| Low-power computer vision: Status, challenges, and opportunities S Alyamkin, M Ardi, AC Berg, A Brighton, B Chen, Y Chen, HP Cheng, ... IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9 (2 …, 2019 | 83 | 2019 |
| Towards adaptive residual network training: A neural-ode perspective C Dong, L Liu, Z Li, J Shang International conference on machine learning, 2616-2626, 2020 | 31 | 2020 |
| Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings? M Sun, Z Li, C Xiao, H Qiu, B Kailkhura, M Liu, B Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 19 | 2021 |
| BFClass: A Backdoor-free Text Classification Framework Z Li, D Mekala, C Dong, J Shang arXiv preprint arXiv:2109.10855, 2021 | 17 | 2021 |
| 2018 low-power image recognition challenge S Alyamkin, M Ardi, A Brighton, AC Berg, Y Chen, HP Cheng, B Chen, ... arXiv preprint arXiv:1810.01732, 2018 | 17 | 2018 |
| Bag of tricks for FGSM adversarial training Z Li, L Liu, Z Wang, Y Zhou, C Xie arXiv preprint arXiv:2209.02684, 2022 | 6 | 2022 |
| Tied-augment: controlling representation similarity improves data augmentation E Kurtuluş, Z Li, Y Dauphin, ED Cubuk International Conference on Machine Learning, 17994-18007, 2023 | 3 | 2023 |
| Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training Z Li, L Liu, C Dong, J Shang arXiv preprint arXiv:2010.08034, 2020 | 3 | 2020 |
| Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies Z Li, C Xie, ED Cubuk arXiv preprint arXiv:2404.08197, 2024 | | 2024 |
Jingbo ShangAssistant Professor, UC San DiegoVerified email at ucsd.edu
