PyVarInf
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
What is PyVarInf?
PyVarInf is a Python package that facilitates Bayesian deep learning by enabling variational inference for PyTorch neural network models. It allows users to train models that output posterior distributions over weights rather than point estimates, providing uncertainty quantification and enabling Bayesian model averaging. The library transforms standard PyTorch models into variational versions with Gaussian posteriors through a simple API.
Machine learning researchers and practitioners working with PyTorch who want to incorporate Bayesian methods into their deep learning pipelines, particularly those interested in uncertainty estimation, model selection, or information-theoretic approaches to neural network training.
PyVarInf provides a straightforward way to apply variational inference to existing PyTorch models without requiring extensive modifications, offering multiple prior distributions and seamless integration with PyTorch's optimization ecosystem. It bridges the gap between practical deep learning and Bayesian methodology with an accessible implementation.
Overview
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
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