# Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes

Source: [Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes](<https://www.sota2.com/research/paper/68fa70cb1e209131cea4d309>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:38:58.164Z

Authors: Yixuan Zhang, Quyu Kong, Feng Zhou

Published: 2023-10-09T07:44:37.000Z

## Abstract

In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP is an enhanced version of Deep Mixture Point Processes (DMPP), which uses a more flexible deep kernel to model complex relationships between events and covariate data, improving the model's expressiveness. To address the intractable training procedure of DKMPP due to the non-integrable deep kernel, we utilize an integration-free method based on score matching, and further improve efficiency by adopting a scalable denoising score matching method. Our experiments demonstrate that DKMPP and its corresponding score-based estimators outperform baseline models, showcasing the advantages of incorporating covariate information, utilizing a deep kernel, and employing score-based estimators.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Spatio-temporal event modeling on NYC Complaint Data](<https://www.sota2.com/research/sota/spatio-temporal-event-modeling-on-nyc-complaint-data.md>): Spatio-temporal event modeling; NYC Complaint Data; TLL: 4.28.

- [Spatio-temporal event modeling on NYC Vehicle Collisions](<https://www.sota2.com/research/sota/spatio-temporal-event-modeling-on-nyc-vehicle-collisions.md>): Spatio-temporal event modeling; NYC Vehicle Collisions; TLL: 400.32.

- [Spatio-temporal Event Modeling on Crimes in Vancouver](<https://www.sota2.com/research/sota/spatio-temporal-event-modeling-on-crimes-in-vancouver.md>): Spatio-temporal event modeling; Crimes in Vancouver; TLL: 67.03.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.05485>)

[PDF](<https://arxiv.org/pdf/2310.05485.pdf>)