Long-Term Vessel Trajectory Imputation with Physics-Guided Diffusion Probabilistic Model-Reference-Cited by-同舟云学术

Long-Term Vessel Trajectory Imputation with Physics-Guided Diffusion Probabilistic Model

Published:2024-08-24 Issue: Volume:34 Page:4398-4407
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Zhiwen¹^ORCID,Fan Zipei¹^ORCID,Lv Zewu¹^ORCID,Song Xuan²^ORCID,Shibasaki Ryosuke³^ORCID

Affiliation:

1. School of Artificial Intelligence, Jilin University, Changchun, China

2. School of Artificial Intelligence, Jilin University & Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology (SUSTech), Changchun, China

3. Research & Development Department, LocationMind Inc., Tokyo, Japan

Funder

Jilin Provincial International Cooperation Key Laboratory for Super Smart City and Zhujiang Project

National Key Research and Development Project of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672086

Reference40 articles.

1. Inferring Uncertain Trajectories from Partial Observations

2. TERI: An Effective Framework for Trajectory Recovery with Irregular Time Intervals

3. RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

4. Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).

5. Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio. 2015. A recurrent latent variable model for sequential data. Advances in neural information processing systems, Vol. 28 (2015).