| Certified defenses for adversarial patches P Chiang, R Ni, A Abdelkader, C Zhu, C Studer, T Goldstein arXiv preprint arXiv:2003.06693, 2020 | 188 | 2020 |
| Data augmentation for meta-learning R Ni, M Goldblum, A Sharaf, K Kong, T Goldstein International Conference on Machine Learning, 8152-8161, 2021 | 104 | 2021 |
| Learning accurate low-bit deep neural networks with stochastic quantization Y Dong, R Ni, J Li, Y Chen, J Zhu, H Su arXiv preprint arXiv:1708.01001, 2017 | 92* | 2017 |
| Unraveling meta-learning: Understanding feature representations for few-shot tasks M Goldblum, S Reich, L Fowl, R Ni, V Cherepanova, T Goldstein International Conference on Machine Learning, 3607-3616, 2020 | 90 | 2020 |
| Gradinit: Learning to initialize neural networks for stable and efficient training C Zhu, R Ni, Z Xu, K Kong, WR Huang, T Goldstein Advances in Neural Information Processing Systems 34, 16410-16422, 2021 | 80 | 2021 |
| 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, 29343-29371, 2023 | 68 | 2023 |
| GOAT: A global transformer on large-scale graphs K Kong, J Chen, J Kirchenbauer, R Ni, CB Bruss, T Goldstein International Conference on Machine Learning, 17375-17390, 2023 | 63 | 2023 |
| Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network R Ni, CH Meyer, SS Blemker, JM Hart, X Feng Journal of Medical Imaging 6 (4), 044009-044009, 2019 | 52 | 2019 |
| Wrapnet: Neural net inference with ultra-low-precision arithmetic R Ni, H Chu, O Castañeda Fernández, P Chiang, C Studer, T Goldstein International Conference on Learning Representations ICLR 2021, 2021 | 32* | 2021 |
| Loss landscapes are all you need: Neural network generalization can be explained without the implicit bias of gradient descent P Chiang, R Ni, DY Miller, A Bansal, J Geiping, M Goldblum, T Goldstein The Eleventh International Conference on Learning Representations, 2022 | 31 | 2022 |
| Lung nodule malignancy prediction in sequential ct scans: Summary of isbi 2018 challenge Y Balagurunathan, A Beers, M Mcnitt-Gray, L Hadjiiski, S Napel, ... IEEE transactions on medical imaging 40 (12), 3748-3761, 2021 | 30 | 2021 |
| Promoting fairness in learned models by learning to active learn under parity constraints A Sharaf, H Daume III, R Ni Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 29 | 2022 |
| Optimal statistical and computational rates for one bit matrix completion R Ni, Q Gu Artificial Intelligence and Statistics, 426-434, 2016 | 26 | 2016 |
| The Close Relationship Between Contrastive Learning and Meta-Learning R Ni, M Shu, H Souri, M Goldblum, T Goldstein International Conference on Learning Representations, 2021 | 22 | 2021 |
| Efficient neural networks with elaborate matrix structures in machine learning environments Y Chen, J Li, R Ni US Patent 12,165,065, 2024 | 21 | 2024 |
| WITCHcraft: Efficient PGD attacks with random step size PY Chiang, J Geiping, M Goldblum, T Goldstein, R Ni, S Reich, A Shafahi ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 19 | 2020 |
| Quantitative relationships between individual lower-limb muscle volumes and jump and sprint performances of basketball players T Xie, KB Crump, R Ni, CH Meyer, JM Hart, SS Blemker, X Feng The Journal of Strength & Conditioning Research 34 (3), 623-631, 2020 | 15 | 2020 |
| K-sam: Sharpness-aware minimization at the speed of sgd R Ni, P Chiang, J Geiping, M Goldblum, AG Wilson, T Goldstein arXiv preprint arXiv:2210.12864, 2022 | 10 | 2022 |
| Improving the tightness of convex relaxation bounds for training certifiably robust classifiers C Zhu, R Ni, P Chiang, H Li, F Huang, T Goldstein arXiv preprint arXiv:2002.09766, 2020 | 4 | 2020 |
| Improving Model and Data Efficiency for Deep Learning R Ni University of Maryland, College Park, 2023 | 1 | 2023 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandEmail confirmado em cs.umd.edu