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.

本文介绍了一种名为神经关系推断（NRI）模型的非监督学习算法，该模型可以在学习动态过程的同时推断系统组成部分之间的相互作用，通过真实场景的一系列实验表明该模型可以准确地预测从运动捕捉和运动追踪数据中推断出的社交网络组成部分之间的关系和运动的复杂动态。

交互系统的神经关系推断