Open psteinb opened 5 months ago
Thanks for this contribution @psteinb! Fits the overall programme very good! I'm happy to help setting it up. Do you have an (initial) idea about the number of people you can handle? Do you plan an open call to get the speakers or will you invite specific experts?
So, the number of people depends on how well we advertise the workshop. ;-) Jokes aside, we will be 5 people supporting the workshop. So anything up to 40 people is doable from my point of view. But my honest estimate would be 20 participants.
We have already a tentative agenda and 2 external speakers. Should I drop everything here?
@psteinb yes, please update the agenda here.
All material is being shared on the workshop page: https://events.hifis.net/event/1403/contributions/10545/
Uncertainty Quantification of ML models: From Introduction to Advanced
Responsible person(s)
Sebastian Starke, , HZDR, Steve Schmerler, HZDR, @elcorto Peter Steinbach, HZDR, @psteinb
Gianni Franchi, ENSTA Paris, @giannifranchi Olivier Laurent ENSTA Paris and Paris Saclay University, @olaurent
Format
Tutorial and Workshop
Timeframe
torch_uncertainty
(45min intro + 45min hands on) -> 120'Description
In this tutorial, we will give a hands-on introduction to uncertainty quantification for ML models. We will focus on MCDropout and DeepEnsembles as the traditional methods used in the field in the beginning. We will then turn to more advanced topics like Bayesian Neural Networks and accelerated Deep Ensembles. We are super happy to received support by the
torch_uncertainty
team. The workshop itself will offer a mixture of teaching presentations and exploratory exercises using local or remote notebooks. We are planning enough time for all participants to ask questions.Requirements
Each beginner is expected to bring their laptop with a working python interpreter (at best python 3.10 or 3.11).