There are 45 repositories under bayesian-deep-learning topic.
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.
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
Bayesian Deep Learning Benchmarks
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
Bayesian Deep Learning: A Survey
Building a Bayesian deep learning classifier
TransMorph: Transformer for Unsupervised Medical Image Registration (PyTorch)
Sparse Variational Dropout, ICML 2017
Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
Second Order Optimization and Curvature Estimation with K-FAC in JAX.
PyTorch implementation of "Weight Uncertainty in Neural Networks"
Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.
In which I try to demystify the fundamental concepts behind Bayesian deep learning.
MLSS2019 Tutorial on Bayesian Deep Learning
Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, 2022)
(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)
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
Uncertainty Guided Progressive GANs for Medical Image Translation
A PyTorch Implementation of Convolutional Conditional Neural Process.
The Deep Weight Prior, ICLR 2019
Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.
This repository contains an official implementation of LPBNN.
Implementations of the ICML 2017 paper (with Yarin Gal)
General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.
The official implementation of "Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation" via Pytorch
ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models
Open Source Photometric classification https://supernnova.readthedocs.io
Code for "BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning"