Locality Aware Temporal FMs for Crime Prediction-Reference-Cited by-同舟云学术

Locality Aware Temporal FMs for Crime Prediction

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
language:
Short-container-title:

Author:

Mansha Sameen¹,Rehman Abdur²,Abdullah Shaaf²,Kamiran Faisal³,Yin Hongzhi⁴

Affiliation:

1. KTH Royal Institute of Technology, Stockholm, Sweden

2. Information Technology University of the Punjab, Lahore, Pakistan

3. Information Technology University of the Punjab,, Lahore, Pakistan

4. The University of Queensland, Brisbane, QLD, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557657

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4. [n.d.]. NYPD complaint Data HISTORIC. https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Historic/qgea-i56i [Online ; accessed 2022 -08--18]. [n.d.]. NYPD complaint Data HISTORIC. https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Historic/qgea-i56i [Online; accessed 2022-08--18].

5. Sam Gross Adam Paszke etal 2019. PyTorch: An Imperative Style High-Performance Deep Learning Library. In Proceedins of NIPS. 8024--8035. Sam Gross Adam Paszke et al. 2019. PyTorch: An Imperative Style High-Performance Deep Learning Library. In Proceedins of NIPS. 8024--8035.

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