Zhusuan-PyTorch

ZhuSuan-PyTorch is a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan-Pytorch is built upon PyTorch. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan-PyTorch provides deep learning style primitives and algorithms for building probabilistic models and applying Bayesian inference. The supported inference algorithms include:

• Variational inference with programmable variational posteriors, various objectives and advanced gradient estimators (SGVB, etc.).

• MCMC samplers: Stochastic Gradient MCMC (sgmcmc), etc.

Installation

ZhuSuan-PyTorch is still under development. Before the first stable release (1.0), please clone the repository and run

pip install .

in the main directory. This will install ZhuSuan and its dependencies automatically.

If you are developing ZhuSuan, you may want to install in an "editable" or "develop" mode. Please refer to the Contributing section below.

Documentation

• Tutorials and API docs

Examples

We provide examples on traditional hierarchical Bayesian models and recent deep generative models.

• Bayesian Neural Networks: SGVB, SGMCMC
• Variational Autoencoder (VAE): SGVB,

Citing ZhuSuan-PyTorch

If you find ZhuSuan-PyTorch useful, please cite it in your publications.

@ARTICLE{zhusuan2017,
  title={Zhu{S}uan: A Library for {B}ayesian Deep Learning},
  author={Shi, Jiaxin and Chen, Jianfei. and Zhu, Jun and Sun, Shengyang
          and Luo, Yucen and Gu, Yihong and Zhou, Yuhao},
  journal={arXiv preprint arXiv:1709.05870},
  year=2017,
}

Contributing

We always welcome contributions to help make ZhuSuan-PyTorch better. If you would like to contribute, please check out the guidelines here.