E
Emmanuel J. Candès
Researcher at Stanford University
Publications - 280
Citations - 148481
Emmanuel J. Candès is an academic researcher from Stanford University. The author has contributed to research in topics: Convex optimization & Compressed sensing. The author has an hindex of 102, co-authored 262 publications receiving 135077 citations. Previous affiliations of Emmanuel J. Candès include Samsung & École Normale Supérieure.
Papers
More filters
Journal ArticleDOI
Harmonic Analysis of Neural Networks
TL;DR: A special admissibility condition for neural activation functions is introduced which requires that the neural activation function be oscillatory and linear transforms are constructed which represent quite general functions f as a superposition of ridge functions.
Journal ArticleDOI
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
Yuxin Chen,Emmanuel J. Candès +1 more
TL;DR: In this article, the authors consider the problem of solving quadratic systems of equations in variables, where the number of equations and unknowns is unknown and the search direction is fixed.
Posted Content
Phase Retrieval from Coded Diffraction Patterns
TL;DR: It is shown that PhaseLift, a recent convex programming technique, recovers the phase information exactly from a number of random modulations, which is polylogarithmic in the number of unknowns.
Journal ArticleDOI
Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators
TL;DR: The utility of the unbiased risk estimation for SVT-based denoising of real clinical cardiac MRI series data is demonstrated and an unbiased risk estimate formula for singular value thresholding (SVT), a popular estimation strategy that applies a soft-thresholding rule to the singular values of the noisy observations is given.
Posted Content
Conformalized Quantile Regression
TL;DR: This paper proposes a new method that is fully adaptive to heteroscedasticity, which combines conformal prediction with classical quantile regression, inheriting the advantages of both.