42 citations · 75 across the 9 of their papers we have counts for
5 papers · 1 filter
Instructor-inspired Machine Learning for Robust Molecular Property Prediction
Fang Wu, Shuting Jin, Siyuan Li +1
Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical da…
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Fang Wu, Siyuan Li, Xurui Jin +4
The success of graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant of the prediction?'' Particularl…
Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks
Fang Wu, Lirong Wu, Dragomir Radev +2
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. H…
Discovering the Representation Bottleneck of Graph Neural Networks
Fang Wu, Siyuan Li, Stan Z. Li
Graph neural networks (GNNs) rely mainly on the message-passing paradigm to propagate node features and build interactions, and different graph learning problems require different…
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
Fang Wu, Nicolas Courty, Shuting Jin +1
Training data are usually limited or heterogeneous in many chemical and biological applications. Existing machine learning models for chemistry and materials science fail to consid…