JavierAntoran/Bayesian-Neural-Networks
PyTorch implementations of Bayesian inference methods for neural networks including Bayes by Backprop, MC Dropout, SGLD, and HMC.
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This repository provides PyTorch implementations of various approximate inference methods for Bayesian neural networks. It includes algorithms like Bayes by Backprop, Monte Carlo Dropout, Stochastic Gradient Langevin Dynamics, Preconditioned SGLD, Kronecker-Factorised Laplace approximation, and Stochastic Gradient Hamiltonian Monte Carlo. The code supports regression and classification experiments on toy datasets and UCI datasets, focusing on uncertainty quantification and out-of-distribution detection in deep learning models.
Frequently asked
- What is JavierAntoran/Bayesian-Neural-Networks?
- PyTorch implementations of Bayesian inference methods for neural networks including Bayes by Backprop, MC Dropout, SGLD, and HMC.
- Is Bayesian-Neural-Networks open source?
- Yes — JavierAntoran/Bayesian-Neural-Networks is open source, released under the MIT license.
- What language is Bayesian-Neural-Networks written in?
- JavierAntoran/Bayesian-Neural-Networks is primarily written in Jupyter Notebook.
- How popular is Bayesian-Neural-Networks?
- JavierAntoran/Bayesian-Neural-Networks has 2k stars on GitHub.
- Where can I find Bayesian-Neural-Networks?
- JavierAntoran/Bayesian-Neural-Networks is on GitHub at https://github.com/JavierAntoran/Bayesian-Neural-Networks.