Donoho, David L.

Compressed sensing

Donoho, D. L. (2006) · IEEE Transactions on Information Theory

Sparse and Compressive Sensing TechniquesMathematical Analysis and Transform Methods

Cited by 23,994

Sparse MRI: The application of compressed sensing for rapid MR imaging

Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine

Advanced MRI Techniques and ApplicationsSparse and Compressive Sensing Techniques

Cited by 6,952

Fast Discrete Curvelet Transforms

Candès, E., Demanet, L., Donoho, D., & Ying, L. (2006) · Multiscale Modeling & Simulation

Mathematical Analysis and Transform MethodsImage and Signal Denoising Methods

Cited by 2,573

Uncertainty Principles and Signal Recovery

Donoho, D. L., & Stark, P. B. (1989) · SIAM Journal on Applied Mathematics

Mathematical Analysis and Transform MethodsSparse and Compressive Sensing Techniques

Cited by 1,054

50 Years of Data Science

Donoho, D. (2017) · Journal of Computational and Graphical Statistics

Statistics Education and MethodologiesBig Data Technologies and Applications

Cited by 695

Wedgelets: nearly minimax estimation of edges

Donoho, D. L. (1999) · The Annals of Statistics

Image and Signal Denoising MethodsStatistical Methods and Inference

Cited by 638

Breakdown Properties of Location Estimates Based on Halfspace Depth and Projected Outlyingness

Donoho, D. L., & Gasko, M. (1992) · The Annals of Statistics

Advanced Statistical Methods and ModelsStatistical Distribution Estimation and Applications

Cited by 583

Prevalence of neural collapse during the terminal phase of deep learning training

Papyan, V., Han, X. Y., & Donoho, D. L. (2020) · Proceedings of the National Academy of Sciences

Stochastic Gradient Optimization TechniquesDomain Adaptation and Few-Shot Learning

Cited by 403

Statistical Estimation and Optimal Recovery

Donoho, D. L. (1994) · The Annals of Statistics

Numerical methods in inverse problemsAdvanced Statistical Methods and Models

Cited by 263

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., Korbak, T., Agrawal, R., Pai, D., Gromov, A., et al. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisTopic Modeling

Cited by 15

Collapse or thrive? perils and promises of synthetic data in a self-generating world

Kazdan, J., Schaeffer, R., Dey, A., Gerstgrasser, M., Rafailov, R., Donoho, D. L., & Koyejo, S. (2024) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 4