| Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models M Shu, W Nie, DA Huang, Z Yu, T Goldstein, A Anandkumar, C Xiao Conference on Neural Information Processing Systems (NeurIPS), 2022 | 300 | 2022 |
| On the reliability of watermarks for large language models J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah, K Kong, ... arXiv preprint arXiv:2306.04634, 2023 | 161 | 2023 |
| On the exploitability of instruction tuning M Shu, J Wang, C Zhu, J Geiping, C Xiao, T Goldstein Advances in Neural Information Processing Systems 36, 61836-61856, 2023 | 90 | 2023 |
| What do vision transformers learn? a visual exploration A Ghiasi, H Kazemi, E Borgnia, S Reich, M Shu, M Goldblum, AG Wilson, ... arXiv preprint arXiv:2212.06727, 2022 | 74* | 2022 |
| Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks M Goldblum, H Souri, R Ni, M Shu, V Prabhu, G Somepalli, ... Advances in Neural Information Processing Systems 36, 2024 | 63 | 2024 |
| Coercing LLMs to do and reveal (almost) anything J Geiping, A Stein, M Shu, K Saifullah, Y Wen, T Goldstein arXiv preprint arXiv:2402.14020, 2024 | 47 | 2024 |
| Gradient-Free Adversarial Training against Image Corruption for Learning-based Steering Y Shen, L Zheng, M Shu, W Li, T Goldstein, M Lin Conference on Neural Information Processing Systems (NeurIPS), 2021 | 44* | 2021 |
| xgen-mm (blip-3): A family of open large multimodal models L Xue*, M Shu*, A Awadalla, J Wang, A Yan, S Purushwalkam, H Zhou, ... arXiv preprint arXiv:2408.08872, 2024 | 42 | 2024 |
| Encoding Robustness to Image Style via Adversarial Feature Perturbation M Shu, Z Wu, M Goldblum, T Goldstein Conference on Neural Information Processing Systems (NeurIPS), 2021 | 35* | 2021 |
| Bring Your Own Data! Self-Sensitivity Evaluation for Large Language Models N Jain, K Saifullah, Y Wen, J Kirchenbauer, M Shu, A Saha, M Goldblum, ... First Conference on Language Modeling, 0 | 26* | |
| The Close Relationship Between Contrastive Learning and Meta-Learning R Ni*, M Shu*, H Souri, M Goldblum, T Goldstein International Conference on Learning Representations (ICLR), 2021 | 22 | 2021 |
| MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens A Awadalla, L Xue, O Lo, M Shu, H Lee, EK Guha, M Jordan, S Shen, ... arXiv preprint arXiv:2406.11271, 2024 | 21 | 2024 |
| Adversarial Differentiable Data Augmentation for Autonomous Systems M Shu, Y Shen, MC Lin, T Goldstein International Conference on Robotics and Automation (ICRA), 2021 | 19 | 2021 |
| Shadowcast: Stealthy data poisoning attacks against vision-language models Y Xu, J Yao, M Shu, Y Sun, Z Wu, N Yu, T Goldstein, F Huang arXiv preprint arXiv:2402.06659, 2024 | 17 | 2024 |
| Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability R Levin, M Shu, E Borgnia, F Huang, M Goldblum, T Goldstein Conference on Neural Information Processing Systems (NeurIPS), 2022, 2021 | 12 | 2021 |
| Headless horseman: Adversarial attacks on transfer learning models A Abdelkader, MJ Curry, L Fowl, T Goldstein, A Schwarzschild, M Shu, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 7 | 2020 |
| xgen-mm-vid (blip-3-video): You only need 32 tokens to represent a video even in vlms MS Ryoo, H Zhou, S Kendre, C Qin, L Xue, M Shu, S Savarese, R Xu, ... arXiv preprint arXiv:2410.16267, 2024 | 5 | 2024 |
| ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models J Zhang, L Xue, L Song, J Wang, W Huang, M Shu, A Yan, Z Ma, ... arXiv preprint arXiv:2412.07012, 2024 | 3* | 2024 |
| Towards accurate quantization and pruning via data-free knowledge transfer C Zhu, Z Xu, A Shafahi, M Shu, A Ghiasi, T Goldstein arXiv preprint arXiv:2010.07334, 2020 | 3 | 2020 |
| xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations C Qin, C Xia, K Ramakrishnan, M Ryoo, L Tu, Y Feng, M Shu, H Zhou, ... arXiv preprint arXiv:2408.12590, 2024 | 2 | 2024 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandDirección de correo verificada de cs.umd.edu