| Robust wireless fingerprinting via complex-valued neural networks S Gopalakrishnan, M Cekic, U Madhow 2019 IEEE Global Communications Conference (GLOBECOM), 1-6, 2019 | 57 | 2019 |
| Wireless fingerprinting via deep learning: The impact of confounding factors M Cekic, S Gopalakrishnan, U Madhow 2021 55th Asilomar Conference on Signals, Systems, and Computers, 677-684, 2021 | 52* | 2021 |
| Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations M Cekic, C Bakiskan, U Madhow 2022 IEEE International Conference on Image Processing (ICIP), 2022 | 12 | 2022 |
| Robust adversarial learning via sparsifying front ends S Gopalakrishnan, Z Marzi, M Cekic, U Madhow, R Pedarsani arXiv preprint arXiv:1810.10625, 2018 | 7 | 2018 |
| Towards robust, interpretable neural networks via Hebbian/anti-Hebbian learning: A software framework for training with feature-based costs M Cekic, C Bakiskan, U Madhow Software Impacts 13, 100347, 2022 | 6 | 2022 |
| Early layers are more important for adversarial robustness C Bakiskan, M Cekic, U Madhow ICLR 2022 Workshop on New Frontiers in Adversarial Machine Learning, 2022 | 6 | 2022 |
| Polarizing front ends for robust CNNs C Bakiskan, S Gopalakrishnan, M Cekic, U Madhow, R Pedarsani ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 5 | 2020 |
| Self-supervised speaker recognition training using human-machine dialogues M Cekic, R Li, Z Chen, Y Yang, A Stolcke, U Madhow ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 4 | 2022 |
| PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores L Goncalves, P Mathur, C Lavania, M Cekic, M Federico, KJ Han arXiv preprint arXiv:2404.07336, 2024 | 1 | 2024 |
| Perceptual evaluation of audio-visual synchrony grounded in viewers’ opinion scores L Goncalves, P Mathur, C Lavania, M Cekic, M Federico, K Han | | 2024 |
| Robust Learning Techniques for Deep Neural Networks M Cekic University of California, Santa Barbara, 2022 | | 2022 |
| Layerwise Hebbian/anti-Hebbian (HaH) Learning In Deep Networks: A Neuro-inspired Approach To Robustness M Cekic, C Bakiskan, U Madhow ICML 2022 Workshop on New Frontiers in Adversarial Machine Learning, 2022 | | 2022 |
| A Neuro-Inspired Autoencoding Defense Against Adversarial Attacks C Bakiskan, M Cekic, AD Sezer, U Madhow 2021 IEEE International Conference on Image Processing (ICIP), 3922-3926, 2021 | | 2021 |
| Sparse Coding Frontend for Robust Neural Networks C Bakiskan, M Cekic, AD Sezer, U Madhow International Conference on Learning Representations (ICLR), Workshop on …, 2021 | | 2021 |
| A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations C Bakiskan, M Cekic, AD Sezer, U Madhow arXiv preprint arXiv:2011.10867, 2020 | | 2020 |
Upamanyu MadhowProfessor of Electrical and Computer Engineering, University of California, Santa Barbara在 ece.ucsb.edu 的电子邮件经过验证
