction in the notebook to download and prepare the data that is used.

Citation

Youssef Mroueh, Mattia Rigotti, "Unbalanced Sobolev Descent", in Advances in Neural Information Processing Systems 34 (NeurIPS), Dec. 2020 [arXiv]