Manli Shu - RankMe

41 papers A* 13A 1Misc 1Journal 23Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	BLIP-3: A Family of Open Large Multimodal Models. ICCVW 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, Shao-Yen Tseng, Gustavo A. Lujan-Moreno, Matthew L. Olson, Musashi Hinck, David Cobbley, Vasudev Lal, 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
2025	J	`jnl`	Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers. CoRR Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan, John Kirchenbauer, Jonas Geiping, Yuxin Wen, Neel Jain, Abhimanyu Hans, Manli Shu, Aditya Tomar, Tom Goldstein, Abhinav Bhatele
2025	J	`jnl`	Exploiting Sparsity for Long Context Inference: Million Token Contexts on Commodity GPUs. CoRR Ryan Synk, Monte Hoover, John Kirchenbauer, Neel Jain, Alex Stein, Manli Shu, Josue Melendez Sanchez, Ramani Duraiswami, Tom Goldstein
2025	A*	`conf`	LATTE: Learning to Think with Vision Specialists. EMNLP Zixian Ma, Jianguo Zhang, Zhiwei Liu, Jieyu Zhang, Juntao Tan, Manli Shu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Caiming Xiong, Ranjay Krishna, Silvio Savarese
2024	J	`jnl`	BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions. CoRR Anas Awadalla, Le Xue, Manli Shu, An Yan, Jun Wang, Senthil Purushwalkam, Sheng Shen, Hannah Lee, Oscar Lo, Jae Sung Park, Etash Kumar Guha, Silvio Savarese, Ludwig Schmidt, Yejin Choi, Caiming Xiong, Ran Xu
2024	J	`jnl`	Coercing LLMs to do and reveal (almost) anything. CoRR Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein
2024	A	`conf`	Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers. SC Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan, John Kirchenbauer, Jonas Geiping, Yuxin Wen, Neel Jain, Abhimanyu Hans, Manli Shu, Aditya Tomar, Tom Goldstein, Abhinav Bhatele
2024	A*	`conf`	Hierarchical Point Attention for Indoor 3D Object Detection. ICRA Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, Ran Xu
2024	A*	`conf`	MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. NeurIPS Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, Ran Xu, Yejin Choi, Ludwig Schmidt
2024	J	`jnl`	MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. CoRR 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
2024	A*	`conf`	On the Reliability of Watermarks for Large Language Models. ICLR John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
2024	J	`jnl`	ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models. CoRR Jieyu Zhang, Le Xue, Linxin Song, Jun Wang, Weikai Huang, Manli Shu, An Yan, Zixian Ma, Juan Carlos Niebles, Silvio Savarese, Caiming Xiong, Zeyuan Chen, Ranjay Krishna, Ran Xu
2024	A*	`conf`	Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. NeurIPS Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang
2024	J	`jnl`	Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. CoRR Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang
2024	J	`jnl`	TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action. CoRR Zixian Ma, Jianguo Zhang, Zhiwei Liu, Jieyu Zhang, Juntao Tan, Manli Shu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Caiming Xiong, Ranjay Krishna, Silvio Savarese
2024	J	`jnl`	xGen-MM (BLIP-3): A Family of Open Large Multimodal Models. CoRR 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
2024	J	`jnl`	xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs. CoRR Michael S. Ryoo, Honglu Zhou, Shrikant Kendre, Can Qin, Le Xue, Manli Shu, Silvio Savarese, Ran Xu, Caiming Xiong, Juan Carlos Niebles
2024	—	`conf`	xGen-VideoSyn-1: High-Fidelity Text-to-Video Synthesis with Compressed Representations. ECCV Workshops (5) 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
2024	J	`jnl`	xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations. CoRR 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
2023	A*	`conf`	Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS 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
2023	J	`jnl`	Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR 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
2023	J	`jnl`	Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. CoRR Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
2023	J	`jnl`	Model-Agnostic Hierarchical Attention for 3D Object Detection. CoRR Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Juan Carlos Niebles, Caiming Xiong, Ran Xu
2023	A*	`conf`	On the Exploitability of Instruction Tuning. NeurIPS Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein
2023	J	`jnl`	On the Exploitability of Instruction Tuning. CoRR Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein
2023	J	`jnl`	On the Reliability of Watermarks for Large Language Models. CoRR John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
2023	—	`—`	Towards Trustworthy Machine Learning Systems. Manli Shu
2022	A*	`conf`	Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. NeurIPS Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao
2022	J	`jnl`	Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. CoRR Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao
2022	A*	`conf`	The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein
2022	J	`jnl`	What do Vision Transformers Learn? A Visual Exploration. CoRR Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
2022	A*	`conf`	Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. NeurIPS Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein
2021	A*	`conf`	Adversarial Differentiable Data Augmentation for Autonomous Systems. ICRA Manli Shu, Yu Shen, Ming C. Lin, Tom Goldstein
2021	A*	`conf`	Encoding Robustness to Image Style via Adversarial Feature Perturbations. NeurIPS Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein
2021	A*	`conf`	Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. NeurIPS Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin
2021	J	`jnl`	Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images. CoRR Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin
2021	J	`jnl`	Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. CoRR Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein
2020	Misc	`conf`	Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
2020	J	`jnl`	Headless Horseman: Adversarial Attacks on Transfer Learning Models. CoRR Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
2020	J	`jnl`	Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization. CoRR Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein
2020	J	`jnl`	Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein