Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.
TrajGAT: A graph-based long-term dependency modeling approach for trajectory similarity computation
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
MoCo-AIS is a MoCo-based contrastive learning framework that learns vessel trajectory embeddings and improves similarity computation over baselines on large-scale real-world AIS datasets while offering a benchmarking platform.
TrajTok learns multi-resolution hexagonal spatial tokens from GPS data and pretrains a factorized transformer with ST-RoPE and masked modeling to yield frozen encoders that outperform task-specific methods on similarity, classification, and travel-time tasks in the Porto dataset.
citing papers explorer
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Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
Scoring functions are sub-optimal for all utility-fairness trade-offs in ranking under a generic fairness formulation, but semi-greedy post-processing can approach the performance of exhaustive post-processing.
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MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories
MoCo-AIS is a MoCo-based contrastive learning framework that learns vessel trajectory embeddings and improves similarity computation over baselines on large-scale real-world AIS datasets while offering a benchmarking platform.
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TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning
TrajTok learns multi-resolution hexagonal spatial tokens from GPS data and pretrains a factorized transformer with ST-RoPE and masked modeling to yield frozen encoders that outperform task-specific methods on similarity, classification, and travel-time tasks in the Porto dataset.