Bayesian inference with probabilistic programming.

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Awesome resources on normalizing flows.

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

Master Thesis on Bayesian Convolutional Neural Network using Variational Inference

Gaussian Processes for Experimental Sciences

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning

A Python package for building Bayesian models with TensorFlow or PyTorch

PyTorch implementation of "Weight Uncertainty in Neural Networks"

Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)

Bayesian Neural Network in PyTorch

PyTorch Implementations of Dropout Variants

Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks

Fully and Partially Bayesian Neural Nets

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

[ACM MM 2020] Uncertainty-based Traffic Accident Anticipation

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Code for the paper Implicit Weight Uncertainty in Neural Networks

Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch

TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".

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"

A pytorch implementation of MCDO(Monte-Carlo Dropout methods)

Acoustic mosquito detection code with Bayesian Neural Networks

bayesgm: An AI-powered versatile Bayesian Generative Modeling Framework

Python library for Multi-Armed Bandits

A collection of Methods and Models for various architectures of Artificial Neural Networks