Objective metrics and gradient descent algorithms for adversarial examples in machine learning U Jang, X Wu, S Jha Proceedings of the 33rd Annual Computer Security Applications Conference …, 2017 | 132 | 2017 |
Detecting adversarial examples using data manifolds S Jha, U Jang, S Jha, B Jalaian MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), 547-552, 2018 | 31 | 2018 |
Reinforcing adversarial robustness using model confidence induced by adversarial training X Wu, U Jang, J Chen, L Chen, S Jha International conference on machine learning, 5334-5342, 2018 | 27 | 2018 |
Analyzing and improving neural networks by generating semantic counterexamples through differentiable rendering L Jain, V Chandrasekaran, U Jang, W Wu, A Lee, A Yan, S Chen, S Jha, ... arXiv preprint arXiv:1910.00727, 2019 | 16 | 2019 |
On the need for topology-aware generative models for manifold-based defenses U Jang, S Jha, S Jha arXiv preprint arXiv:1909.03334, 2019 | 16 | 2019 |
Generating semantic adversarial examples with differentiable rendering L Jain, S Chen, W Wu, U Jang, V Chandrasekaran, S Seshia, S Jha | 12 | 2019 |
The manifold assumption and defenses against adversarial perturbations X Wu, U Jang, L Chen, S Jha | 6 | 2018 |
Holistic Cube Analysis: A Query Framework for Data Insights X Wu, S Deep, J Benassi, F Li, Y Zhang, U Jang, J Foster, S Kim, Y Sun, ... arXiv preprint arXiv:2302.00120, 2023 | 1 | 2023 |
Bilevel Relations and Their Applications to Data Insights X Wu, X Yu, S Deep, A Mahmood, U Jang, S Viglas, S Jha, J Cieslewicz, ... arXiv preprint arXiv:2311.04824, 2023 | | 2023 |