We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures. We borrow the idea from the attention mechanism and incorporate it into the point processes' conditional intensity function. We further introduce a novel score function using Fourier kernel embedding, whose spectrum is represented using neural networks, which drastically differs from the traditional dot-product kernel and can capture a more complex similarity structure. We establish our approach's theoretical properties and demonstrate our approach's competitive performance compared to the state-of-the-art for synthetic and real data.

翻译：我们为离散事件数据提供了一个新的关注模型,以捕捉复杂的非线性时间依赖结构。我们从关注机制中借用这个想法,并将其纳入点处理过程的有条件强度功能。我们进一步引入了一个新的评分功能,使用Fourier内核嵌入器,其频谱代表的是神经网络,它与传统的圆点产品内核有很大不同,可以捕捉更复杂的相似结构。我们建立了我们的方法的理论特性,并展示了我们的方法相对于合成和真实数据的最新技术的竞争性表现。