Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
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Updated
Apr 19, 2024 - Python
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Bayesian deep learning for remaining useful life estimation via Stein variational gradient descent
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"
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Code for the paper "Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift"
Lightweight Bayesian deep learning library for fast prototyping based on PyTorch
PyTorch implementation of "Weight Uncertainty in Neural Networks"
PyTorch implementation of the paper 'Weight Uncertainty in Neural Networks'
Pytorch implementation of Bayes by Backprop from scratch.
Comparison of a network implemented via Variational Inference with the same network implemented via Monte Carlo Dropout
PyTorch implementation of "Weight Uncertainties in Neural Networks" (Bayes-by-Backprop)
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