| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1490 | 2023 |
| Large-Scale Adversarial Training for Vision-and-Language Representation Learning Z Gan, YC Chen, L Li, C Zhu, Y Cheng, J Liu NeurIPS 2020 & arXiv:2006.06195, 2020 | 513 | 2020 |
| Freelb: Enhanced adversarial training for natural language understanding C Zhu, Y Cheng, Z Gan, S Sun, T Goldstein, J Liu ICLR 2020 & arXiv:1909.11764, 2020 | 512 | 2020 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 363 | 2021 |
| Transferable Clean-Label Poisoning Attacks on Deep Neural Nets C Zhu, WR Huang, A Shafahi, H Li, G Taylor, C Studer, T Goldstein ICML 2019 & arXiv: 1905.05897, 2019 | 339 | 2019 |
| The Intrinsic Dimension of Images and Its Impact on Learning P Pope, C Zhu, A Abdelkader, M Goldblum, T Goldstein ICLR 2021 & arXiv:2104.08894, 2021 | 242 | 2021 |
| Robust optimization as data augmentation for large-scale graphs K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 209* | 2022 |
| Compressing neural networks using the variational information bottleneck B Dai, C Zhu, B Guo, D Wipf ICML 2018 & arXiv: 1802.10399, 2018 | 189 | 2018 |
| Certified defenses for adversarial patches PY Chiang, R Ni, A Abdelkader, C Zhu, C Studor, T Goldstein ICLR 2020 & arXiv:2003.06693, 2020 | 175 | 2020 |
| Adversarially robust transfer learning A Shafahi, P Saadatpanah, C Zhu, A Ghiasi, C Studer, D Jacobs, ... ICLR 2020 & arXiv:1905.08232, 2019 | 144 | 2019 |
| Deep k-NN Defense Against Clean-Label Data Poisoning Attacks N Peri, N Gupta, WR Huang, L Fowl, C Zhu, S Feizi, T Goldstein, ... Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 136 | 2020 |
| Modifying memories in transformer models C Zhu, AS Rawat, M Zaheer, S Bhojanapalli, D Li, F Yu, S Kumar arXiv preprint arXiv:2012.00363, 2020 | 131 | 2020 |
| Long-short transformer: Efficient transformers for language and vision C Zhu, W Ping, C Xiao, M Shoeybi, T Goldstein, A Anandkumar, ... NeurIPS 2021 & arXiv:2107.02192, 2021 | 129 | 2021 |
| Structured attentions for visual question answering C Zhu, Y Zhao, S Huang, K Tu, Y Ma ICCV 2017 & arXiv: 1708.02071, 2017 | 122 | 2017 |
| Learning from Noisy Anchors for One-stage Object Detection H Li, Z Wu, C Zhu, C Xiong, R Socher, LS Davis CVPR 2020 & arXiv:1912.05086, 2019 | 110 | 2019 |
| Learning visual knowledge memory networks for visual question answering Z Su, C Zhu, Y Dong, D Cai, Y Chen, J Li CVPR 2018 & arXiv: 1806.04860, 2018 | 81 | 2018 |
| Retrieval meets long context large language models P Xu, W Ping, X Wu, L McAfee, C Zhu, Z Liu, S Subramanian, ... ICLR 2024 & arXiv:2310.03025, 2023 | 79 | 2023 |
| GradInit: Learning to initialize neural networks for stable and efficient training C Zhu, R Ni, Z Xu, K Kong, WR Huang, T Goldstein NeurIPS 2021 & arXiv:2102.08098, 2021 | 65 | 2021 |
| Fine-grained video categorization with redundancy reduction attention C Zhu, X Tan, F Zhou, X Liu, K Yue, E Ding, Y Ma Proceedings of the European Conference on Computer Vision (ECCV), 136-152, 2018 | 57 | 2018 |
| On the exploitability of instruction tuning M Shu, J Wang, C Zhu, J Geiping, C Xiao, T Goldstein NeurIPS 2023 & arXiv:2306.17194, 2023 | 51 | 2023 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
