This repository contains code for reproducing results for ICML 2020 paper.
"Latent Variable Modeling with Hyperbolic Normalizing Flows", by:
Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton
ArXiv Link: https://arxiv.org/pdf/2002.06336.pdf If this repository is helpful in your research, please consider citing us.
@article{bose2020latent,
title={Latent Variable Modelling with Hyperbolic Normalizing Flows},
author={Bose, Avishek Joey and Smofsky, Ariella and Liao, Renjie and Panangaden, Prakash and Hamilton, William L},
journal={Proceedings of the 37th International Conference on Machine Learning},
year={2020}
}
Main Python Packages:
- Pytorch Geometric: https://github.com/rusty1s/pytorch_geometric Follow the installation instructions carefully for this package! Make sure all your environment Path variables are exactly as outlined otherwise you will get weird symbol errors
- Pytorch 1.5
- WandB for logging
Other packages can be found in Requirements.txt but not all from that list are needed.
Download the datasets:
python -m data.download
python main.py --dataset=mnist --batch_size=100 --epochs=100 --model=hyperbolic --wandb --namestr="MNIST 2-HyperbolicVAE"
python main.py --dataset=mnist --batch_size=100 --epochs=100 --model=euclidean --flow_model=RealNVP --wandb --namestr="MNIST 2-Hyperbolic 2-RealNVP"
python main.py --dataset=mnist --batch_size=100 --epochs=100 --model=hyperbolic --flow_model=TangentRealNVP --n_blocks=4 --wandb --namestr="MNIST 2-Hyperbolic 4-TangentRealNVP"
- "A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning": https://github.com/pfnet-research/hyperbolic_wrapped_distribution
- "Mixed-Curvature Variational Autoencoder": https://www.dropbox.com/s/tzilf229js1gsqu/mvae.zip?dl=0
- "Hyperbolic Neural Networks": https://github.com/dalab/hyperbolic_nn
- "Hyperbolic Graph Convolutional Neural Networks": https://github.com/HazyResearch/hgcn