BayesDLL: Bayesian Deep Learning Library
Minyoung Kim Timothy Hospedales
We release a new Bayesian neural network library for PyTorch for large-scale
deep networks. Our library implements mainstream approximate Bayesian inference
algorithms: variational inference, MC-dropout, stochastic-gradient MCMC, and
Laplace approximation. The main differences from other existing Bayesian neural
network libraries are as follows: 1) Our library can deal with very large-scale
deep networks including Vision Transformers (ViTs). 2) We need virtually zero
code modifications for users (e.g., the backbone network definition codes do
not neet to be modified at all). 3) Our library also allows the pre-trained
model weights to serve as a prior mean, which is very useful for performing
Bayesian inference with the large-scale foundation models like ViTs that are
hard to optimise from scratch with the downstream data alone. Our code is
publicly available at: \url{https://github.com/SamsungLabs/BayesDLL}\footnote{A
mirror repository is also available at:
\url{https://github.com/minyoungkim21/BayesDLL}.}.