91 citations · 137 across the 22 of their papers we have counts for
7 papers · 1 filter
On the Limit of Explaining Black-box Temporal Graph Neural Networks
Minh N. Vu, My T. Thai
Temporal Graph Neural Network (TGNN) has been receiving a lot of attention recently due to its capability in modeling time-evolving graph-related tasks. Similar to Graph Neural Net…
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Khang Tran, Phung Lai, NhatHai Phan +5
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph dat…
EMaP: Explainable AI with Manifold-based Perturbations
Minh N. Vu, Huy Q. Mai, My T. Thai
In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. Th…
NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee
Minh N. Vu, Truc D. Nguyen, My T. Thai
Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions o…
Continual Learning with Differential Privacy
Pradnya Desai, Phung Lai, NhatHai Phan +1
In this paper, we focus on preserving differential privacy (DP) in continual learning (CL), in which we train ML models to learn a sequence of new tasks while memorizing previous t…
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Minh N. Vu, My T. Thai
In Graph Neural Networks (GNNs), the graph structure is incorporated into the learning of node representations. This complex structure makes explaining GNNs' predictions become muc…