Zhusuan-Jittor

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ZhuSuan-Jittor is a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan-Jittor is built upon Jittor. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan-Jittor 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, SWI, etc.).

• Importance sampling for learning and evaluating models, with programmable proposals.

• MCMC samplers: Hamiltonian Monte Carlo (HMC) with parallel chains, and Stochastic Gradient MCMC (sgmcmc).

Installation

ZhuSuan-Jittor 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.

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

Citing ZhuSuan-Jittor

If you find ZhuSuan-Jittor 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-Jittor better. If you would like to contribute, please check out the guidelines here.