| 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 | 888 | 2018 |
| 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 | 822 | 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 | 564 | 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 | 440 | 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 | 179 | 2019 |
| Label-free concept bottleneck models T Oikarinen, S Das, LM Nguyen, TW Weng. International Conference on Learning Representations, 2023 | 126 | 2023 |
| 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 | 116 | 2019 |
| 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 | 96 | 2019 |
| 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 | 94 | 2021 |
| 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 | 88 | 2020 |
| 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 | 75 | 2016 |
| Finite-sum smooth optimization with SARAH LM Nguyen, M van Dijk, DT Phan, PH Nguyen, TW Weng, ... Computational Optimization and Applications 82 (3), 561-593, 2022 | 71* | 2022 |
| CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks T Oikarinen, TW Weng ICLR 2023, 2023 | 67 | 2023 |
| 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 | 67 | 2014 |
| 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 | 63 | 2015 |
| 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 | 53 | 2021 |
| Stochastic simulation and robust design optimization of integrated photonic filters TW Weng, D Melati, A Melloni, L Daniel Nanophotonics 6 (1), 299-308, 2017 | 47 | 2017 |
| 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 | 46 | 2020 |
| Adversarially robust conformal prediction A Gendler, TW Weng, L Daniel, Y Romano International Conference on Learning Representations, 2021 | 36 | 2021 |
| 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 | 36 | 2020 |
Tuomas OikarinenUC San DiegoE-mail confirmado em ucsd.edu