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HOW TO BUILD AND EXPLORE MODELS? XIPLOT
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Explainable and Robust AI for VILMA Virtual Laboratory
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- 1 Intro
- 2 Aim: understand which gases form completel new particles in the atmosphere
- 3 Challenge 1: molecular level modelling works, but wo require calculations longer than the age of the univers
- 4 Challenge 2: difficulties and biases in detectio relevant chemical species and molecular clust
- 5 VILMA VIRTUAL LABORATORY: AI FOR SCIENCE
- 6 CAN WE TRUST OUR PREDICTIONS? DETECTING CONCEPT DRIFT
- 7 HOW TO BUILD AND EXPLORE MODELS? XIPLOT
- 8 CRASH INTRO TO EXPLAINABLE AI (XAI): GLOBAL VS LOCAL EXPLANATIONS
- 9 MOTIVATION: LOCAL EXPLANATIONS
- 10 MOTIVATION: DIMENSIONALITY REDUCTI - AS A TOOL FOR SCIENCE
- 11 HOW DO THE MACHINE-LEARNING MODELS W SLISEMAP FOR EXPLAINABLE AI (XAI)
- 12 SLISEMAP: RANDOM FOREST PREDICTIN FUEL CONSUMPTION OF CARS
- 13 SLISEMAP: THE EFFECT OF RADIUS
- 14 SLISEMAP: MULTIPLE EXPLANATIONS
- 15 SLISEMAP: SUBSAMPLING
- 16 SLISEMAP: USAGE
- 17 SLISEMAP: SUMMARY
- 18 SLISEMAP: PROBLEM DEFINITION
