Accelerated MRI using iterative non-local shrinkage. - Yasir Q. Mohsin; Gregory Ongie; Mathews Jacob (EMBC, 2014)

Higher degree total variation for 3-D image recovery. - Greg Ongie; Yue Hu 003; Mathews Jacob (ISBI, 2014)

Iterative Non-Local Shrinkage Algorithm for MR Image Reconstruction. - Yasir Q. Mohsin; Greg Ongie; Mathews Jacob (CoRR, 2014)

Generalized Higher Degree Total Variation (HDTV) Regularization. - Yue Hu 003; Greg Ongie; Sathish Ramani; Mathews Jacob (IEEE Trans. Image Process., 2014)

Super-resolution MRI using finite rate of innovation curves. - Greg Ongie; Mathews Jacob (ISBI, 2015)

Recovery of Piecewise Smooth Images from Few Fourier Samples. - Greg Ongie; Mathews Jacob (CoRR, 2015)

Recovery of Discontinuous Signals Using Group Sparse Higher Degree Total Variation. - Greg Ongie; Mathews Jacob (IEEE Signal Process. Lett., 2015)

Iterative Shrinkage Algorithm for Patch-Smoothness Regularized Medical Image Recovery. - Yasir Q. Mohsin; Gregory Ongie; Mathews Jacob (IEEE Trans. Medical Imaging, 2015)

Accelerated dynamic MRI using structured low rank matrix completion. - Arvind Balachandrasekaran; Greg Ongie; Mathews Jacob (ICIP, 2016)

Structured low-rank recovery of piecewise constant signals with performance guarantees. - Greg Ongie; Sampurna Biswas; Mathews Jacob (ICIP, 2016)

A fast algorithm for structured low-rank matrix recovery with applications to undersampled MRI reconstruction. - Greg Ongie; Mathews Jacob (ISBI, 2016)

GIRAF: A Fast Algorithm for Structured Low-Rank Matrix Recovery. - Greg Ongie; Mathews Jacob (CoRR, 2016)

Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples. - Greg Ongie; Mathews Jacob (SIAM J. Imaging Sci., 2016)

Enhanced online subspace estimation via adaptive sensing. - Greg Ongie; David Hong; Dejiao Zhang; Laura Balzano (ACSSC, 2017)

Low algebraic dimension matrix completion. - Daniel L. Pimentel-Alarcón; Gregory Ongie; Laura Balzano; Rebecca Willett; Robert D. Nowak (Allerton, 2017)

Online dynamic MRI reconstruction via robust subspace tracking. - Greg Ongie; Saket Dewangan; Jeffrey A. Fessler; Laura Balzano (GlobalSIP, 2017)

Algebraic Variety Models for High-Rank Matrix Completion. - Greg Ongie; Rebecca Willett; Robert D. Nowak; Laura Balzano (ICML, 2017)

Efficient computation of regularized field map estimates in 3D. - Greg Ongie; Junyun Shi; Jeffrey A. Fessler (ISBI, 2017)

Convex recovery of continuous domain piecewise constant images from non-uniform Fourier samples. - Greg Ongie; Sampurna Biswas; Mathews Jacob (CoRR, 2017)

A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recovery. - Gregory Ongie; Mathews Jacob (IEEE Trans. Computational Imaging, 2017)

Online Estimation of Coherent Subspaces with Adaptive Sampling. - Greg Ongie; David Hong; Dejiao Zhang; Laura Balzano (SSP, 2018)

Convex Recovery of Continuous Domain Piecewise Constant Images From Nonuniform Fourier Samples. - Greg Ongie; Sampurna Biswas; Mathews Jacob (IEEE Trans. Signal Process., 2018)

Learned Patch-Based Regularization for Inverse Problems in Imaging. - Davis Gilton; Greg Ongie; Rebecca Willett (CAMSAP, 2019)

Learning to Regularize Using Neumann Networks. - Davis Gilton; Greg Ongie; Rebecca Willett (DSW, 2019)

Neumann Networks for Inverse Problems in Imaging. - Davis Gilton; Greg Ongie; Rebecca Willett (CoRR, 2019)

Streaming Principal Component Analysis From Incomplete Data. - Armin Eftekhari; Gregory Ongie; Laura Balzano; Michael B. Wakin (J. Mach. Learn. Res., 2019)

