1 citations · 1 across the 2 of their papers we have counts for
5 papers
Optimal Sample Complexity of Subgradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration
Paul Hand, Oscar Leong, Vladislav Voroninski
We consider the problem of recovering a real-valued -dimensional signal from phaseless, linear measurements and analyze the amplitude-based non-smooth least squares objectiv…
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors
Paul Hand, Oscar Leong, Vladislav Voroninski
Advances in compressive sensing provided reconstruction algorithms of sparse signals from linear measurements with optimal sample complexity, but natural extensions of this methodo…
Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems
Oscar Leong, Wesam Sakla
Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can suc…
Phase Retrieval Under a Generative Prior
Paul Hand, Oscar Leong, Vladislav Voroninski
The phase retrieval problem asks to recover a natural signal from quadratic observations, where is to be minimized. As is common in many imaging prob…
Proving Tucker's Lemma with a Volume Argument
Beauttie Kuture, Oscar Leong, Christopher Loa +2
Sperner's lemma is a statement about labeled triangulations of a simplex. McLennan and Tourky (2007) provided a novel proof of Sperner's Lemma by examining volumes of simplices in…