17 citations · 18 across the 2 of their papers we have counts for
7 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…
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors
Jorio Cocola, Paul Hand, Vladislav Voroninski
Many problems in statistics and machine learning require the reconstruction of a rank-one signal matrix from noisy data. Enforcing additional prior information on the rank-one comp…
A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors
Wen Huang, Paul Hand, Reinhard Heckel +1
Deep generative modeling has led to new and state of the art approaches for enforcing structural priors in a variety of inverse problems. In contrast to priors given by sparsity, d…
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…
Rate-Optimal Denoising with Deep Neural Networks
Reinhard Heckel, Wen Huang, Paul Hand +1
Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying princip…