Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations-Reference-Cited by-同舟云学术

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Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations

Published:2023-12-01 Issue: Volume:33 Page:150-159
ISSN:
Container-title:2023 IEEE International Conference on Data Mining (ICDM)
language:
Short-container-title:

Author:

Hu Mingzhi¹,Zhong Zhuoyun¹,Zhang Xin²,Li Yanhua¹,Xie Yiqun³,Jia Xiaowei⁴,Zhou Xun⁵,Luo Jun⁶

Affiliation:

1. Worcester Polytechnic Institute

2. San Diego State University

3. University of Maryland

4. University of Pittsburgh

5. University of Iowa

6. Logistics and Supply Chain MultiTech R&D Centre,Hong Kong

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10415628/10415561/10415669.pdf?arnumber=10415669

Reference55 articles.

1. Driver identification using automobile sensor data from a single turn

2. Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

3. Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories

4. Sequential Anomaly Detection using Inverse Reinforcement Learning

5. ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification

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