14 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
Sean Gunn, Jorio Cocola, Oliver De Candido +2
Latent generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals at a single, fixed complexity, go…
cs.LG2022
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…
cs.NE2018★ 14 cited
State Space Representations of Deep Neural Networks
Michael Hauser, Sean Gunn, Samer Saab +1
This paper deals with neural networks as dynamical systems governed by differential or difference equations. It shows that the introduction of skip connections into network archite…