Cubuk, Ekin D.

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 3,558

RandAugment: Practical automated data augmentation with a reduced search space

Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2019) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 3,223

Randaugment: Practical automated data augmentation with a reduced search space

Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 3,223

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020) · arXiv

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 2,892

Scaling deep learning for materials discovery

Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,527

AutoAugment: Learning Augmentation Policies from Data

Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2018) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 1,106

An autonomous laboratory for the accelerated synthesis of inorganic materials

Szymanski, N. J., Rendy, B., Fei, Y., Kumar, R. E., He, T., Milsted, D., McDermott, M. J., Gallant, M., Cubuk, E. D., Merchant, A., et al. (2023) · Nature

Machine Learning in Materials ScienceInorganic Chemistry and Materials

Cited by 1,041

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

Oliver, A., Odena, A., Raffel, C., Cubuk, E. D., & Goodfellow, I. J. (2018) · arXiv

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 772

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J. (2017) · Energy & Environmental Science

Machine Learning in Materials ScienceAdvanced Battery Materials and Technologies

Cited by 448

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

Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 165

Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Sendek, A. D., Ransom, B., Cubuk, E. D., Pellouchoud, L. A., Nanda, J., & Reed, E. J. (2022) · Advanced Energy Materials

Machine Learning in Materials ScienceAdvancements in Battery Materials

Cited by 100

Multi-Task Self-Training for Learning General Representations

Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., & Lin, T.-Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 84