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Found 5 papers, 5 papers with code
Graph-based Neural Weather Prediction for Limited Area Modeling
The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere.
Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
This research investigates the performance of various motion models in combination with numerical solvers for the prediction task.
Temporal Graph Neural Networks for Irregular Data
Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph.
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior.
Scalable Deep Gaussian Markov Random Fields for General Graphs
We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only.
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