Shaoxuan Gu — accepted AI-conference papers

2026

Self-Supervised Cross-City Trajectory Representation Learning Based on Meta-Learning

Yanwei Yu, Hong Xia, Shaoxuan Gu, Xingyu Zhao, Dongliang Chen, Yuan Cao

AAAI 2026technical

Trajectory representation learning transforms complex spatio-temporal features of trajectories into dense, low-dimensional embeddings, enabling applications in intelligent transportation systems. With advances in this field and the availability of large-scale traffic data, intelligent urban systems

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2026

Towards Efficient and Effective Unimodal Trajectory Representation Learning: A Simple Yet Powerful Approach

Shaoxuan Gu, Xingyu Zhao, Dongliang Chen, Yuan Cao, Yanwei Yu

IJCAI 2026

Trajectory representation learning transforms trajectory data into low-dimensional embeddings for downstream analytics. Although trajectory data inherently contains rich spatiotemporal information that remains to be more deeply explored, recent approaches have increasingly favored integrating extern

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