In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks.
Description
[1709.05870] ZhuSuan: A Library for Bayesian Deep Learning
%0 Generic
%1 shi2017zhusuan
%A Shi, Jiaxin
%A Chen, Jianfei
%A Zhu, Jun
%A Sun, Shengyang
%A Luo, Yucen
%A Gu, Yihong
%A Zhou, Yuhao
%D 2017
%K 2017 arxiv china deep-learning machine-learning probabilistic-programming tsinghua
%T ZhuSuan: A Library for Bayesian Deep Learning
%U http://arxiv.org/abs/1709.05870
%X In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks.
@misc{shi2017zhusuan,
abstract = {In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks.},
added-at = {2018-05-12T09:22:21.000+0200},
author = {Shi, Jiaxin and Chen, Jianfei and Zhu, Jun and Sun, Shengyang and Luo, Yucen and Gu, Yihong and Zhou, Yuhao},
biburl = {https://www.bibsonomy.org/bibtex/2dabf556dcc216d244ad4bdbf800a2168/achakraborty},
description = {[1709.05870] ZhuSuan: A Library for Bayesian Deep Learning},
interhash = {e8d1d82f11fcc4cda18958f74ed4a86c},
intrahash = {dabf556dcc216d244ad4bdbf800a2168},
keywords = {2017 arxiv china deep-learning machine-learning probabilistic-programming tsinghua},
note = {cite arxiv:1709.05870Comment: The GitHub page is at https://github.com/thu-ml/zhusuan},
timestamp = {2018-05-12T09:22:21.000+0200},
title = {ZhuSuan: A Library for Bayesian Deep Learning},
url = {http://arxiv.org/abs/1709.05870},
year = 2017
}