In this episode, I caught up with Nils Lehmann to learn about Uncertainty Quantification for Neural Networks. The conversation begins with a discussion on Bayesian neural networks and their ability to quantify the uncertainty of their predictions. Unlike regular deterministic neural networks, Bayesian neural networks offer a more principled method for providing predictions with a measure of confidence.

Nils then introduces the Pytorch Lightning UQ Box project on GitHub, a tool that enables experimentation with a variety of Uncertainty Quantification (UQ) techniques for neural networks. Model interpretability is a crucial topic, essential for providing transparency to end users of machine learning models. The video of this conversation is also available on YouTube here

* Nils’s website

* Lightning UQ box on Github

* Further reading: A survey of uncertainty in deep neural networks

Bio: Nils Lehmann is a PhD Student at the Technical University of Munich (TUM), supervised by Jonathan Bamber and Xiaoxiang Zhu, working on uncertainty quantification for sea-level rise. More broadly his interests lie in Bayesian Deep Learning, uncertainty quantification and generative modelling for Earth Observational data. He is also passionate about open-source software contributions and a maintainer of the Torchgeo package.

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