Helmholtz AI Conference 2024 Prologue Day

Tutorial: Uncertainty Quantification of ML models

Sebastian Starke¹, Steve Schmerler¹, Peter Steinbach¹, Gianni Franchi²

¹ HZDR, Helmholtz AI
² ENSTA Paris

 

When: 13:15 - 18:00
Where: JSC Rotunde, building 16.4, room 301 [coordinates]

 

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.

Schedule

• 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

Requirements

Each beginner is expected to bring their laptop with a working python interpreter (at best python 3.10 or 3.11).

 

Latest details on github [link]