Relational State-Space Model for Stochastic Multi-Object Systems

01/13/2020
by   Fan Yang, et al.
0

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time-series datasets.

READ FULL TEXT
06/29/2022

Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios

Recurrent State-space models (RSSMs) are highly expressive models for le...
05/24/2022

Ensemble Multi-Relational Graph Neural Networks

It is well established that graph neural networks (GNNs) can be interpre...
05/26/2022

Sparse Graph Learning for Spatiotemporal Time Series

Outstanding achievements of graph neural networks for spatiotemporal tim...
02/02/2019

Graph Neural Networks with Generated Parameters for Relation Extraction

Recently, progress has been made towards improving relational reasoning ...
09/11/2018

Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active r...
09/30/2020

The Role of Isomorphism Classes in Multi-Relational Datasets

Multi-interaction systems abound in nature, from colloidal suspensions t...
06/30/2021

Relational VAE: A Continuous Latent Variable Model for Graph Structured Data

Graph Networks (GNs) enable the fusion of prior knowledge and relational...