| Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses M Goldblum, D Tsipras, C Xie, ... IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022, 2022 | 297* | 2022 |
| Saint: Improved neural networks for tabular data via row attention and contrastive pre-training G Somepalli, M Goldblum, A Schwarzschild, CB Bruss, T Goldstein arXiv preprint arXiv:2106.01342, 2021 | 272* | 2021 |
| The Intrinsic Dimension of Images and Its Impact on Learning P Pope, C Zhu, A Abdelkader, M Goldblum, T Goldstein International Conference on Learning Representations (ICLR) 2021, 2021 | 234 | 2021 |
| Diffusion art or digital forgery? investigating data replication in diffusion models G Somepalli, V Singla, M Goldblum, J Geiping, T Goldstein Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 203 | 2023 |
| Adversarially Robust Distillation M Goldblum, L Fowl, S Feizi, T Goldstein AAAI Conference on Artificial Intelligence (AAAI) 2020, 2020 | 200 | 2020 |
| Cold diffusion: Inverting arbitrary image transforms without noise A Bansal, E Borgnia, HM Chu, JS Li, H Kazemi, F Huang, M Goldblum, ... Advances in Neural Information Processing Systems (NeurIPS), 2023 | 188 | 2023 |
| Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks A Schwarzschild*, M Goldblum*, A Gupta, JP Dickerson, T Goldstein International Conference on Machine Learning (ICML) 2021, 2021 | 161 | 2021 |
| Baseline defenses for adversarial attacks against aligned language models N Jain, A Schwarzschild, Y Wen, G Somepalli, J Kirchenbauer, P Chiang, ... arXiv preprint arXiv:2309.00614, 2023 | 152* | 2023 |
| Universal guidance for diffusion models A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ... The Twelfth International Conference on Learning Representations (ICLR) 2024, 2024 | 144* | 2024 |
| Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff E Borgnia, V Cherepanova, L Fowl, A Ghiasi, J Geiping, M Goldblum, ... International Conference on Acoustics, Speech, and Signal Processing (ICASSP …, 2021 | 143* | 2021 |
| Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery Y Wen, N Jain, J Kirchenbauer, M Goldblum, J Geiping, T Goldstein Advances in Neural Information Processing Systems 36, 2023 | 136 | 2023 |
| LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition V Cherepanova, M Goldblum, H Foley, S Duan, J Dickerson, G Taylor, ... International Conference on Learning Representations (ICLR) 2021, 2021 | 134 | 2021 |
| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models L Fowl, J Geiping, W Czaja, M Goldblum, T Goldstein International Conference on Learning Representations (ICLR) 2022, 2022 | 116 | 2022 |
| Adversarial Examples Make Strong Poisons L Fowl*, M Goldblum*, P Chiang, J Geiping, W Czaja, T Goldstein Advances in Neural Information Processing Systems (NeurIPS), 2021 | 106 | 2021 |
| Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch H Souri, L Fowl, R Chellappa, M Goldblum, T Goldstein Advances in Neural Information Processing Systems (NeurIPS) 35, 19165-19178, 2022 | 97 | 2022 |
| On the Reliability of Watermarks for Large Language Models J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah, K Kong, ... The Twelfth International Conference on Learning Representations (ICLR) 2024, 2024 | 93* | 2024 |
| Towards transferable adversarial attacks on image and video transformers Z Wei, J Chen, M Goldblum, Z Wu, T Goldstein, YG Jiang, LS Davis IEEE Transactions on Image Processing 32, 6346-6358, 2023 | 92* | 2023 |
| Adversarially Robust Few-Shot Learning: A Meta-Learning Approach M Goldblum, L Fowl, T Goldstein Advances in Neural Information Processing Systems (NeurIPS), 2020 | 92* | 2020 |
| Data Augmentation for Meta-Learning R Ni, M Goldblum, A Sharaf, K Kong, T Goldstein International Conference on Machine Learning (ICML) 2021, 2021 | 87* | 2021 |
| A Cookbook of Self-Supervised Learning R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ... arXiv preprint arXiv:2304.12210, 2023 | 83* | 2023 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of Maryland在 cs.umd.edu 的电子邮件经过验证
