9 citations · 28 across the 8 of their papers we have counts for
8 papers
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Liang Zeng, Lanqing Li, Jian Li
How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery. This paper studies this problem and proposes to incorporat…
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation
Liang Zeng, Attila Lengyel, Nergis Tömen +1
In this work, we leverage estimated depth to boost self-supervised contrastive learning for segmentation of urban scenes, where unlabeled videos are readily available for training…
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification
Liang Zeng, Lanqing Li, Ziqi Gao +2
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, th…
Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network
Yuchen Fang, Yanjun Qin, Haiyong Luo +4
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate tempora…
CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting
Yuchen Fang, Yanjun Qin, Haiyong Luo +4
Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-t…
Inductive Matrix Completion Using Graph Autoencoder
Wei Shen, Chuheng Zhang, Yun Tian +4
Recently, the graph neural network (GNN) has shown great power in matrix completion by formulating a rating matrix as a bipartite graph and then predicting the link between the cor…
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling
Liang Zeng, Lei Wang, Hui Niu +3
Price movement forecasting, aimed at predicting financial asset trends based on current market information, has achieved promising advancements through machine learning (ML) method…
Context-Aware Sparse Deep Coordination Graphs
Tonghan Wang, Liang Zeng, Weijun Dong +3
Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning. This paper studies this probl…