7 citations · 9 across the 3 of their papers we have counts for
6 papers
Misspecified Phase Retrieval with Generative Priors
Zhaoqiang Liu, Xinshao Wang, Jiulong Liu
In this paper, we study phase retrieval under model misspecification and generative priors. In particular, we aim to estimate an -dimensional signal from i.i.d.…
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
Zhaoqiang Liu, Jun Han
In this paper, we propose projected gradient descent (PGD) algorithms for signal estimation from noisy nonlinear measurements. We assume that the unknown -dimensional signal lie…
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett
Compressive phase retrieval is a popular variant of the standard compressive sensing problem in which the measurements only contain magnitude information. In this paper, motivated…
The Generalized Lasso with Nonlinear Observations and Generative Priors
Zhaoqiang Liu, Jonathan Scarlett
In this paper, we study the problem of signal estimation from noisy non-linear measurements when the unknown -dimensional signal is in the range of an -Lipschitz continuous g…
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari +1
The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of t…
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models
Zhaoqiang Liu, Jonathan Scarlett
It has recently been shown that for compressive sensing, significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption the unknown vecto…