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Found 3 papers, 2 papers with code
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction
However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i. e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush.
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition.
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction
Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting.
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