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Uncertainty Quantification in AI

Talk von Dr. Florian Wilhelm und Simon Bachstein auf der PyCon/PyData Berlin am 10.10.2019


„Are you sure about that?! Uncertainty Quantification in AI“

With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.

In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.

Event: PyCon/PyData Berlin 2019

Datum: 10.10.2019

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Hans-Peter Zorn

Head of Artificial Intelligence