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Scalable Normalizing Flows for Permutation Invariant Densities
Marin Biloš · Stephan Günnemann

Tue Jul 20 06:35 PM -- 06:40 PM (PDT) @ None

Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densities with continuous normalizing flows. This allows us to maximize the likelihood directly and sample new realizations with ease. In this work, we demonstrate how calculating the trace, a crucial step in this method, raises issues that occur both during training and inference, limiting its practicality. We propose an alternative way of defining permutation equivariant transformations that give closed form trace. This leads not only to improvements while training, but also to better final performance. We demonstrate the benefits of our approach on point processes and general set modeling.

Author Information

Marin Biloš (Technical University of Munich)
Stephan Günnemann (Technical University of Munich)

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