26-28 November, 2019, Vilnius

Conference is over! See you next year.

Florian Wilhelm

inovex GmbH, Germany


Florian is a Data Scientist living in Cologne, Germany with a mathematical background. After his postdoctoral position, he started as a Data Scientist at Blue Yonder (now JDA), the leading platform provider for Predictive Applications and Big Data in the European market.
Right now he enjoys working on innovative Data Science projects with experts every day at inovex.
With more than five years of project experience in the field of Predictive & Prescriptive Analytics and Big Data, he has acquired profound knowledge in the domains of mathematical modelling, statistics, machine learning, high-performance computing and data mining. For the last years Florian programmed mostly with the Python Data Science stack (NumPy, SciPy, Scikit-Learn, Pandas, Matplotlib, Jupyter, etc.) to which he also contributed several extensions.


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.

Session Keywords

Uncertainty Quantification
Deep Learning
Gaussian Processes
Bayesian Methods