论文标题
神经时空点过程
Neural Spatio-Temporal Point Processes
论文作者
论文摘要
我们为时空点过程提出了一类新的参数化，该过程利用神经ODE作为计算方法，并启用在连续时间和空间中定位的离散事件的灵活，高保真模型。我们方法的核心是连续时间神经网络与两个新型神经体系结构的结合，即跳跃和细心的连续时间正常化流量。这种方法使我们能够为空间和时间域学习复杂的分布，并在观察到的事件历史记录上非平凡地条件。我们从各种环境（例如地震学，流行病学，城市流动性和神经科学）等各种环境中验证了模型。
We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of continuous-time neural networks with two novel neural architectures, i.e., Jump and Attentive Continuous-time Normalizing Flows. This approach allows us to learn complex distributions for both the spatial and temporal domain and to condition non-trivially on the observed event history. We validate our models on data sets from a wide variety of contexts such as seismology, epidemiology, urban mobility, and neuroscience.
