Factorised Neural Relational Inference for Multi-Interaction Systems

Understand

Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles.

• A number of architectures have been developed to articulate this kind of structure, both implicitly and explicitly.
• We consider an unsupervised explicit model, the NRI model, and make a series of representational adaptations and physically motivated changes.
• Most notably we factorise the inferred latent interaction graph into a multiplex graph, allowing each layer to encode for a different interaction-type.