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Deep Fourier Kernel for Self-Attentive Point Processes
[article]
2021
arXiv
pre-print
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.
arXiv:2002.07281v5
fatcat:ubmconvmdvgt3knvc3ehbchm7i