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Found 4 papers, 2 papers with code
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework.
Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph.
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns.
Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network
The primary goal of skeletal motion prediction is to generate future motion by observing a sequence of 3D skeletons.
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