| Cold diffusion: Inverting arbitrary image transforms without noise A Bansal, E Borgnia, HM Chu, J Li, H Kazemi, F Huang, M Goldblum, ... Advances in Neural Information Processing Systems 36, 2024 | 263 | 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, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 143 | 2021 |
| Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks A Schwarzschild, E Borgnia, A Gupta, F Huang, U Vishkin, M Goldblum, ... Advances in Neural Information Processing Systems 34, 6695-6706, 2021 | 74 | 2021 |
| 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 | 68 | 2022 |
| Dp-instahide: Provably defusing poisoning and backdoor attacks with differentially private data augmentations E Borgnia, J Geiping, V Cherepanova, L Fowl, A Gupta, A Ghiasi, ... arXiv preprint arXiv:2103.02079, 2021 | 55 | 2021 |
| Modulation of MICAL monooxygenase activity by its calponin homology domain: structural and mechanistic insights SS Alqassim, M Urquiza, E Borgnia, M Nagib, LM Amzel, MA Bianchet Scientific reports 6 (1), 22176, 2016 | 36 | 2016 |
| Canary in a coalmine: Better membership inference with ensembled adversarial queries Y Wen, A Bansal, H Kazemi, E Borgnia, M Goldblum, J Geiping, ... arXiv preprint arXiv:2210.10750, 2022 | 29 | 2022 |
| Thinking Deeper With Recurrent Networks: Logical Extrapolation Without Overthinking A Bansal, A Schwarzschild, E Borgnia, Z Emam, F Huang, M Goldblum, ... | 17* | |
| Where do models go wrong? parameter-space saliency maps for explainability R Levin, M Shu, E Borgnia, F Huang, M Goldblum, T Goldstein Advances in Neural Information Processing Systems 35, 15602-15615, 2022 | 12 | 2022 |
| Datasets for studying generalization from easy to hard examples A Schwarzschild, E Borgnia, A Gupta, A Bansal, Z Emam, F Huang, ... arXiv preprint arXiv:2108.06011, 2021 | 10 | 2021 |
| What do vision transformers learn A Ghiasi, H Kazemi, E Borgnia, S Reich, M Shu, M Goldblum, AG Wilson, ... A visual exploration. arXiv 2212, 2022 | 6 | 2022 |
| Field Theoretic Bayesian Inference Model for Judy-Benjamin Problem E Borgnia | | 2019 |
| MICAL-1 calponim homology (CH) domain modulates the enzyme methionine-sulphoxide oxidase activity MA Bianchet, S Alqassim, E Borgnia, M Nagib, M Urquiza, LM Amzel 66th Annual Meeting, 2016 | | 2016 |
| What do vision transformers learn? A visual exploration H Kazemi, A Ghiasi, E Borgnia, S Reich, M Shu, M Goldblum, AG Wilson, ... | | |
| DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations E Borgnia, J Geiping, V Cherepanova, LH Fowl, A Gupta, A Ghiasi, ... | | |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandEmail yang diverifikasi di cs.umd.edu