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Manli Shu
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2020 – today
- 2024
- [c11]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein:
On the Reliability of Watermarks for Large Language Models. ICLR 2024 - [c10]Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, Ran Xu:
Hierarchical Point Attention for Indoor 3D Object Detection. ICRA 2024: 4245-4251 - [i18]Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang:
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. CoRR abs/2402.06659 (2024) - [i17]Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein:
Coercing LLMs to do and reveal (almost) anything. CoRR abs/2402.14020 (2024) - [i16]Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Matt Jordan, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, Ran Xu, Yejin Choi, Ludwig Schmidt:
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. CoRR abs/2406.11271 (2024) - [i15]Le Xue, Manli Shu, Anas Awadalla, Jun Wang, An Yan, Senthil Purushwalkam, Honglu Zhou, Viraj Prabhu, Yutong Dai, Michael S. Ryoo, Shrikant Kendre, Jieyu Zhang, Can Qin, Shu Zhang, Chia-Chih Chen, Ning Yu, Juntao Tan, Tulika Manoj Awalgaonkar, Shelby Heinecke, Huan Wang, Yejin Choi, Ludwig Schmidt, Zeyuan Chen, Silvio Savarese, Juan Carlos Niebles, Caiming Xiong, Ran Xu:
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models. CoRR abs/2408.08872 (2024) - [i14]Can Qin, Congying Xia, Krithika Ramakrishnan, Michael S. Ryoo, Lifu Tu, Yihao Feng, Manli Shu, Honglu Zhou, Anas Awadalla, Jun Wang, Senthil Purushwalkam, Le Xue, Yingbo Zhou, Huan Wang, Silvio Savarese, Juan Carlos Niebles, Zeyuan Chen, Ran Xu, Caiming Xiong:
xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations. CoRR abs/2408.12590 (2024) - [i13]Michael S. Ryoo, Honglu Zhou, Shrikant Kendre, Can Qin, Le Xue, Manli Shu, Silvio Savarese, Ran Xu, Caiming Xiong, Juan Carlos Niebles:
xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs. CoRR abs/2410.16267 (2024) - 2023
- [c9]Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS 2023 - [c8]Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein:
On the Exploitability of Instruction Tuning. NeurIPS 2023 - [i12]Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Juan Carlos Niebles, Caiming Xiong, Ran Xu:
Model-Agnostic Hierarchical Attention for 3D Object Detection. CoRR abs/2301.02650 (2023) - [i11]John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando
, Aniruddha Saha, Micah Goldblum, Tom Goldstein:
On the Reliability of Watermarks for Large Language Models. CoRR abs/2306.04634 (2023) - [i10]Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. CoRR abs/2306.13651 (2023) - [i9]Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein:
On the Exploitability of Instruction Tuning. CoRR abs/2306.17194 (2023) - [i8]Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR abs/2310.19909 (2023) - 2022
- [c7]Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein:
The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR 2022 - [c6]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. NeurIPS 2022 - [c5]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. NeurIPS 2022 - [i7]Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao:
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. CoRR abs/2209.07511 (2022) - [i6]Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
What do Vision Transformers Learn? A Visual Exploration. CoRR abs/2212.06727 (2022) - 2021
- [c4]Manli Shu, Yu Shen, Ming C. Lin, Tom Goldstein:
Adversarial Differentiable Data Augmentation for Autonomous Systems. ICRA 2021: 14069-14075 - [c3]Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin:
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. NeurIPS 2021: 26250-26263 - [c2]Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein:
Encoding Robustness to Image Style via Adversarial Feature Perturbations. NeurIPS 2021: 28042-28053 - [i5]Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin:
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images. CoRR abs/2102.13262 (2021) - [i4]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. CoRR abs/2108.01335 (2021) - 2020
- [c1]Ahmed Abdelkader, Michael J. Curry
Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP 2020: 3087-3091 - [i3]Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu:
Headless Horseman: Adversarial Attacks on Transfer Learning Models. CoRR abs/2004.09007 (2020) - [i2]Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein:
Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization. CoRR abs/2009.08965 (2020) - [i1]Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein:
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR abs/2010.07334 (2020)
Coauthor Index
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last updated on 2024-11-27 20:33 CET by the dblp team
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