Specialization as an optimal strategy under varying external conditions. - M. Ani Hsieh; Ádám M. Halász; Ekin Dogus Cubuk; Samuel S. Schoenholz; Alcherio Martinoli (ICRA, 2009)

Computational caches. - Amos Waterland; Elaine Angelino; Ekin D. Cubuk; Efthimios Kaxiras; Ryan P. Adams; Jonathan Appavoo; Margo I. Seltzer (SYSTOR, 2013)

Intriguing Properties of Adversarial Examples. - Ekin Dogus Cubuk; Barret Zoph; Samuel S. Schoenholz; Quoc V. Le (ICLR (Workshop), 2018)

Realistic Evaluation of Semi-Supervised Learning Algorithms. - Avital Oliver; Augustus Odena; Colin Raffel; Ekin D. Cubuk; Ian J. Goodfellow (ICLR (Workshop), 2018)

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. - Avital Oliver; Augustus Odena; Colin Raffel; Ekin Dogus Cubuk; Ian J. Goodfellow (NeurIPS, 2018)

AutoAugment: Learning Augmentation Policies from Data. - Ekin Dogus Cubuk; Barret Zoph; Dandelion Mané; Vijay Vasudevan; Quoc V. Le (CoRR, 2018)

AutoAugment: Learning Augmentation Strategies From Data. - Ekin D. Cubuk; Barret Zoph; Dandelion Mané; Vijay Vasudevan; Quoc V. Le (CVPR, 2019)

Adversarial Examples Are a Natural Consequence of Test Error in Noise. - Justin Gilmer; Nicolas Ford; Nicholas Carlini; Ekin D. Cubuk (ICML, 2019)

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. - Daniel S. Park; William Chan; Yu Zhang 033; Chung-Cheng Chiu; Barret Zoph; Ekin D. Cubuk; Quoc V. Le (INTERSPEECH, 2019)

A Fourier Perspective on Model Robustness in Computer Vision. - Dong Yin; Raphael Gontijo Lopes; Jonathon Shlens; Ekin Dogus Cubuk; Justin Gilmer (NeurIPS, 2019)

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation. - Raphael Gontijo Lopes; Dong Yin; Ben Poole; Justin Gilmer; Ekin D. Cubuk (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)

RandAugment: Practical data augmentation with no separate search. - Ekin D. Cubuk; Barret Zoph; Jonathon Shlens; Quoc V. Le (CoRR, 2019)

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring. - David Berthelot; Nicholas Carlini; Ekin D. Cubuk; Alex Kurakin; Kihyuk Sohn; Han Zhang 010; Colin Raffel (CoRR, 2019)

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 (ECCV (9), 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; et al. (ECCV (21), 2020)

Learning Data Augmentation Strategies for Object Detection. - Barret Zoph; Ekin D. Cubuk; Golnaz Ghiasi; Tsung-Yi Lin; Jonathon Shlens; Quoc V. Le (ECCV (27), 2020)

ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. - David Berthelot; Nicholas Carlini; Ekin D. Cubuk; Alex Kurakin; Kihyuk Sohn; Han Zhang 010; Colin Raffel (ICLR, 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 (ICLR, 2020)

RandAugment: Practical Automated Data Augmentation with a Reduced Search Space. - Ekin Dogus Cubuk; Barret Zoph; Jonathon Shlens; Quoc Le 001 (NeurIPS, 2020)

JAX MD: A Framework for Differentiable Physics. - Samuel S. Schoenholz; Ekin Dogus Cubuk (NeurIPS, 2020)

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. - Kihyuk Sohn; David Berthelot; Nicholas Carlini; Zizhao Zhang; Han Zhang 010; Colin Raffel; Ekin Dogus Cubuk; Alexey Kurakin; Chun-Liang Li (NeurIPS, 2020)

Rethinking Pre-training and Self-training. - Barret Zoph; Golnaz Ghiasi; Tsung-Yi Lin; Yin Cui; Hanxiao Liu; Ekin Dogus Cubuk; Quoc Le 001 (NeurIPS, 2020)

Affinity and Diversity: Quantifying Mechanisms of Data Augmentation. - Raphael Gontijo Lopes; Sylvia J. Smullin; Ekin D. Cubuk; Ethan Dyer (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)

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics. - Li Li 060; Stephan Hoyer; Ryan Pederson; Ruoxi Sun 002; Ekin D. Cubuk; Patrick Riley 001; Kieron Burke (CoRR, 2020)

