Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning
Diffusion models have proven to be highly effective in generating high-quality images.
However, adapting large pre-trained diffusion models to new domains remains an open …
However, adapting large pre-trained diffusion models to new domains remains an open …
Theoretical perspectives on deep learning methods in inverse problems
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-resolution …
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …
Non-iterative recovery from nonlinear observations using generative models
In this paper, we aim to estimate the direction of an underlying signal from its nonlinear
observations following the semi-parametric single index model (SIM). Unlike for …
observations following the semi-parametric single index model (SIM). Unlike for …
Unsupervised deep learning for phase retrieval via teacher-student distillation
Phase retrieval (PR) is a challenging nonlinear inverse problem in scientific imaging that
involves reconstructing the phase of a signal from its intensity measurements. Recently …
involves reconstructing the phase of a signal from its intensity measurements. Recently …
Uniform Recovery Guarantees for Quantized Corrupted Sensing Using Structured or Generative Priors
This paper studies quantized corrupted sensing where the measurements are contaminated
by unknown corruption and then quantized by a dithered uniform quantizer. We establish …
by unknown corruption and then quantized by a dithered uniform quantizer. We establish …
Misspecified phase retrieval with generative priors
In this paper, we study phase retrieval under model misspecification and generative priors.
In particular, we aim to estimate an $ n $-dimensional signal $\mathbf {x} $ from $ m $ iid …
In particular, we aim to estimate an $ n $-dimensional signal $\mathbf {x} $ from $ m $ iid …
Projected gradient descent algorithms for solving nonlinear inverse problems with generative priors
In this paper, we propose projected gradient descent (PGD) algorithms for signal estimation
from noisy nonlinear measurements. We assume that the unknown $ p $-dimensional signal …
from noisy nonlinear measurements. We assume that the unknown $ p $-dimensional signal …
Solving quadratic systems with full-rank matrices using sparse or generative priors
The problem of recovering a signal $\boldsymbol x\in\mathbb {R}^ n $ from a quadratic
system $\{y_i=\boldsymbol x^\top\boldsymbol A_i\boldsymbol x,\i= 1,\ldots, m\} $ with full …
system $\{y_i=\boldsymbol x^\top\boldsymbol A_i\boldsymbol x,\i= 1,\ldots, m\} $ with full …
Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors
In this work, we focus on high-dimensional single index models with non-Gaussian sensing
vectors and generative priors. More specifically, our goal is to estimate the underlying signal …
vectors and generative priors. More specifically, our goal is to estimate the underlying signal …
Do algorithms and barriers for sparse principal component analysis extend to other structured settings?
We study a principal component analysis problem under the spiked Wishart model in which
the structure in the signal is captured by a class of union-of-subspace models. This general …
the structure in the signal is captured by a class of union-of-subspace models. This general …