Speaker
Lynton Ardizzone
(Heidelberg)
Description
Invertible Neural Networks (INNs) are an extremely versatile class of generative models. Their invertibility allows for exact modelling of proability densities, computation of information-theoretic quanities, interpretable and disentangled features, among other things. Due to these properties, INNs have seen growing adoption in recent years, especially in natural sciences and engineering disciplines. In this talk, we present a number of examples for successful applications of INN-specific methods to real-world problems, covering various scientific fields beyond particle physics.
