[R] Add and Thin: Diffusion for Temporal Point Processes

Paper: [https://arxiv.org/abs/2311.01139](https://arxiv.org/abs/2311.01139)Code: [https://github.com/davecasp/add-thin](https://github.com/davecasp/add-thin)

https://preview.redd.it/ydhlwfte9h5c1.jpg?width=2544&format=pjpg&auto=webp&s=1c11a4a1f8cab2626e46546d589c35afb5e02fea

Generative diffusion models are all the rage, but it is unclear how they could be applied to sequences of varying numbers of events, e.g. tweets, reddit comments or taxi trips. We present a diffusion approach that turns any sequence into a noise sequence, a sample from a homogeneous Poisson process. Conversely, our model transforms such noise sequence samples iteratively into samples from any target data distribution by deleting events that it classifies as noise and proposing …

diffusion diffusion models events generative machinelearning noise numbers process reddit sample tweets