bayesian-neural-networks

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

bayesgm: An AI-powered versatile Bayesian Generative Modeling Framework

[ACM MM 2020] Uncertainty-based Traffic Accident Anticipation