101 citations · 409 across the 32 of their papers we have counts for
5 papers · 1 filter
Graph-based Ensemble Machine Learning for Student Performance Prediction
Yinkai Wang, Aowei Ding, Kaiyi Guan +2
Student performance prediction is a critical research problem to understand the students' needs, present proper learning opportunities/resources, and develop the teaching quality.…
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du +4
Group equivariance (e.g. SE(3) equivariance) is a critical physical symmetry in science, from classical and quantum physics to computational biology. It enables robust and accurate…
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Ziming Liu, Yunyue Chen, Yuanqi Du +1
Integrating physical inductive biases into machine learning can improve model generalizability. We generalize the successful paradigm of physics-informed learning (PIL) into a more…
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image
Yuanqi Du, Quan Quan, Hu Han +1
Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e.g., with lesions), is critical in many perspectives, from lesion detection…
A Survey on Graph Structure Learning: Progress and Opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang +5
Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensit…