45 citations · 109 across the 19 of their papers we have counts for
10 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…
Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles
Martin Zimmermann, Minh Nhat Vu, Florian Beck +2
This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques su…
Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators
Minh Nhat Vu, Florian Beck, Michael Schwegel +3
Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. M…
Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators
Florian Beck, Minh Nhat Vu, Christian Hartl-Nesic +1
This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kin…
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…
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning
Minh Vu, Yuki Akiyama, Konstantinos Slavakis
This study addresses the problem of selecting dynamically, at each time instance, 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…
Sampling-Based Trajectory (re)planning for Differentially Flat Systems: Application to a 3D Gantry Crane
Minh Nhat Vu, Michael Schwegel, Christian Hartl-Nesic +1
In this paper, a sampling-based trajectory planning algorithm for a laboratory-scale 3D gantry crane in an environment with static obstacles and subject to bounds on the velocity a…