BayesDLL: Bayesian Deep Learning Library

• URL: http://arxiv.org/abs/2309.12928v1
• Date: Fri, 22 Sep 2023 15:27:54 GMT
• Title: BayesDLL: Bayesian Deep Learning Library
• Authors: Minyoung Kim, Timothy Hospedales
• Abstract summary: We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our library implements mainstream inference algorithms: variational inference, MC-dropout, approximate-gradient MCMC, and Laplace approximation.
• Score: 29.624531252627484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.}.

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