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

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

DOI

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.

OriginalsprogEngelsk
Artikelnummer100431
TidsskriftSoftware Impacts
Vol/bind14
DOI
StatusUdgivet - nov. 2022

Bibliografisk note

Funding Information:
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.

Funding Information:
This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.

Publisher Copyright:
© 2022 The Author(s)

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