Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historical impacts. While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations. Recently, deep generative models such as denoising diffusion and score matching models have achieved great progress in image generating tasks by demonstrating their capability of generating samples of high quality. However, there are no complete and unified works exploring and studying the potential of generative models in the context of event occurence modeling for TPP. In this work, we try to fill the gap by designing a unified \textbf{g}enerative framework for \textbf{n}eural \textbf{t}emporal \textbf{p}oint \textbf{p}rocess (\textsc{GNTPP}) model to explore their feasibility and effectiveness, and further improve models' predictive performance. Besides, in terms of measuring the historical impacts, we revise the attentive models which summarize influence from historical events with an adaptive reweighting term considering events' type relation and time intervals. Extensive experiments have been conducted to illustrate the improved predictive capability of \textsc{GNTPP} with a line of generative probabilistic decoders, and performance gain from the revised attention. To the best of our knowledge, this is the first work that adapts generative models in a complete unified framework and studies their effectiveness in the context of TPP. Our codebase including all the methods given in Section.5.1.1 is open in \url{https://github.com/BIRD-TAO/GNTPP}. We hope the code framework can facilitate future research in Neural TPPs.
翻译:热点进程( TPP) 通常用于模拟以历史影响为条件的概率模型所揭示的、 发生时间印记的零星事件序列 。 虽然许多先前的工程都以TPP 模型的“ 良好性能” 为主, 但其预测性能并不令人满意, 这意味着模型生成的时间标记与真实观察相去甚远。 最近, 深层的基因化模型, 如分解的传播和得分匹配模型, 通过展示其生成高质量样本的能力, 在图像生成任务方面取得了巨大的进步。 然而, 没有完整和统一的工程, 探索和研究基因化模型在为TPP 建模 的情况下的“ 良好性能 ” 。 在这项工作中, 我们试图通过设计一个统一的 textbf{ MP{ n} 来填补差距, 这意味着模型生成的时间性标定值框架 。 我们给出的troupbf fp} text/ textfleoplefration flationf} 成功度框架(\ textc) 。 但是, 还没有一个完整和最佳的网络化网络化的模型, 来测量其真实性模型, 的模型的模型是用来测量性能和预测性关系。