45 citations · 66 across the 17 of their papers we have counts for
8 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…
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering
Yuki Akiyama, Minh Vu, Konstantinos Slavakis
This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on th…
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…
An Explainer for Temporal Graph Neural Networks
Wenchong He, Minh N. Vu, Zhe Jiang +1
Temporal graph neural networks (TGNNs) have been widely used for modeling time-evolving graph-related tasks due to their ability to capture both graph topology dependency and non-l…
Learning Interpretation with Explainable Knowledge Distillation
Raed Alharbi, Minh N. Vu, My T. Thai
Knowledge Distillation (KD) has been considered as a key solution in model compression and acceleration in recent years. In KD, a small student model is generally trained from a la…