Author

Christian S. Jensen

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Preprint Aug 2026

Using Lower-Bound Representations for Trajectory Similarity Learning

This work revisits trajectory similarity learning from a lower-bound representation perspective and proposes LB-TrajRep, a unified lower-bound representation framework independent of deep neural embeddings that is able to consistently outperform state-of-the-art neural trajectory embeddings across diverse distance meas...

Liwei Deng, Hao Meng, Yupu Zhang et al. · 0 citations

#artificial intelligence Book Open access Aug 2026

REFINE: Trajectory Representation Learning via Closed-Loop Transcription

This work proposes REFINE, a simple yet effective Representation lEarning Framework that tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually de...

Sean Bin Yang, Ying Sun, Ji-Lin Hu et al. · Proceedings of the 32nd ACM... · 2 citations

#artificial intelligence Conference Open access Sep 2026

DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning

DGCPath is presented, an innovative Distribution-aware Generative Contrastive learning framework for Path representation that establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings.

Sean Bin Yang, Hao Miao, Zong-Yi Xu et al. · Proceedings of the Thirty-Fi... · 1 citation