# Deep Fourier Kernel for Self-Attentive Point Processes

arXiv: https://arxiv.org/abs/2002.07281 · HTML: https://mlsanity.com/paper/2002.07281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes](https://mlsanity.com/paper/2101.11179/) (2021)

## Abstract (arXiv)

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.