A multi-channel masked discrete diffusion model generates semantic mobility skeletons faster than two-stage diffusion baselines while strongly matching temporal length and interval distributions.
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2 Pith papers cite this work, alongside 36 external citations. Polarity classification is still indexing.
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Empirical comparison of Outlierness, Diversity, Representativeness, Uncertainty, and Random selection for trajectory data augmentation across four datasets shows conditional gains in stability over random baselines but degradation in dense data.
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MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
A multi-channel masked discrete diffusion model generates semantic mobility skeletons faster than two-stage diffusion baselines while strongly matching temporal length and interval distributions.
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A Systematic Approach for Selecting Trajectories for Data Augmentation
Empirical comparison of Outlierness, Diversity, Representativeness, Uncertainty, and Random selection for trajectory data augmentation across four datasets shows conditional gains in stability over random baselines but degradation in dense data.