| A survey on transferability of adversarial examples across deep neural networks J Gu, X Jia, P de Jorge, W Yu, X Liu, A Ma, Y Xun, A Hu, A Khakzar, Z Li, ... Transactions on Machine Learning Research (TMLR), 2023 | 32 | 2023 |
| SOAR: Second-Order Adversarial Regularization A Ma, F Faghri, N Papernot, A Farahmand arXiv preprint arXiv:2004.01832, 2020 | 22* | 2020 |
| Comparative analysis of gait speed estimation using wideband and narrowband radars, thermal camera, and motion tracking suit technologies PP Morita, AS Rocha, G Shaker, D Lee, J Wei, B Fong, A Thatte, A Karimi, ... Journal of Healthcare Informatics Research 4, 215-237, 2020 | 18 | 2020 |
| Improving hierarchical adversarial robustness of deep neural networks A Ma, A Virmaux, K Scaman, J Lu arXiv preprint arXiv:2102.09012, 2021 | 6 | 2021 |
| Depth from defocus via active quasi-random point projections A Ma, F Li, A Wong Journal of Computational Vision and Imaging Systems 2 (1), 2016 | 6 | 2016 |
| SAGE: Saliency-Guided Mixup with Optimal Rearrangements A Ma, N Dvornik, R Zhang, L Pishdad, KG Derpanis, A Fazly The 33rd British Machine Vision Conference Proceedings, 2022 | 5 | 2022 |
| Improving adversarial transferability via model alignment A Ma, A Farahmand, Y Pan, P Torr, J Gu European Conference on Computer Vision (ECCV), 2024 | 4 | 2024 |
| Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods A Ma, Y Pan, A Farahmand Transactions on Machine Learning Research (TMLR), 2023 | 4 | 2023 |
| Computational depth from defocus via active quasi-random pattern projections B Ma University of Waterloo, 2018 | 4 | 2018 |
| 63‐3: Real‐time Spatial‐based Projector Resolution Enhancement A Ma, A Gawish, M Lamm, A Wong, P Fieguth SID Symposium Digest of Technical Papers 49 (1), 831-834, 2018 | 4 | 2018 |
| Enhanced Depth from Defocus via Active Quasi-random Colored Point Projections A Ma, A Wong 9th International Conference on Inverse Problems in Engineering, 2017 | 3 | 2017 |
| Depth from Defocus via Active Quasi-random Point Projections: A Deep Learning Approach A Ma, A Wong, D Clausi Image Analysis and Recognition: 14th International Conference, ICIAR 2017 …, 2017 | 3 | 2017 |
| Real-time spatial-based resolution enhancement using shifted superposition B Ma, A Gawish, A Wong, P Fieguth, M Lamm US Patent 10,009,587, 2018 | 2 | 2018 |
| Depth from defocus via active multispectral quasi-random point projections using deep learning A Ma, A Wong, D Clausi Journal of Computational Vision and Imaging Systems 3 (1), 2017 | 2 | 2017 |
| An inverse problem approach to computational active depth from defocus A Ma, A Wong Journal of Physics: Conference Series 1047 (1), 012009, 2018 | 1 | 2018 |
| Motion detection in high resolution enhancement X Hu, A Ma, A Gawish, M Lamm, P Fieguth Journal of Computational Vision and Imaging Systems 3 (1), 2017 | 1 | 2017 |
| PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling A Ma, Y Pan, A Farahmand arXiv preprint arXiv:2502.01925, 2025 | | 2025 |
| Saliency-guided mixup with optimal re-arrangements for efficient data augmentation B Ma, M Dvornik, R Zhang, K Derpanis, A Fazly US Patent App. 18/201,521, 2024 | | 2024 |
| Deep Learning-Driven Depth from Defocus via Active Multispectral Quasi-Random Projections with Complex Subpatterns A Ma, A Wong, DA Clausi 2018 15th Conference on Computer and Robot Vision (CRV), 292-296, 2018 | | 2018 |
Alexander WongCanada Research Chair FIET FInstP FRSPH FRGS FGS FRSA FISDDE, Systems Design, University of WaterlooПодтвержден адрес электронной почты в домене uwaterloo.ca