Bayesian inference with probabilistic programming.

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

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

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

Awesome resources on normalizing flows.

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

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

Master Thesis on Bayesian Convolutional Neural Network using Variational Inference

Gaussian Processes for Experimental Sciences

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).

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

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

PyTorch Implementations of Dropout Variants

Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks

Bayesian Neural Network in PyTorch

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

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