181 citations · 396 across the 31 of their papers we have counts for
36 papers
Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing
Zhaoming Li, Paul Hand
Generative models have been studied experimentally and theoretically as priors for inverse problems such as compressed sensing. Recent work by Gunn et al. studied the use of genera…
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…
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime
Daniel Goldfarb, Paul Hand
Overparameterization is known to permit strong generalization performance in neural networks. In this work, we provide an initial theoretical analysis of its effect on catastrophic…
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues +2
In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resoluti…
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…
Score-based Generative Neural Networks for Large-Scale Optimal Transport
Mara Daniels, Tyler Maunu, Paul Hand
We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions. In certain cases, the optimal transport plan takes the…