This project implements variational inference for Bayesian neural networks with PyTorch. While the computational expense is expected to increase in comparison to classical model training, the approach enables a means of uncertainty quantification in deep learning. Only classification problems can be addressed at this point. Another limitation is that the variational distribution, which acts as a parametric posterior approximation, is restricted to a multivariate Gaussian with a diagonal covariance matrix.
pip install -e .
python scripts/main.py fit --config config/moons.yaml
python scripts/main.py fit --config config/mnist.yaml