research
∙
06/29/2022
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
In recent years, there have been significant advances in the use of deep...
share
research
∙
03/08/2022
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems
Generative Adversarial Networks (GANs) have been shown to be powerful an...
research
∙
10/07/2021
Score-based Generative Neural Networks for Large-Scale Optimal Transport
We consider the fundamental problem of sampling the optimal transport co...
research
∙
02/22/2021
Generator Surgery for Compressed Sensing
Image recovery from compressive measurements requires a signal prior for...
research
∙
10/31/2020
Optimal Sample Complexity of Gradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration
We consider the problem of recovering a real-valued n-dimensional signal...
research
∙
08/24/2020
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors
Advances in compressive sensing provided reconstruction algorithms of sp...
research
∙
06/14/2020
Nonasymptotic Guarantees for Low-Rank Matrix Recovery with Generative Priors
Many problems in statistics and machine learning require the reconstruct...
research
∙
06/14/2020
Global Convergence of Sobolev Training for Overparametrized Neural Networks
Sobolev loss is used when training a network to approximate the values a...
research
∙
01/23/2020
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Generative models, such as GANs, learn an explicit low-dimensional repre...
research
∙
06/25/2019
Bilinear Compressed Sensing under known Signs via Convex Programming
We consider the bilinear inverse problem of recovering two vectors, x∈R^...
research
∙
05/29/2019
Global Guarantees for Blind Demodulation with Generative Priors
We study a deep learning inspired formulation for the blind demodulation...
research
∙
05/28/2019
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Trained generative models have shown remarkable performance as priors fo...
research
∙
04/26/2019
Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming
We consider the task of recovering two real or complex m-vectors from ph...
research
∙
10/02/2018
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Deep neural networks, in particular convolutional neural networks, have ...
research
∙
09/22/2018
A convex program for bilinear inversion of sparse vectors
We consider the bilinear inverse problem of recovering two vectors, x∈R^...
research
∙
07/11/2018
Phase Retrieval Under a Generative Prior
The phase retrieval problem asks to recover a natural signal y_0 ∈R^n fr...
research
∙
06/21/2018
Blind Deconvolutional Phase Retrieval via Convex Programming
We consider the task of recovering two real or complex m-vectors from ph...
research
∙
05/22/2018
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior
Deep neural networks provide state-of-the-art performance for image deno...
research
∙
08/07/2016
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
We introduce a new method for location recovery from pair-wise direction...
research
∙
06/04/2015
