Stabilizing Invertible Neural Networks Using Mixture Models

Reconstructing parameters of physical models is an important task in science. Usually, such problems are severely under determined and sophisticated reconstruction techniques are necessary. Whereas classical regularisation methods focus on finding just the most desirable or most likely solution of an inverse problem, more recent methods focus on analyzing the complete distribution of possible parameters. In particular, this provides us with a way to quantify how trustworthy the obtained solution is. Among the most popular methods for uncertainty quantification are Bayesian methods [16], which build up on evaluating the posterior using Bayes theorem, and Markov Chain Monte Carlo (MCMC) [38].