[1802.04687] Neural Relational Inference for Interacting Systems

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.

1 mentions: @shion_honda
Date: 2019/06/08 03:47

Referring Tweets

@shion_honda Neural Relational Inference [Kipf+, 2018, ICML] システムにおける物理/社会的相互作用をGNNベースのVAEで推定する研究。潜在変数zは各ノード間のエッジタイプを表す。物理シミュレーション、モーションキャプチャ、スポーツトラッキングのデータで実験した。 https://t.co/JCKDEXeQlr #NowReading https://t.co/j5ytlVeS97