EricssonResearch/illia

Framework agnostic Bayesian Neural Network library.

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Emerging

This library helps deep learning practitioners build models that not only make predictions but also provide a measure of confidence or uncertainty about those predictions. It takes deep learning model architectures, such as convolutional layers, and modifies them to quantify the reliability of their outputs. This is useful for machine learning engineers, data scientists, and researchers working on critical applications where understanding prediction certainty is as important as the prediction itself.

Use this if you are a deep learning engineer or researcher needing to incorporate uncertainty quantification into your models, especially in fields like telecommunications or medicine where prediction reliability is paramount.

Not ideal if you are a business user or practitioner without a strong background in deep learning model development and Python programming.

deep learning uncertainty quantification machine learning engineering predictive modeling model reliability

No Package No Dependents

Maintenance 13 / 25

Adoption 4 / 25

Maturity 16 / 25

Community 9 / 25

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Language

Python

License

MIT

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