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Oral
Neural Relational Inference for Interacting Systems
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel

Wed Jul 11 08:50 AM -- 09:00 AM (PDT) @ A5
in Networks and Relational Learning 2 »

Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often be explained using a simple model of the system's constituent parts. In this work, we introduce the neural relational inference (NRI) model: an unsupervised model that learns to infer interactions while simultaneously learning the dynamics purely from observational data. Our model takes the form of a variational auto-encoder, in which the latent code represents the underlying interaction graph and the reconstruction is based on graph neural networks. In experiments on simulated physical systems, we show that our NRI model can accurately recover ground-truth interactions in an unsupervised manner. We further demonstrate that we can find an interpretable structure and predict complex dynamics in real motion capture and sports tracking data.

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Author Information

Thomas Kipf (University of Amsterdam)
Ethan Fetaya (University of Toronto)
Jackson Wang (Univeristy of Toronto)
Max Welling (University of Amsterdam)
Richard Zemel (Vector Institute)

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