6 citations · 6 across the 2 of their papers we have counts for
3 papers
cs.LG2026
DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
Yuxiang Zeng, Kun Xie, Yang Wang +5
Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidates. The key difficulty is th…
cs.LG2023★ 6 cited
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
Ta Jiun Ting, Xiaocan Li, Scott Sanner +1
Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have b…
cs.LG2023
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler +4
Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical v…