4 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…
eess.IV2024
Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images
Ruiwen Ding, Kha-Dinh Luong, Erika Rodriguez +2
In computational pathology, extracting spatial features from gigapixel whole slide images (WSIs) is a fundamental task, but due to their large size, WSIs are typically segmented in…
cs.LG2023★ 4 cited
Fragment-based Pretraining and Finetuning on Molecular Graphs
Kha-Dinh Luong, Ambuj Singh
Property prediction on molecular graphs is an important application of Graph Neural Networks. Recently, unlabeled molecular data has become abundant, which facilitates the rapid de…