| Verifying that the influence of a user data point has been removed from a machine learning classifier S Shintre, J Dhaliwal US Patent 10,225,277, 2019 | 5 | 2019 |
| Gradient similarity: An explainable approach to detect adversarial attacks against deep learning J Dhaliwal, S Shintre arXiv preprint arXiv:1806.10707, 2018 | 4 | 2018 |
| Making Machine Learning Forget S Shintre, KA Roundy, J Dhaliwal Annual Privacy Forum, 72-83, 2019 | 2 | 2019 |
| DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees J Dhaliwal, K Hambrook arXiv preprint arXiv:2101.05130, 2021 | | 2021 |
| Compressive Recovery Defense: Defending Neural Networks Against ℓ2, ℓ∞, and ℓ0 Norm Attacks J Dhaliwal, K Hambrook 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | | 2020 |
| Adversarial Campaign Mitigation via ROC-Centric Prognostics J Echauz, K Kenemer, S Hussein, J Dhaliwal, S Shintre, S Grzonkowski, ... Annual Conference of the PHM Society 11 (1), 2019 | | 2019 |
| Verifying that the influence of a user data point has been removed from a machine learning classifier S Shintre, J Dhaliwal US Patent 10,397,266, 2019 | | 2019 |
| Recovery Guarantees for Compressible Signals with Adversarial Noise J Dhaliwal, K Hambrook arXiv preprint arXiv:1907.06565, 2019 | | 2019 |
| Utility Preserving Secure Private Data Release J Dhaliwal, G So, A Parker-Wood, M Beck arXiv preprint arXiv:1901.09858, 2019 | | 2019 |
