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Many complex natural and cultural phenomena are well modelled by systems ofsimple interactions between particles. A number of architectures have beendeveloped to articulate this kind of structure, both implicitly and explicitly.We consider an unsupervised explicit model, the NRI model, and make a series ofrepresentational adaptations and physically motivated changes. Most notably wefactorise the inferred latent interaction graph into a multiplex graph,allowing each layer to encode for a different interaction-type. This fNRI modelis smaller in size and significantly outperforms the original in both edge andtrajectory prediction, establishing a new state-of-the-art. We also present asimplified variant of our model, which demonstrates the NRI's formulation as avariational auto-encoder is not necessary for good performance, and make anadaptation to the NRI's training routine, significantly improving its abilityto model complex physical dynamical systems.

Factorised Neural Relational Inference for Multi-Interaction Systems