| Technical report on the cleverhans v2. 1.0 adversarial examples library N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768, 2016 | 443 | 2016 |
| Interpolated adversarial training: Achieving robust neural networks without sacrificing too much accuracy A Lamb, V Verma, K Kawaguchi, A Matyasko, S Khosla, J Kannala, ... Neural Networks, 2022 | 113 | 2022 |
| cleverhans v0. 1: an adversarial machine learning library I Goodfellow, N Papernot, P McDaniel, R Feinman, F Faghri, A Matyasko, ... arXiv preprint arXiv:1610.00768 1, 2016 | 72 | 2016 |
| Margin maximization for robust classification using deep learning A Matyasko, LP Chau 2017 International Joint Conference on Neural Networks (IJCNN), 300-307, 2017 | 23 | 2017 |
| Improved network robustness with adversary critic A Matyasko, LP Chau Advances in Neural Information Processing Systems, 10578-10587, 2018 | 15 | 2018 |
| PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack A Matyasko, LP Chau arXiv preprint arXiv:2106.01538, 2021 | 9 | 2021 |
| A light field sparse representation structure and its fast coding technique J Chen, A Matyasko, LP Chau 2014 19th International Conference on Digital Signal Processing, 214-218, 2014 | 4 | 2014 |
| Towards deep neural networks robust to adversarial examples A Matyasko Nanyang Technological University, 2020 | 1 | 2020 |
| Enhanced Physics-Informed Neural Networks with Optimized Sensor Placement via Multi-Criteria Adaptive Sampling C Zhou, J Chen, Z Yang, A Matyasko, CE Png 2024 International Joint Conference on Neural Networks (IJCNN), 1-8, 2024 | | 2024 |
