Recent advancements in generative modeling have made it possible to generate high-quality content from context information, but a key question remains: how to teach models to know when to generate content? To answer this question, this study proposes a novel event generative model that draws its statistical intuition from marked temporal point processes, and offers a clean, flexible, and computationally efficient solution for a wide range of applications involving multi-dimensional marks. We aim to capture the distribution of the point process without explicitly specifying the conditional intensity or probability density. Instead, we use a conditional generator that takes the history of events as input and generates the high-quality subsequent event that is likely to occur given the prior observations. The proposed framework offers a host of benefits, including exceptional efficiency in learning the model and generating samples, as well as considerable representational power to capture intricate dynamics in multi- or even high-dimensional event space. Our numerical results demonstrate superior performance compared to other state-of-the-art baselines.

翻译：近期生成建模领域的进展使得从上下文信息生成高质量内容成为可能，但一个核心问题依然存在：如何教会模型判断"何时"生成内容？为解答该问题，本研究提出一种新型事件生成模型，其统计直觉源于标记时点过程，并为涉及多维标记的广泛场景提供了简洁、灵活且计算高效的解决方案。我们旨在不显式指定条件强度或概率密度的情况下捕捉点过程分布，转而采用以历史事件序列为输入的条件生成器，生成基于先验观测最可能发生的后续高质量事件。该框架具备诸多优势：在模型学习与样本生成过程中展现出卓越效率，同时具备强大表征能力以捕捉多维乃至高维事件空间中的复杂动态。数值实验结果表明，该方法较其他前沿基准模型具有更优性能。