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

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.

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

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

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

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

Bayesian Neural Network in PyTorch

Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks

(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

Fully and Partially Bayesian Neural Nets

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

Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch

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

General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.

The implementation of "Uncertainty-Aware Robust Adaptive Video Streaming with Bayesian Neural Network and Model Predictive Control" (NOSSDAV 2021)