Ekin Dogus Cubuk

According to our database, Ekin Dogus Cubuk authored at least 52 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Scaling deep learning for materials discovery.

Amil Merchant, Simon L. Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin Dogus Cubuk

Nat., 2023

AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions.

Esteban Real, Yao Chen, Mirko Rossini, Connal de Souza, Manav Garg, Akhil Verghese, Moritz Firsching, Quoc V. Le, Ekin Dogus Cubuk, David H. Park

CoRR, 2023

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.

Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore

CoRR, 2023

Scalable Diffusion for Materials Generation.

Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk

CoRR, 2023

Predicting emergence of crystals from amorphous matter with deep learning.

Muratahan Aykol, Amil Merchant, Simon L. Batzner, Jennifer N. Wei, Ekin Dogus Cubuk

CoRR, 2023

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials.

Mgcini Keith Phuthi, Archie Mingze Yao, Simon L. Batzner, Albert Musaelian, Boris Kozinsky, Ekin Dogus Cubuk, Venkatasubramanian Viswanathan

CoRR, 2023

End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX.

Mehmet Cagri Kaymak, Samuel S. Schoenholz, Ekin D. Cubuk, Kurt A. O'Hearn, Kenneth M. Merz Jr., Hasan Metin Aktulga

Proceedings of the High Performance Computing - 38th International Conference, 2023

Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations.

Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation.

Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk

Proceedings of the International Conference on Machine Learning, 2023

2022

Do better ImageNet classifiers assess perceptual similarity better?

Manoj Kumar, Neil Houlsby, Nal Kalchbrenner, Ekin Dogus Cubuk

Trans. Mach. Learn. Res., 2022

∂<i>PV</i>: An end-to-end differentiable solar-cell simulator.

Sean Mann, Eric Fadel, Samuel S. Schoenholz, Ekin D. Cubuk, Steven G. Johnson, Giuseppe Romano

Comput. Phys. Commun., 2022

LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations.

Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan

CoRR, 2022

What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries.

Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli, Samuel S. Schoenholz

CoRR, 2022

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.

Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, Dragomir Anguelov

CoRR, 2022

On the surprising tradeoff between ImageNet accuracy and perceptual similarity.

Manoj Kumar, Neil Houlsby, Nal Kalchbrenner, Ekin D. Cubuk

CoRR, 2022

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR.

Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park

Proceedings of the IEEE Spoken Language Technology Workshop, 2022

No One Representation to Rule Them All: Overlapping Features of Training Methods.

Raphael Gontijo Lopes, Yann N. Dauphin, Ekin Dogus Cubuk

Proceedings of the Tenth International Conference on Learning Representations, 2022

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.

Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Jay Shi, Shuyang Cheng, Dragomir Anguelov

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Revisiting ResNets: Improved Training and Scaling Strategies.

Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learn2Hop: Learned Optimization on Rough Landscapes.

Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin D. Cubuk

Proceedings of the 38th International Conference on Machine Learning, 2021

Tradeoffs in Data Augmentation: An Empirical Study.

Raphael Gontijo Lopes, Sylvia J. Smullin, Ekin Dogus Cubuk, Ethan Dyer

Proceedings of the 9th International Conference on Learning Representations, 2021

Deconstructing the Regularization of BatchNorm.

Yann N. Dauphin, Ekin Dogus Cubuk

Proceedings of the 9th International Conference on Learning Representations, 2021

Multi-Task Self-Training for Learning General Representations.

Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation.

Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Crystal Structure Search with Random Relaxations Using Graph Networks.

Gowoon Cheon, Lusann Yang, Kevin McCloskey, Evan J. Reed, Ekin D. Cubuk

CoRR, 2020

Does Data Augmentation Benefit from Split BatchNorms.

Amil Merchant, Barret Zoph, Ekin Dogus Cubuk

CoRR, 2020

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics.

Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, Kieron Burke

CoRR, 2020

Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.

Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

CoRR, 2020

Affinity and Diversity: Quantifying Mechanisms of Data Augmentation.

Raphael Gontijo Lopes, Sylvia J. Smullin, Ekin D. Cubuk, Ethan Dyer

CoRR, 2020

Rethinking Pre-training and Self-training.

Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence.

Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

JAX MD: A Framework for Differentiable Physics.

Samuel S. Schoenholz, Ekin Dogus Cubuk

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

Proceedings of the 8th International Conference on Learning Representations, 2020

ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring.

David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel

Proceedings of the 8th International Conference on Learning Representations, 2020

Learning Data Augmentation Strategies for Object Detection.

Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le

Proceedings of the Computer Vision - ECCV 2020, 2020

Improving 3D Object Detection Through Progressive Population Based Augmentation.

Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov

Proceedings of the Computer Vision - ECCV 2020, 2020

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.

Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

Proceedings of the Computer Vision - ECCV 2020, 2020

Randaugment: Practical automated data augmentation with a reduced search space.

Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel

CoRR, 2019

RandAugment: Practical data augmentation with no separate search.

Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le

CoRR, 2019

Using learned optimizers to make models robust to input noise.

Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk

CoRR, 2019

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation.

Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk

CoRR, 2019

A Fourier Perspective on Model Robustness in Computer Vision.

Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, Justin Gilmer

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le

Proceedings of the Interspeech 2019, 2019

Adversarial Examples Are a Natural Consequence of Test Error in Noise.

Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk

Proceedings of the 36th International Conference on Machine Learning, 2019

AutoAugment: Learning Augmentation Strategies From Data.

Ekin D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, Quoc V. Le

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

AutoAugment: Learning Augmentation Policies from Data.

Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, Quoc V. Le

CoRR, 2018

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms.

Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, Ian J. Goodfellow

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Realistic Evaluation of Semi-Supervised Learning Algorithms.

Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow

Proceedings of the 6th International Conference on Learning Representations, 2018

Intriguing Properties of Adversarial Examples.

Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le

Proceedings of the 6th International Conference on Learning Representations, 2018

2013

Computational caches.

Amos Waterland, Elaine Angelino, Ekin D. Cubuk, Efthimios Kaxiras, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer

Proceedings of the 6th Annual International Systems and Storage Conference, 2013

2009

Specialization as an optimal strategy under varying external conditions.

M. Ani Hsieh, Ádám M. Halász, Ekin Dogus Cubuk, Samuel S. Schoenholz, Alcherio Martinoli

Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009