Bayesian Neural Networks Reading List

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"
Alternatives To Bayesian Neural Networks Reading List
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Pyro8,356189014 days ago34July 29, 2023248apache-2.0Python
Deep universal probabilistic programming with Python and PyTorch
Zhusuan2,139
2 years ago11mitPython
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Bayesian Neural Networks1,633
7 months ago5mitJupyter Notebook
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Fortuna824
3 months ago4apache-2.0Python
A Library for Uncertainty Quantification.
Bayesian Torch39614 months ago3December 14, 20222bsd-3-clausePython
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
Pyvarinf300
5 years ago2December 13, 20183mitPython
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
Bayesflow216
4 months ago4September 12, 20232mitPython
A Python library for amortized Bayesian workflows using generative neural networks.
Pytensor201173 months ago37January 02, 2024159otherPython
PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
Bayesianrecurrentnn196
5 years ago2mitPython
Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
The Books Making You Better169
4 months ago
A list of time-lasting classic books, which not only help you figure out how it works, but also grasp when it works and why it works in that way.
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