Uncertainty Quantification of ML models: Hands-on Introduction #8
Description
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
- 13:30h Introduction to Uncertainties by Peter Steinbach
- break
- 15:00h Gianni Franchi et al:
- packed ensembles to extend ensembles (15min intro + 15min hands on) -> 45'
- Bayesian Neural Networks with
torch_uncertainty(45min intro + 45min hands on) -> 120'
- 17:30-18:00h Finish
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).