Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Point Process for Learning Spatiotemporal Event Dynamics   Learning the dynamics of spatiotemporal events is a fundamental problem.
Neural point processes enhance the expressivity of point process models with
deep neural networks. However, most existing methods only consider temporal
dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process
(DeepSTPP), a deep dynamics model that integrates spatiotemporal point
processes. Our method is flexible, efficient, and can accurately forecast
irregularly sampled events over space and time. The key construction of our
approach is the nonparametric space-time intensity function, governed by a
latent process. The intensity function enjoys closed-form integration for the
density. The latent process captures the uncertainty of the event sequence. We
use amortized variational inference to infer the latent process with deep
networks. Using synthetic datasets, we validate our model can accurately learn
the true intensity function. On real-world benchmark datasets, our model
demonstrates superior performance over state-of-the-art baselines.

33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics