<ul><li>Spatio-temporal point processes (STPPs) are used to model discrete events distributed in time and space.</li><li>Traditional models often rely on parametric kernels, limiting their ability to capture heterogeneous, nonstationary dynamics.</li><li>Recent innovations integrate deep neural architectures to model the conditional intensity function or learn flexible, data-driven influence kernels.</li><li>The article discusses the development of the deep influence kernel approach, its components, applications in crime analysis, earthquake aftershock prediction, and sepsis prediction modeling, and promising directions for the field.</li></ul>

Deep spatio-temporal point processes: Advances and new directions

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