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. - Greg Ongie; Rebecca Willett; Daniel Soudry; Nathan Srebro (ICLR, 2020)

Supervised learning of model observers for assessment of CT image reconstruction algorithms. - Gregory Ongie; Emil Y. Sidky; Ingrid S. Reiser; Xiaochuan Pan (Image Perception, Observer Performance, and Technology Assessment, 2020)

Deep Learning Techniques for Inverse Problems in Imaging. - Gregory Ongie; Ajil Jalal; Christopher A. Metzler; Richard G. Baraniuk; Alexandros G. Dimakis; Rebecca Willett (IEEE J. Sel. Areas Inf. Theory, 2020)

Neumann Networks for Linear Inverse Problems in Imaging. - Davis Gilton; Greg Ongie; Rebecca Willett (IEEE Trans. Computational Imaging, 2020)

Model Adaptation In Biomedical Image Reconstruction. - Davis Gilton; Greg Ongie; Rebecca Willett (ISBI, 2021)

A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction. - John Paul Phillips; Emil Y. Sidky; Greg Ongie; Weimin Zhou; Juan-Pablo Cruz-Bastida; Ingrid S. Reiser; Mark A. Anastasio; Xiaochuan Pan (Image Perception, Observer Performance, and Technology Assessment, 2021)

Tensor Methods for Nonlinear Matrix Completion. - Greg Ongie; Daniel L. Pimentel-Alarcón; Laura Balzano; Rebecca Willett; Robert D. Nowak (SIAM J. Math. Data Sci., 2021)

Model Adaptation for Inverse Problems in Imaging. - Davis Gilton; Gregory Ongie; Rebecca Willett (IEEE Trans. Computational Imaging, 2021)

Deep Equilibrium Architectures for Inverse Problems in Imaging. - Davis Gilton; Gregory Ongie; Rebecca Willett (IEEE Trans. Computational Imaging, 2021)

Optimizing model observer performance in learning-based CT reconstruction. - Gregory Ongie; Emil Y. Sidky; Ingrid S. Reiser; Xiaochuan Pan (Image Perception, Observer Performance, and Technology Assessment, 2022)

The Role of Linear Layers in Nonlinear Interpolating Networks. - Greg Ongie; Rebecca Willett (CoRR, 2022)

The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks. - Mor Shpigel Nacson; Rotem Mulayoff; Greg Ongie; Tomer Michaeli; Daniel Soudry (ICLR, 2023)

Learning-Based Material Decomposition in Dual Energy CT Using an Unrolled Estimator. - Megan Lantz; Greg Ongie (ISBI, 2023)

How do Minimum-Norm Shallow Denoisers Look in Function Space? - Chen Zeno; Greg Ongie; Yaniv Blumenfeld; Nir Weinberger; Daniel Soudry (NeurIPS, 2023)

Linear Neural Network Layers Promote Learning Single- and Multiple-Index Models. - Suzanna Parkinson; Greg Ongie; Rebecca Willett (CoRR, 2023)

Towards a Sampling Theory for Implicit Neural Representations. - Mahrokh Najaf; Gregory Ongie (IEEECONF, 2024)

Depth Separation in Norm-Bounded Infinite-Width Neural Networks. - Suzanna Parkinson; Greg Ongie; Rebecca Willett; Ohad Shamir; Nathan Srebro (COLT, 2024)

Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function. - Megan Lantz; Emil Y. Sidky; Ingrid S. Reiser; Xiaochuan Pan; Gregory Ongie (CoRR, 2024)

ACcelerated Optimization of Implicit Neural Representations for CT Reconstruction. - Mahrokh Najaf; Gregory Ongie (ISBI, 2025)

ReLU Neural Networks with Linear Layers Are Biased towards Single- and Multi-index Models. - Suzanna Parkinson; Greg Ongie; Rebecca Willett (SIAM J. Math. Data Sci., 2025)

Sampling Theory for Super-Resolution with Implicit Neural Representations. - Mahrokh Najaf; Gregory Ongie (CoRR, 2025)

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets. - Chen Zeno; Hila Manor; Greg Ongie; Nir Weinberger; Tomer Michaeli; Daniel Soudry (CoRR, 2025)