Headless Horseman: Adversarial Attacks on Transfer Learning Models. - Ahmed Abdelkader; Michael J. Curry; Liam Fowl; Tom Goldstein; Avi Schwarzschild; Manli Shu; Christoph Studer; Chen Zhu 001 (ICASSP, 2020)

Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization. - Manli Shu; Zuxuan Wu; Micah Goldblum; Tom Goldstein (CoRR, 2020)

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. - Chen Zhu 001; Zheng Xu 002; Ali Shafahi; Manli Shu; Amin Ghiasi; Tom Goldstein (CoRR, 2020)

Adversarial Differentiable Data Augmentation for Autonomous Systems. - Manli Shu; Yu Shen; Ming C. Lin; Tom Goldstein (ICRA, 2021)

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering. - Yu Shen; Laura Y. Zheng; Manli Shu; Weizi Li; Tom Goldstein; Ming C. Lin (NeurIPS, 2021)

Encoding Robustness to Image Style via Adversarial Feature Perturbations. - Manli Shu; Zuxuan Wu; Micah Goldblum; Tom Goldstein (NeurIPS, 2021)

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images. - Yu Shen; Laura Y. Zheng; Manli Shu; Weizi Li; Tom Goldstein; Ming C. Lin (CoRR, 2021)

The Close Relationship Between Contrastive Learning and Meta-Learning. - Renkun Ni; Manli Shu; Hossein Souri; Micah Goldblum; Tom Goldstein (ICLR, 2022)

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. - Roman Levin; Manli Shu; Eitan Borgnia; Furong Huang; Micah Goldblum; Tom Goldstein (NeurIPS, 2022)

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. - Manli Shu; Weili Nie; De-An Huang; Zhiding Yu; Tom Goldstein; Anima Anandkumar; Chaowei Xiao (NeurIPS, 2022)

What do Vision Transformers Learn? A Visual Exploration. - Amin Ghiasi; Hamid Kazemi; Eitan Borgnia; Steven Reich; Manli Shu; Micah Goldblum; Andrew Gordon Wilson; Tom Goldstein (CoRR, 2022)

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. - 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; et al. (NeurIPS, 2023)

On the Exploitability of Instruction Tuning. - Manli Shu; Jiongxiao Wang; Chen Zhu 001; Jonas Geiping; Chaowei Xiao; Tom Goldstein (NeurIPS, 2023)

Model-Agnostic Hierarchical Attention for 3D Object Detection. - Manli Shu; Le Xue; Ning Yu 006; Roberto Martín-Martín; Juan Carlos Niebles; Caiming Xiong; Ran Xu 001 (CoRR, 2023)

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. - Neel Jain; Khalid Saifullah; Yuxin Wen; John Kirchenbauer; Manli Shu; Aniruddha Saha; Micah Goldblum; Jonas Geiping; Tom Goldstein (CoRR, 2023)

Towards Trustworthy Machine Learning Systems. - Manli Shu (2023)

xGen-VideoSyn-1: High-Fidelity Text-to-Video Synthesis with Compressed Representations. - Can Qin; Congying Xia; Krithika Ramakrishnan; Michael S. Ryoo; Lifu Tu; Yihao Feng; Manli Shu; Honglu Zhou; Anas Awadalla; Jun Wang 090; Senthil Purushwalkam; Le Xue; et al. (ECCV Workshops (5), 2024)

On the Reliability of Watermarks for Large Language Models. - John Kirchenbauer; Jonas Geiping; Yuxin Wen; Manli Shu; Khalid Saifullah; Kezhi Kong; Kasun Fernando; Aniruddha Saha; Micah Goldblum; Tom Goldstein (ICLR, 2024)

Hierarchical Point Attention for Indoor 3D Object Detection. - Manli Shu; Le Xue; Ning Yu 006; Roberto Martín-Martín; Caiming Xiong; Tom Goldstein; Juan Carlos Niebles; Ran Xu 001 (ICRA, 2024)

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. - Anas Awadalla; Le Xue; Oscar Lo; Manli Shu; Hannah Lee; Etash Kumar Guha; Sheng Shen 001; Mohamed Awadalla; Silvio Savarese; Caiming Xiong; Ran Xu 001; Yejin Choi 001; et al. (NeurIPS, 2024)

Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. - Yuancheng Xu; Jiarui Yao; Manli Shu; Yanchao Sun; Zichu Wu; Ning Yu 006; Tom Goldstein; Furong Huang (NeurIPS, 2024)

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers. - Siddharth Singh; Prajwal Singhania; Aditya Ranjan; John Kirchenbauer; Jonas Geiping; Yuxin Wen; Neel Jain; Abhimanyu Hans; Manli Shu; Aditya Tomar; Tom Goldstein; Abhinav Bhatele (SC, 2024)

Coercing LLMs to do and reveal (almost) anything. - Jonas Geiping; Alex Stein; Manli Shu; Khalid Saifullah; Yuxin Wen; Tom Goldstein (CoRR, 2024)

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. - Anas Awadalla; Le Xue; Oscar Lo; Manli Shu; Hannah Lee; Etash Kumar Guha; Matt Jordan; Sheng Shen 001; Mohamed Awadalla; Silvio Savarese; Caiming Xiong; Ran Xu 001; et al. (CoRR, 2024)

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models. - Le Xue; Manli Shu; Anas Awadalla; Jun Wang 090; An Yan; Senthil Purushwalkam; Honglu Zhou; Viraj Prabhu; Yutong Dai 002; Michael S. Ryoo; Shrikant Kendre; Jieyu Zhang 001; et al. (CoRR, 2024)

xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs. - Michael S. Ryoo; Honglu Zhou; Shrikant Kendre; Can Qin; Le Xue; Manli Shu; Silvio Savarese; Ran Xu 001; Caiming Xiong; Juan Carlos Niebles (CoRR, 2024)

BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions. - Anas Awadalla; Le Xue; Manli Shu; An Yan; Jun Wang 090; Senthil Purushwalkam; Sheng Shen 001; Hannah Lee; Oscar Lo; Jae Sung Park; Etash Kumar Guha; Silvio Savarese; et al. (CoRR, 2024)

TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action. - Zixian Ma; Jianguo Zhang; Zhiwei Liu 001; Jieyu Zhang 001; Juntao Tan; Manli Shu; Juan Carlos Niebles; Shelby Heinecke; Huan Wang 014; Caiming Xiong; Ranjay Krishna; Silvio Savarese (CoRR, 2024)

ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models. - Jieyu Zhang 001; Le Xue; Linxin Song; Jun Wang 090; Weikai Huang; Manli Shu; An Yan; Zixian Ma; Juan Carlos Niebles; Silvio Savarese; Caiming Xiong; Zeyuan Chen 001; et al. (CoRR, 2024)

Exploiting Sparsity for Long Context Inference: Million Token Contexts on Commodity GPUs. - Ryan Synk; Monte Hoover; John Kirchenbauer; Neel Jain; Alex Stein; Manli Shu; Josue Melendez Sanchez; Ramani Duraiswami; Tom Goldstein (CoRR, 2025)