Geometry-Aware Generative Autoencoder for
Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
If you find this work useful in your research, please consider citing:
@inproceedings{sun2025geometry,
title={Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds},
author={Sun, Xingzhi and Liao, Danqi and MacDonald, Kincaid and Zhang, Yanlei and Liu, Chen and Huguet, Guillaume and Wolf, Guy and Adelstein, Ian and Rudner, Tim GJ and Krishnaswamy, Smita},
booktitle={International Conference on Artificial Intelligence and Statistics},
year={2025},
organization={PMLR},
}conda create -n dmae -c conda-forge python=3.11.5
conda activate dmae
pip install -r requirements.txt
pip install -e . # install the package in dev mode.If you also want to use jupyter notebooks, install
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=dmae- Generate the data: notebooks/swiss_roll_data.ipynb
- run the model:
cd src
python main.py \
logger.use_wandb=False \
data.file_type=npy \
data.require_phate=False \
data.datapath=../data/swiss_roll.npy \
data.phatepath=../data/swiss_roll_phate.npy \
training.max_epochs=5- check the results: notebooks/swiss_roll_result.ipynb
- download the data from https://github.com/KrishnaswamyLab/PHATE/blob/main/data/BMMC_myeloid.csv.gz
- prepare the data with notebooks/myeloid_data.ipynb
- run the model:
cd src
python main.py \
logger.use_wandb=False \
data.file_type=h5ad \
data.require_phate=False \
data.datapath=../data/BMMC_myeloid.h5ad- check the results: notebooks/BMMC_myeloid_result.ipynb
- Download the data from kaggle
- Prepare the data with notebooks/multi_data.ipynb and notebooks/cite_data.ipynb
- Train the model (the example runs for CITE data in 100 PCA dimension)
cd notebooks/flow_matching
./train.sh- Evaluate the model (the example runs for CITE data in 100 PCA dimension)
cd notebooks/flow_matching
./eval.sh