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Found 2 papers, 1 papers with code
Dynamic Causal Graph Convolutional Network for Traffic Prediction
In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data.
Ranked #13 on
Traffic Prediction
on METR-LA
Hybrid Channel Based Pedestrian Detection
When using a more advanced RPN in our framework, our approach can be further improved and get competitive results on both benchmarks.
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