181 citations · 396 across the 31 of their papers we have counts for
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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…
Global Convergence of Sobolev Training for Overparameterized Neural Networks
Jorio Cocola, Paul Hand
Sobolev loss is used when training a network to approximate the values and derivatives of a target function at a prescribed set of input points. Recent works have demonstrated its…
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Mara Daniels, Paul Hand, Reinhard Heckel
Generative models, such as GANs, learn an explicit low-dimensional representation of a particular class of images, and so they may be used as natural image priors for solving inver…