JavierAntoran/Bayesian-Neural-Networks

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1969 stars · 306 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of PyTorch implementations of Bayesian neural network approximate inference methods, including Bayes by Backprop, MC Dropout, SGLD, KF-Laplace, and SG-HMC. It ships as Jupyter notebooks and scripts with regression and MNIST classification experiments demonstrating uncertainty estimation.

Use cases

• estimate neural network prediction uncertainty
• compare Bayesian deep learning inference methods
• run MC dropout experiments in pytorch
• implement Bayes by Backprop for regression
• detect out-of-distribution inputs with uncertainty
• reproduce Bayesian neural network research results
• train SGLD or SG-HMC models on MNIST

When to choose

• you want reference implementations of multiple Bayesian inference methods in PyTorch
• you are studying or benchmarking uncertainty estimation techniques
• you need runnable notebooks for regression and classification uncertainty experiments

When to avoid

• you need a production-ready, pip-installable library with a stable API
• you require Python 3 support and modern PyTorch versions out of the box
• you need scalable Bayesian inference for large models or datasets

Facets

library · maturity maintenance

machine-learning deep-learning deep-learning machine-learning artificial-intelligence python cross-platform bayesian-neural-networks pytorch uncertainty-quantification variational-inference mc-dropout sgld bayes-by-backprop laplace-approximation research-code jupyter-notebooks gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="JavierAntoran/Bayesian-Neural-Networks")