FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within each.

Apr 30, 2020 · FastPoint's key computational advantage is that it eliminates the need to compute K-many terms at each point for likelihood-based estimation.

FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within each ...

FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within ...

We propose FastPoint, a novel multivariate point process that enables fast and accurate learning and inference. FastPoint uses deep recurrent neural ...

We propose FastPoint, a novel multivariate point process that enables fast and accurate learning and inference. FastPoint uses deep recurrent neural ...

Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While.

Awesome Track of developments in Temporal Point Processes (TPPs). Table of Contents Lecture notes, tutorials and blogs.

Deep mixture point processes: Spatio-temporal event prediction with rich contextual information. ... FastPoint: Scalable deep point processes. In ECML PKDD, 2019.

Neural point process (NPP) is a family of deep generative models that integrate deep neural networks. (DNN) with point processes for modeling irregularly ...