Tsui-Wei Weng
Tsui-Wei Weng
Verified email at - Homepage
Cited by
Cited by
Towards fast computation of certified robustness for relu networks
L Weng, H Zhang, H Chen, Z Song, CJ Hsieh, L Daniel, D Boning, ...
International Conference on Machine Learning, 5276-5285, 2018
Efficient neural network robustness certification with general activation functions
H Zhang, TW Weng, PY Chen, CJ Hsieh, L Daniel
Advances in neural information processing systems 31, 2018
Evaluating the robustness of neural networks: An extreme value theory approach
TW Weng, H Zhang, PY Chen, J Yi, D Su, Y Gao, CJ Hsieh, L Daniel
arXiv preprint arXiv:1801.10578, 2018
Topology attack and defense for graph neural networks: An optimization perspective
K Xu, H Chen, S Liu, PY Chen, TW Weng, M Hong, X Lin
arXiv preprint arXiv:1906.04214, 2019
Cnn-cert: An efficient framework for certifying robustness of convolutional neural networks
A Boopathy, TW Weng, PY Chen, S Liu, L Daniel
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3240-3247, 2019
Big-data tensor recovery for high-dimensional uncertainty quantification of process variations
Z Zhang, TW Weng, L Daniel
IEEE Transactions on Components, Packaging and Manufacturing Technology 7 (5 …, 2016
Synthesis model and design of a common-mode bandstop filter (CM-BSF) with an all-pass characteristic for high-speed differential signals
TW Weng, CH Tsai, CH Chen, DH Han, TL Wu
IEEE transactions on microwave theory and techniques 62 (8), 1647-1656, 2014
POPQORN: Quantifying robustness of recurrent neural networks
CY Ko, Z Lyu, L Weng, L Daniel, N Wong, D Lin
International Conference on Machine Learning, 3468-3477, 2019
Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters
TW Weng, Z Zhang, Z Su, Y Marzouk, A Melloni, L Daniel
Optics express 23 (4), 4242-4254, 2015
PROVEN: Verifying robustness of neural networks with a probabilistic approach
L Weng, PY Chen, L Nguyen, M Squillante, A Boopathy, I Oseledets, ...
International Conference on Machine Learning, 6727-6736, 2019
Finite-sum smooth optimization with SARAH
LM Nguyen, M van Dijk, DT Phan, PH Nguyen, TW Weng, ...
Computational Optimization and Applications, 1-33, 2022
Towards verifying robustness of neural networks against a family of semantic perturbations
J Mohapatra, TW Weng, PY Chen, S Liu, L Daniel
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Stochastic simulation and robust design optimization of integrated photonic filters
TW Weng, D Melati, A Melloni, L Daniel
Nanophotonics 6 (1), 299-308, 2017
Towards certificated model robustness against weight perturbations
TW Weng, P Zhao, S Liu, PY Chen, X Lin, L Daniel
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6356-6363, 2020
Robust deep reinforcement learning through adversarial loss
T Oikarinen, W Zhang, A Megretski, L Daniel, TW Weng
Advances in Neural Information Processing Systems 34, 26156-26167, 2021
Higher-order certification for randomized smoothing
J Mohapatra, CY Ko, TW Weng, PY Chen, S Liu, L Daniel
Advances in Neural Information Processing Systems 33, 4501-4511, 2020
On fast adversarial robustness adaptation in model-agnostic meta-learning
R Wang, K Xu, S Liu, PY Chen, TW Weng, C Gan, M Wang
ICLR 2021, 2021
Verification of neural network control policy under persistent adversarial perturbation
YS Wang, TW Weng, L Daniel
arXiv preprint arXiv:1908.06353, 2019
A big-data approach to handle process variations: Uncertainty quantification by tensor recovery
Z Zhang, TW Weng, L Daniel
2016 IEEE 20th Workshop on Signal and Power Integrity (SPI), 1-4, 2016
Toward evaluating robustness of deep reinforcement learning with continuous control
TW Weng, KD Dvijotham, J Uesato, K Xiao, S Gowal, R Stanforth, P Kohli
International Conference on Learning Representations, 2019
The system can't perform the operation now. Try again later.
Articles 1–20