Ekin Dogus Cubuk
Ekin Dogus Cubuk
Google DeepMind
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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
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
Randaugment: Practical automated data augmentation with a reduced search space
ED Cubuk, B Zoph, J Shlens, QV Le
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
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
Augmix: A simple method to improve robustness and uncertainty under data shift
D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan
International conference on learning representations 1 (2), 5, 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 31, 2018
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
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
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
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
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
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
A structural approach to relaxation in glassy liquids
SS Schoenholz, ED Cubuk, E Kaxiras, AJ Liu
Nature Physics 12, 469-471, 2016
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
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
Adversarial examples are a natural consequence of test error in noise
J Gilmer, N Ford, N Carlini, E Cubuk
International Conference on Machine Learning, 2280-2289, 2019
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
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
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
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
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