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Variational Neural Networks implementation in Pytorch and JAX[Formula presented]

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Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.

Original languageEnglish
Article number100431
JournalSoftware Impacts
Publication statusPublished - Nov 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

    Research areas

  • Bayesian deep learning, Bayesian Neural Networks, JAX, PyTorch, Uncertainty estimation

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