| Autoaugment: Learning augmentation strategies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 2927* | 2019 |
| Specaugment: A simple data augmentation method for automatic speech recognition DS Park, W Chan, Y Zhang, CC Chiu, B Zoph, ED Cubuk, QV Le arXiv preprint arXiv:1904.08779, 2019 | 2603 | 2019 |
| Randaugment: Practical data augmentation with no separate search ED Cubuk, B Zoph, J Shlens, QV Le arXiv preprint arXiv:1909.13719 2 (4), 7, 2019 | 2021* | 2019 |
| Fixmatch: Simplifying semi-supervised learning with consistency and confidence K Sohn, D Berthelot, N Carlini, Z Zhang, H Zhang, CA Raffel, ED Cubuk, ... Advances in neural information processing systems 33, 596-608, 2020 | 1735 | 2020 |
| Realistic evaluation of deep semi-supervised learning algorithms A Oliver, A Odena, CA Raffel, ED Cubuk, I Goodfellow Advances in Neural Information Processing Systems, 3235-3246, 2018 | 916 | 2018 |
| Augmix: A simple data processing method to improve robustness and uncertainty D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan arXiv preprint arXiv:1912.02781, 2019 | 727 | 2019 |
| Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring D Berthelot, N Carlini, ED Cubuk, A Kurakin, K Sohn, H Zhang, C Raffel arXiv preprint arXiv:1911.09785, 2019 | 720 | 2019 |
| Simple copy-paste is a strong data augmentation method for instance segmentation G Ghiasi, Y Cui, A Srinivas, R Qian, TY Lin, ED Cubuk, QV Le, B Zoph Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 474 | 2021 |
| Rethinking pre-training and self-training B Zoph, G Ghiasi, TY Lin, Y Cui, H Liu, ED Cubuk, Q Le Advances in neural information processing systems 33, 3833-3845, 2020 | 450 | 2020 |
| Learning data augmentation strategies for object detection B Zoph, ED Cubuk, G Ghiasi, TY Lin, J Shlens, QV Le Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 416 | 2020 |
| Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods ED Cubuk, SS Schoenholz, JM Rieser, BD Malone, J Rottler, DJ Durian, ... Physical Review Letters 114, 108001, 2015 | 360 | 2015 |
| A structural approach to relaxation in glassy liquids SS Schoenholz, ED Cubuk, E Kaxiras, AJ Liu Nature Physics 12, 469-471, 2016 | 357 | 2016 |
| A fourier perspective on model robustness in computer vision D Yin, R Gontijo Lopes, J Shlens, ED Cubuk, J Gilmer Advances in Neural Information Processing Systems 32, 2019 | 321 | 2019 |
| Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials AD Sendek, Q Yang, ED Cubuk, KAN Duerloo, Y Cui, EJ Reed Energy & Environmental Science 10 (1), 306-320, 2017 | 291 | 2017 |
| Atomic Layer Deposition of Stable LiAlF4 Lithium Ion Conductive Interfacial Layer for Stable Cathode Cycling J Xie, AD Sendek, ED Cubuk, X Zhang, Z Lu, Y Gong, T Wu, F Shi, W Liu, ... Acs Nano 11 (7), 7019-7027, 2017 | 245 | 2017 |
| Structure-property relationships from universal signatures of plasticity in disordered solids ED Cubuk, RJS Ivancic, SS Schoenholz, DJ Strickland, A Basu, ... Science 358 (6366), 1033-1037, 2017 | 219 | 2017 |
| Unveiling the predictive power of static structure in glassy systems V Bapst, T Keck, A Grabska-Barwińska, C Donner, ED Cubuk, ... Nature Physics 16 (4), 448-454, 2020 | 211 | 2020 |
| Machine learning-assisted discovery of solid Li-ion conducting materials AD Sendek, ED Cubuk, ER Antoniuk, G Cheon, Y Cui, EJ Reed Chemistry of Materials 31 (2), 342-352, 2018 | 182 | 2018 |
| Revisiting resnets: Improved training and scaling strategies I Bello, W Fedus, X Du, ED Cubuk, A Srinivas, TY Lin, J Shlens, B Zoph Advances in Neural Information Processing Systems 34, 22614-22627, 2021 | 174 | 2021 |
| Adversarial examples are a natural consequence of test error in noise N Ford, J Gilmer, N Carlini, D Cubuk arXiv preprint arXiv:1901.10513, 2019 | 156 | 2019 |
