Yuxuan Liang
Yuxuan Liang
Assistant Professor, Hong Kong University of Science and Technology (Guangzhou)
Verified email at - Homepage
Cited by
Cited by
Geoman: Multi-level attention networks for geo-sensory time series prediction.
Y Liang, S Ke, J Zhang, X Yi, Y Zheng
IJCAI 2018, 3428-3434, 2018
Urban traffic prediction from spatio-temporal data using deep meta learning
Z Pan, Y Liang, W Wang, Y Yu, Y Zheng, J Zhang
KDD 2019, 1720-1730, 2019
Urban water quality prediction based on multi-task multi-view learning
Y Liu, Y Zheng, Y Liang, S Liu, DS Rosenblum
IJCAI 2016, 2016
Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks
J Sun, J Zhang, Q Li, X Yi, Y Liang, Y Zheng
IEEE Transactions on Knowledge and Data Engineering 34 (5), 2348-2359, 2020
Federated forest
Y Liu, Y Liu, Z Liu, Y Liang, C Meng, J Zhang, Y Zheng
IEEE Transactions on Big Data 8 (3), 843-854, 2020
Mixup for node and graph classification
Y Wang, W Wang, Y Liang, Y Cai, B Hooi
WWW 2021, 3663-3674, 2021
Nodeaug: Semi-supervised node classification with data augmentation
Y Wang, W Wang, Y Liang, Y Cai, J Liu, B Hooi
KDD 2020, 207-217, 2020
Spatio-temporal meta learning for urban traffic prediction
Z Pan, W Zhang, Y Liang, W Zhang, Y Yu, J Zhang, Y Zheng
IEEE Transactions on Knowledge and Data Engineering 34 (3), 1462-1476, 2020
Urbanfm: Inferring fine-grained urban flows
Y Liang, K Ouyang, L Jing, S Ruan, Y Liu, J Zhang, DS Rosenblum, ...
KDD 2019, 3132-3142, 2019
Time-llm: Time series forecasting by reprogramming large language models
M Jin, S Wang, L Ma, Z Chu, JY Zhang, X Shi, PY Chen, Y Liang, YF Li, ...
ICLR 2024, 2024
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
G Jin, Y Liang, Y Fang, Z Shao, J Huang, J Zhang, Y Zheng
IEEE Transactions on Knowledge and Data Engineering, 2023
Directed graph convolutional network
Z Tong, Y Liang, C Sun, DS Rosenblum, A Lim
arXiv preprint arXiv:2004.13970, 2020
Autost: Efficient neural architecture search for spatio-temporal prediction
T Li, J Zhang, K Bao, Y Liang, Y Li, Y Zheng
KDD 2020, 794-802, 2020
Digraph inception convolutional networks
Z Tong, Y Liang, C Sun, X Li, D Rosenblum, A Lim
NeurIPS 2020 33, 17907-17918, 2020
AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph✱
Z Pan, S Ke, X Yang, Y Liang, Y Yu, J Zhang, Y Zheng
WWW 2021, 1846-1855, 2021
Fine-grained urban flow prediction
Y Liang, K Ouyang, J Sun, Y Wang, J Zhang, Y Zheng, D Rosenblum, ...
WWW 2021, 1833-1845, 2021
Multi-behavior hypergraph-enhanced transformer for sequential recommendation
Y Yang, C Huang, L Xia, Y Liang, Y Yu, C Li
KDD 2022, 2263-2274, 2022
Learning to generate maps from trajectories
S Ruan, C Long, J Bao, C Li, Z Yu, R Li, Y Liang, T He, Y Zheng
AAAI 2020 34 (01), 890-897, 2020
When do contrastive learning signals help spatio-temporal graph forecasting?
X Liu, Y Liang, C Huang, Y Zheng, B Hooi, R Zimmermann
ACM SIGSPATIAL 2022, 1-12, 2022
Directed graph contrastive learning
Z Tong, Y Liang, H Ding, Y Dai, X Li, C Wang
NeurIPS 2021 34, 19580-19593, 2021
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