ZhuSuan: A Library for Bayesian Deep Learning
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Bayesian Data Sketching for Varying Coefficient Regression Models
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
Statistical Machine Learning for Astronomy -- A Textbook
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models
Active Learning for Manifold Gaussian Process Regression
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
토론
이 논문과 연결된 포럼 글이 아직 없습니다.