论文标题
随机多对象系统的关系状态空间模型
Relational State-Space Model for Stochastic Multi-Object Systems
论文作者
论文摘要
现实世界中的动态系统通常由相互相互作用的多个随机子系统组成。建模和预测这种动态的行为通常并不容易，因为理解其成分的复杂相互作用和演变的固有硬度。本文介绍了关系状态空间模型（R-SSM），这是一种顺序层次的潜在变量模型，该模型使用图形神经网络（GNN）来模拟多个相关对象的关节状态过渡。通过让GNN与SSM合作，R-SSM提供了一种灵活的方式，将关系信息纳入多对象动力学的建模。我们进一步建议通过对顶点索引随机变量实例化的归一化流量来增强模型，并提出了两个辅助对比目标以促进学习。 R-SSM的实用性在综合和实时序列数据集上进行了经验评估。
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.
