4 papers
Signal Recovery with Non-Expansive Generative Network Priors
Jorio Cocola
We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for…
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems
Sean Gunn, Jorio Cocola, Paul Hand
Generative Adversarial Networks (GANs) have been shown to be powerful and flexible priors when solving inverse problems. One challenge of using them is overcoming representation er…
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…