4 citations · 7 across the 3 of their papers we have counts for
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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…
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…