Abstract
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.
Original language | English |
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Title of host publication | 35th International Conference on Machine Learning, ICML 2018 |
Editors | Jennifer Dy, Andreas Krause |
Publisher | International Machine Learning Society (IMLS) |
Pages | 4209-4225 |
Number of pages | 17 |
ISBN (Electronic) | 9781510867963 |
State | Published - 2018 |
Externally published | Yes |
Event | 35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden Duration: 10 Jul 2018 → 15 Jul 2018 |
Publication series
Name | 35th International Conference on Machine Learning, ICML 2018 |
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Volume | 6 |
Conference
Conference | 35th International Conference on Machine Learning, ICML 2018 |
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Country/Territory | Sweden |
City | Stockholm |
Period | 10/07/18 → 15/07/18 |
Bibliographical note
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