Does Data Augmentation Benefit from Split BatchNorms. - Amil Merchant; Barret Zoph; Ekin Dogus Cubuk (CoRR, 2020)

Crystal Structure Search with Random Relaxations Using Graph Networks. - Gowoon Cheon; Lusann Yang; Kevin McCloskey; Evan J. Reed; Ekin D. Cubuk (CoRR, 2020)

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 (CVPR, 2021)

Multi-Task Self-Training for Learning General Representations. - Golnaz Ghiasi; Barret Zoph; Ekin D. Cubuk; Quoc V. Le; Tsung-Yi Lin (ICCV, 2021)

Deconstructing the Regularization of BatchNorm. - Yann N. Dauphin; Ekin Dogus Cubuk (ICLR, 2021)

Tradeoffs in Data Augmentation: An Empirical Study. - Raphael Gontijo Lopes; Sylvia J. Smullin; Ekin Dogus Cubuk; Ethan Dyer (ICLR, 2021)

Learn2Hop: Learned Optimization on Rough Landscapes. - Amil Merchant; Luke Metz; Samuel S. Schoenholz; Ekin D. Cubuk (ICML, 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 (NeurIPS, 2021)

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. - Zhaoqi Leng; Mingxing Tan; Chenxi Liu 001; Ekin Dogus Cubuk; Jay Shi; Shuyang Cheng; Dragomir Anguelov (ICLR, 2022)

No One Representation to Rule Them All: Overlapping Features of Training Methods. - Raphael Gontijo Lopes; Yann N. Dauphin; Ekin Dogus Cubuk (ICLR, 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 001; Quoc V. Le; Daniel S. Park (SLT, 2022)

On the surprising tradeoff between ImageNet accuracy and perceptual similarity. - Manoj Kumar; Neil Houlsby; Nal Kalchbrenner; Ekin D. Cubuk (CoRR, 2022)

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. - Zhaoqi Leng; Mingxing Tan; Chenxi Liu 001; Ekin Dogus Cubuk; Xiaojie Shi; Shuyang Cheng; Dragomir Anguelov (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)

LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations. - Zhaoqi Leng; Guowang Li; Chenxi Liu 001; Ekin Dogus Cubuk; Pei Sun; Tong He; Dragomir Anguelov; Mingxing Tan (CoRR, 2022)

∂PV: 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)

Do better ImageNet classifiers assess perceptual similarity better? - Manoj Kumar; Neil Houlsby; Nal Kalchbrenner; Ekin Dogus Cubuk (Trans. Mach. Learn. Res., 2022)

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. - Emirhan Kurtulus; Zichao Li 002; Yann N. Dauphin; Ekin Dogus Cubuk (ICML, 2023)

Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations. - Zhaoqi Leng; Guowang Li; Chenxi Liu 001; Ekin Dogus Cubuk; Pei Sun; Tong He; Dragomir Anguelov; Mingxing Tan (ICRA, 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 (ISC, 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)

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)

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)

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)

Scaling deep learning for materials discovery. - Amil Merchant; Simon L. Batzner; Samuel S. Schoenholz; Muratahan Aykol; Gowoon Cheon; Ekin Dogus Cubuk (Nat., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Scalable Diffusion for Materials Generation. - Sherry Yang 001; KwangHwan Cho; Amil Merchant; Pieter Abbeel; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (ICLR, 2024)

Generative Hierarchical Materials Search. - Sherry Yang 001; Simon L. Batzner; Ruiqi Gao; Muratahan Aykol; Alexander L. Gaunt; Brendan McMorrow; Danilo Jimenez Rezende; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (NeurIPS, 2024)

Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies. - Zichao Li 002; Cihang Xie; Ekin Dogus Cubuk (Trans. Mach. Learn. Res., 2024)

Predicting emergence of crystals from amorphous precursors with deep learning potentials. - Muratahan Aykol; Amil Merchant; Simon L. Batzner; Jennifer N. Wei; Ekin Dogus Cubuk (Nat. Comput. Sci., 2025)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)

System of Agentic AI for the Discovery of Metal-Organic Frameworks. - Théo Jaffrelot Inizan; Sherry Yang 001; Aaron D. Kaplan; Yen-hsu Lin; Jian Yin 030; Saber Mirzaei; Mona Abdelgaid; Ali H. Alawadhi; KwangHwan Cho; Zhiling Zheng; Ekin Dogus Cubuk; Christian Borgs; et al. (CoRR, 2025)