42 citations · 48 across the 6 of their papers we have counts for
6 papers
On the Contractivity of Stochastic Interpolation Flow
Mara Daniels
We investigate stochastic interpolation, a recently introduced framework for high dimensional sampling which bears many similarities to diffusion modeling. Stochastic interpolation…
Multi-layer State Evolution Under Random Convolutional Design
Mara Daniels, Cédric Gerbelot, Florent Krzakala +1
Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstructi…
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…
Generator Surgery for Compressed Sensing
Niklas Smedemark-Margulies, Jung Yeon Park, Max Daniels +3
Image recovery from compressive measurements requires a signal prior for the images being reconstructed. Recent work has explored the use of deep generative models with low latent…
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…
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong +2
Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test…