Search Results for author:
Found 5 papers, 0 papers with code
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix.
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations.
Exploring Multi-dimensional Data via Subset Embedding
The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings.
Quality Control of Neuron Reconstruction Based on Deep Learning
To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper.
Urban flows prediction from spatial-temporal data using machine learning: A survey
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc.
