The generative paradigm has become increasingly important in machine learning
and deep learning models. Among popular generative models are normalizing
flows, which enable exact likelihood estimation by transforming a base
distribution through diffeomorphic transformations. Extending t
Flow-based deep generative models can be used for novelty detection in time series data and outperform traditional methods like the Local Outlier Factor.