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Found 5 papers, 3 papers with code
Are Large Language Models Geospatially Knowledgeable?
Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension of geographic data and related ability to facilitate informed geospatial decision-making.
Disentangled Dynamic Graph Deep Generation
Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns.
Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints
Inspired by the success of deep generative neural networks for images and texts, a fast-developing research topic is deep generative models for trajectory data which can learn expressively explanatory models for sophisticated latent patterns.
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design.
Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization
Urban areas provide us with a treasure trove of available data capturing almost every aspect of a population's life.
