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Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priors
Namhoon Kim, Sara Fridovich-Keil
Generative models have shown strong potential as data-driven priors for solving inverse problems such as reconstructing medical images from undersampled measurements. While these p…
Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction
Rohan Sanda, Asad Aali, Andrew Johnston +3
Magnetic resonance imaging (MRI) requires long acquisition times, raising costs, reducing accessibility, and making scans more susceptible to motion artifacts. Diffusion probabilis…
A Recovery Guarantee for Sparse Neural Networks
Sara Fridovich-Keil, Mert Pilanci
We prove the first guarantees of sparse recovery for ReLU neural networks, where the sparse network weights constitute the signal to be recovered. Specifically, we study structural…
Grids Often Outperform Implicit Neural Representations at Compressing Dense Signals
Namhoon Kim, Sara Fridovich-Keil
Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We in…
Accurate, provable and fast polychromatic tomographic reconstruction: A variational inequality approach
Mengqi Lou, Kabir Aladin Verchand, Sara Fridovich-Keil +1
We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ra…