9 citations · 93 across the 42 of their papers we have counts for
43 papers
FTD-GNO: Memory-Efficient Graph Neural Operators through Functional Tensor Decomposition of the Kernel
Xiaomin Zhang, Boyue Wang, Junbin Gao +1
Graph Neural Operators (GNOs) provide flexible surrogate models for learning solution operators of partial differential equations (PDEs). However, standard GNOs typically parameter…
Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment
Qijie Ding, Jie Yin, Daokun Zhang +1
Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. To circumvent the shortage of…
Variational Counterfactual Prediction under Runtime Domain Corruption
Hechuan Wen, Tong Chen, Li Kheng Chai +3
To date, various neural methods have been proposed for causal effect estimation based on observational data, where a default assumption is the same distribution and availability of…
Diffusion Models for Time Series Applications: A Survey
Lequan Lin, Zhengkun Li, Ruikun Li +2
Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research. With a distinguished performan…
Graph Contrastive Learning with Implicit Augmentations
Huidong Liang, Xingjian Du, Bilei Zhu +3
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, alterin…
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Jie Chen, Shouzhen Chen, Mingyuan Bai +3
The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and agg…