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Manli Shu
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Year
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
1742022
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
772023
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
482022
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
39*2021
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
352023
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
30*2021
Bring your own data! self-supervised evaluation for large language models
N Jain, K Saifullah, Y Wen, J Kirchenbauer, M Shu, A Saha, M Goldblum, ...
arXiv preprint arXiv:2306.13651, 2023
172023
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
162024
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
162021
Adversarial Differentiable Data Augmentation for Autonomous Systems
M Shu, Y Shen, MC Lin, T Goldstein
International Conference on Robotics and Automation (ICRA), 2021
132021
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
82024
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
82021
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
62020
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
32024
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
32020
Model-Agnostic Hierarchical Attention for 3D Object Detection
M Shu, L Xue, N Yu, R Martín-Martín, JC Niebles, C Xiong, R Xu
arXiv preprint arXiv:2301.02650, 2023
12023
Systems and methods for attention mechanism in three-dimensional object detection
M Shu, L Xue, N Yu, R Martín-Martín, JCN Duque, C Xiong, R Xu
US Patent App. 18/161,661, 2024
2024
Neural network prompt tuning
A Anandkumar, C Xiao, W Nie, DA Huang, Z Yu, M Shu
US Patent App. 18/243,348, 2024
2024
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