Ekin Dogus Cubuk

Orcid: 0000-0003-0524-2837

According to our database1, Ekin Dogus Cubuk authored at least 55 papers between 2009 and 2024.

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Bibliography

2024
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies.
Trans. Mach. Learn. Res., 2024

Generative Hierarchical Materials Search.
CoRR, 2024

Scalable Diffusion for Materials Generation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
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Trans. Mach. Learn. Res., 2023

Scaling deep learning for materials discovery.
Nat., 2023

AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions.
CoRR, 2023

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.
CoRR, 2023

Predicting emergence of crystals from amorphous matter with deep learning.
CoRR, 2023

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials.
CoRR, 2023

End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX.
Proceedings of the High Performance Computing - 38th International Conference, 2023

Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation.
Proceedings of the International Conference on Machine Learning, 2023

2022
Do better ImageNet classifiers assess perceptual similarity better?
Trans. Mach. Learn. Res., 2022

∂<i>PV</i>: An end-to-end differentiable solar-cell simulator.
Comput. Phys. Commun., 2022

LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations.
CoRR, 2022

What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries.
CoRR, 2022

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.
CoRR, 2022

On the surprising tradeoff between ImageNet accuracy and perceptual similarity.
CoRR, 2022

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR.
Proceedings of the IEEE Spoken Language Technology Workshop, 2022

No One Representation to Rule Them All: Overlapping Features of Training Methods.
Proceedings of the Tenth International Conference on Learning Representations, 2022

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Revisiting ResNets: Improved Training and Scaling Strategies.
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.
Proceedings of the 38th International Conference on Machine Learning, 2021

Tradeoffs in Data Augmentation: An Empirical Study.
Proceedings of the 9th International Conference on Learning Representations, 2021

Deconstructing the Regularization of BatchNorm.
Proceedings of the 9th International Conference on Learning Representations, 2021

Multi-Task Self-Training for Learning General Representations.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Crystal Structure Search with Random Relaxations Using Graph Networks.
CoRR, 2020

Does Data Augmentation Benefit from Split BatchNorms.
CoRR, 2020

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics.
CoRR, 2020

Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.
CoRR, 2020

Affinity and Diversity: Quantifying Mechanisms of Data Augmentation.
CoRR, 2020

Rethinking Pre-training and Self-training.
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.
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.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

RandAugment: Practical Automated Data Augmentation with a Reduced Search Space.
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.
Proceedings of the 8th International Conference on Learning Representations, 2020

ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring.
Proceedings of the 8th International Conference on Learning Representations, 2020

Learning Data Augmentation Strategies for Object Detection.
Proceedings of the Computer Vision - ECCV 2020, 2020

Improving 3D Object Detection Through Progressive Population Based Augmentation.
Proceedings of the Computer Vision - ECCV 2020, 2020

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.
CoRR, 2019

RandAugment: Practical data augmentation with no separate search.
CoRR, 2019

Using learned optimizers to make models robust to input noise.
CoRR, 2019

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation.
CoRR, 2019

A Fourier Perspective on Model Robustness in Computer Vision.
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.
Proceedings of the 20th Annual Conference of the International Speech Communication Association, 2019

Adversarial Examples Are a Natural Consequence of Test Error in Noise.
Proceedings of the 36th International Conference on Machine Learning, 2019

AutoAugment: Learning Augmentation Strategies From Data.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
AutoAugment: Learning Augmentation Policies from Data.
CoRR, 2018

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms.
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.
Proceedings of the 6th International Conference on Learning Representations, 2018

Intriguing Properties of Adversarial Examples.
Proceedings of the 6th International Conference on Learning Representations, 2018

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

2009
Specialization as an optimal strategy under varying external conditions.
Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009


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