论文标题
深度傅立叶内核进行自我牵手过程
Deep Fourier Kernel for Self-Attentive Point Processes
论文作者
论文摘要
我们为离散事件数据提供了一个新型的基于注意力的模型，以捕获复杂的非线性时间依赖性结构。我们从注意机制中借用了这个想法，并将其纳入点过程的条件强度函数。我们进一步使用傅立叶内核嵌入来引入新的得分函数，其频谱使用神经网络表示，该频谱与传统的点 - 产物 - 产物内核有很大不同，并且可以捕获更复杂的相似性结构。我们建立了方法的理论属性，并与合成和真实数据的最先进相比，证明了方法的竞争性能。
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.
