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 | 269 | 2018 |
Efficient neural network robustness certification with general activation functions H Zhang, TW Weng, PY Chen, CJ Hsieh, L Daniel arXiv preprint arXiv:1811.00866, 2018 | 194 | 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 | 164 | 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 | 68 | 2019 |
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 | 52 | 2014 |
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 | 48 | 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 | 46 | 2015 |
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 | 45 | 2016 |
Optimal finite-sum smooth non-convex optimization with SARAH LM Nguyen, M van Dijk, DT Phan, PH Nguyen, TW Weng, ... arXiv preprint arXiv:1901.07648, 2019 | 35* | 2019 |
Stochastic simulation and robust design optimization of integrated photonic filters TW Weng, D Melati, A Melloni, L Daniel Nanophotonics 6 (1), 299-308, 2017 | 30 | 2017 |
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 | 26 | 2019 |
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 | 23 | 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 | 12 | 2016 |
Verification of neural network control policy under persistent adversarial perturbation YS Wang, TW Weng, L Daniel arXiv preprint arXiv:1908.06353, 2019 | 7 | 2019 |
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 | 4 | 2020 |
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 | 4 | 2020 |
On extensions of clever: A neural network robustness evaluation algorithm TW Weng, H Zhang, PY Chen, A Lozano, CJ Hsieh, L Daniel 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2018 | 3 | 2018 |
Fast stochastic simulation of silicon waveguide with non-gaussian correlated process variations TW Weng, Z Zhang, Z Su, L Daniel 2014 Asia Communications and Photonics Conference (ACP), 1-3, 2014 | 2 | 2014 |
Filtering device with slotted ground structure TL Wu, TW Weng, CY Hsiao US Patent App. 13/714,704, 2014 | 2 | 2014 |
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning R Wang, K Xu, S Liu, PY Chen, TW Weng, C Gan, M Wang arXiv preprint arXiv:2102.10454, 2021 | 1 | 2021 |