Članki z zahtevami za javni dostop - Han XuVeč o tem
Ni na voljo nikjer: 1
Adversarial Robustness in Deep Learning: From Practices to Theories
H Xu, Y Li, X Liu, W Wang, J Tang
KDD Tutorial (2021), 2021
Zahteve: US National Science Foundation, US Department of Defense
Na voljo nekje: 15
Adversarial attacks and defenses in images, graphs and text: A review
H Xu, Y Ma, H Liu, D Deb, H Liu, J Tang, A Jain, K
International Journal of Automation and Computing (2020), 2020
Zahteve: US National Science Foundation
Adversarial attacks and defenses on graphs
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang
KDD Explorations 22, 19-34, 2021
Zahteve: US National Science Foundation
To be robust or to be fair: Towards fairness in adversarial training
H Xu, X Liu, Y Li, A Jain, J Tang
International Conference on Machine Learning (2021), 2021
Zahteve: US National Science Foundation, US Department of Defense
Deeprobust: a platform for adversarial attacks and defenses
Y Li, W Jin, H Xu, J Tang
AAAI (2021), 2021
Zahteve: US National Science Foundation
Graph neural networks with adaptive residual
X Liu, J Ding, W Jin, H Xu, Y Ma, Z Liu, J Tang
NeurIPS (2021), 2021
Zahteve: US National Science Foundation, US Department of Defense
Jointly attacking graph neural network and its explanations
W Fan, H Xu, W Jin, X Liu, X Tang, S Wang, Q Li, J Tang, J Wang, ...
International Conference on Data Engineering (2023), 2023
Zahteve: US National Science Foundation, US Department of Defense, National Natural …
Imbalanced adversarial training with reweighting
W Wang, H Xu, X Liu, Y Li, B Thuraisingham, J Tang
International Conference on Data Mining (2022), 2022
Zahteve: US National Science Foundation, US Department of Defense
Deep adversarial canonical correlation analysis
W Fan, Y Ma, H Xu, X Liu, J Wang, Q Li, J Tang
SIAM international conference on data mining (2020), 2020
Zahteve: US National Science Foundation, National Natural Science Foundation of China …
Adversarial attacks and defenses: Frontiers, advances and practice
H Xu, Y Li, W Jin, J Tang
KDD Tutorial (2020), 2020
Zahteve: US National Science Foundation
Covariance-insured screening
K He, J Kang, HG Hong, J Zhu, Y Li, H Lin, H Xu, Y Li
Computational statistics & data analysis (2019), 2019
Zahteve: US National Institutes of Health, National Natural Science Foundation of China
Yet meta learning can adapt fast, it can also break easily
H Xu, Y Li, X Liu, H Liu, J Tang
SIAM International Conference on Data Mining (2021), 2021
Zahteve: US National Science Foundation
A selective overview of feature screening methods with applications to neuroimaging data
K He, H Xu, J Kang
Wiley Interdisciplinary Reviews: Computational Statistics (2019) 11, e1454, 2019
Zahteve: US National Institutes of Health
Probabilistic categorical adversarial attack and adversarial training
H Xu, P He, J Ren, Y Wan, Z Liu, H Liu, J Tang
International Conference on Machine Learning (2023), 2023
Zahteve: US National Science Foundation, US Department of Defense, National Natural …
How does the Memorization of Neural Networks Impact Adversarial Robust Models?
H Xu, X Liu, W Wang, Z Liu, AK Jain, J Tang
KDD (2023), 2023
Zahteve: US National Science Foundation, US Department of Defense, National Natural …
Towards adversarial learning: from evasion attacks to poisoning attacks
W Wang, H Xu, Y Wan, J Ren, J Tang
KDD Tutorial (2022), 2022
Zahteve: US National Science Foundation, US Department of Defense
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