Ekin Dogus Cubuk
Ekin Dogus Cubuk
Google Brain
Verified email at fas.harvard.edu
Cited by
Cited by
Autoaugment: Learning augmentation policies from data
ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le
arXiv preprint arXiv:1805.09501, 2018
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
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
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
K Sohn, D Berthelot, CL Li, Z Zhang, N Carlini, ED Cubuk, A Kurakin, ...
arXiv preprint arXiv:2001.07685, 2020
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
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
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
Learning data augmentation strategies for object detection
B Zoph, ED Cubuk, G Ghiasi, TY Lin, J Shlens, QV Le
European Conference on Computer Vision, 566-583, 2020
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
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
A fourier perspective on model robustness in computer vision
D Yin, RG Lopes, J Shlens, ED Cubuk, J Gilmer
arXiv preprint arXiv:1906.08988, 2019
Rethinking pre-training and self-training
B Zoph, G Ghiasi, TY Lin, Y Cui, H Liu, ED Cubuk, QV Le
arXiv preprint arXiv:2006.06882, 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
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
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
Improving robustness without sacrificing accuracy with patch gaussian augmentation
RG Lopes, D Yin, B Poole, J Gilmer, ED Cubuk
arXiv preprint arXiv:1906.02611, 2019
Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFF
A Ostadhossein, ED Cubuk, GA Tritsaris, E Kaxiras, S Zhang, ...
Physical Chemistry Chemical Physics 17 (5), 3832-3840, 2015
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