Zobrazeno 1 - 10
of 352
pro vyhledávání: '"Vu, P. N."'
Quantum Annealing (QA) holds great potential for solving combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms heavily relies on the embedding of problem instances, represented as logical graphs, into the quantu
Externí odkaz:
http://arxiv.org/abs/2406.07124
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of
Externí odkaz:
http://arxiv.org/abs/2403.04784
Autor:
Fontanesi, G., Ortíz, F., Lagunas, E., Baeza, V. Monzon, Vázquez, M. Á., Vásquez-Peralvo, J. A., Minardi, M., Vu, H. N., Honnaiah, P. J., Lacoste, C., Drif, Y., Abdu, T. S., Eappen, G., Rehman, J., Garcés-Socorrás, L. M., Martins, W. A., Henarejos, P., Al-Hraishawi, H., Duncan, J. C. Merlano, Vu, T. X., Chatzinotas, S.
This paper surveys the application and development of Artificial Intelligence (AI) in Satellite Communication (SatCom) and Non-Terrestrial Networks (NTN). We first present a comprehensive list of use cases, the relative challenges and the main AI too
Externí odkaz:
http://arxiv.org/abs/2304.13008
Autor:
Vu, Minh N., Thai, My T.
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 Networks, it is also non-trivial to interpret predictions made by a TGNN
Externí odkaz:
http://arxiv.org/abs/2212.00952
Autor:
Tran, Huan, Vu, Tuoc N.
Publikováno v:
Phys. Rev. Materials 7, 054805 (2023)
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future discoveries if thei
Externí odkaz:
http://arxiv.org/abs/2211.03265
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. The goal of this work is to introduce a novel perturbation scheme so tha
Externí odkaz:
http://arxiv.org/abs/2209.08453
Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating th
Externí odkaz:
http://arxiv.org/abs/2209.08448
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-linear temporal dynamic. The explanation of TGNNs is of vital importanc
Externí odkaz:
http://arxiv.org/abs/2209.00807
Autor:
Phan, Vu H. N., Vardi, Moshe Y.
In Bayesian inference, the maximum a posteriori (MAP) problem combines the most probable explanation (MPE) and marginalization (MAR) problems. The counterpart in propositional logic is the exist-random stochastic satisfiability (ER-SSAT) problem, whi
Externí odkaz:
http://arxiv.org/abs/2205.09826
Autor:
Phan, Vu H. N., Vardi, Moshe Y.
In Bayesian inference, the most probable explanation (MPE) problem requests a variable instantiation with the highest probability given some evidence. Since a Bayesian network can be encoded as a literal-weighted CNF formula $\varphi$, we study Boole
Externí odkaz:
http://arxiv.org/abs/2205.08632