Pytorch source code for paper
Youssef Mroueh and Mattia Rigotti, "Unbalanced Sobolev Descent", in Advances in Neural Information Processing Systems 33 (NeurIPS), Dec. 2020 [arXiv:2009.14148]
- 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
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.
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.
Youssef Mroueh, Mattia Rigotti, "Unbalanced Sobolev Descent", in Advances in Neural Information Processing Systems 33 (NeurIPS), Dec. 2020 [arXiv]