AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting

29 January 2021

Papers citing "AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting"

7 / 7 papers shown

Title
$Variational inference of fractional Brownian motion with linear computational complexity$ Variational inference of fractional Brownian motion with linear computational complexity Hippolyte Verdier Franccois Laurent Alhassan Cassé Christian L. Vestergaard Jean-Baptiste Masson 165 6 0 15 Mar 2022
A Comprehensive Survey on Graph Neural Networks Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang Philip S. Yu FaML GNN AI4TS AI4CE 778 8,533 0 03 Jan 2019
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting Ting Yu Haoteng Yin Zhanxing Zhu GNN AI4TS 115 3,715 0 14 Sep 2017
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks Junbo Zhang Yu Zheng Dekang Qi Ruiyuan Li Xiuwen Yi Tianrui Li GNN 3DPC HAI AI4TS 47 434 0 10 Jan 2017
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework Yuankai Wu Huachun Tan AI4TS 68 251 0 03 Dec 2016
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 641 29,076 0 09 Sep 2016
Sequence to Sequence Learning with Neural Networks Ilya Sutskever Oriol Vinyals Quoc V. Le AIMat 437 20,568 0 10 Sep 2014