{"total":40,"rows":[{"id":"2103.12828","title":"Learning to Optimize: A Primer and A Benchmark","created":"2021-03-23","cited_by_count":208,"area":null},{"id":"2103.04715","title":"Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie","created":"2021-03-08","cited_by_count":97,"area":null},{"id":"2203.06823","title":"SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation","created":"2022-03-14","cited_by_count":44,"area":null},{"id":"2112.12744","title":"AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis","created":"2021-12-23","cited_by_count":38,"area":null},{"id":"2309.07835","title":"Learning to Warm-Start Fixed-Point Optimization Algorithms","created":"2023-09-14","cited_by_count":32,"area":null},{"id":"2103.01648","title":"Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior","created":"2021-03-02","cited_by_count":24,"area":null},{"id":"2210.01802","title":"Alternating Differentiation for Optimization Layers","created":"2022-10-03","cited_by_count":22,"area":null},{"id":"2112.07464","title":"Efficient differentiable quadratic programming layers: an ADMM approach","created":"2021-12-14","cited_by_count":19,"area":null},{"id":"2104.14090","title":"Feasibility-based Fixed Point Networks","created":"2021-04-29","cited_by_count":17,"area":null},{"id":"2111.13236","title":"Joint inference and input optimization in equilibrium networks","created":"2021-11-25","cited_by_count":15,"area":null},{"id":"2201.06133","title":"On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent","created":"2022-01-16","cited_by_count":14,"area":null},{"id":"2207.00698","title":"Uncertainty Quantification for Deep Unrolling-Based Computational Imaging","created":"2022-07-02","cited_by_count":10,"area":null},{"id":"2202.00264","title":"Graph-based Neural Acceleration for Nonnegative Matrix Factorization","created":"2022-02-01","cited_by_count":9,"area":null},{"id":"2110.00493","title":"Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration","created":"2021-10-01","cited_by_count":7,"area":null},{"id":"2204.13940","title":"PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent","created":"2022-04-29","cited_by_count":7,"area":null},{"id":"2111.05055","title":"MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction","created":"2021-11-09","cited_by_count":6,"area":null},{"id":"2103.04003","title":"Memory-efficient Learning for High-Dimensional MRI Reconstruction","created":"2021-03-06","cited_by_count":5,"area":null},{"id":"2404.13831","title":"Data-Driven Performance Guarantees for Classical and Learned Optimizers","created":"2024-04-22","cited_by_count":5,"area":null},{"id":"2504.19000","title":"Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers","created":"2025-04-26","cited_by_count":4,"area":null},{"id":"2101.04348","title":"Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork","created":"2021-01-12","cited_by_count":3,"area":null},{"id":"2102.11720","title":"Deep Unrolled Network for Video Super-Resolution","created":"2021-02-23","cited_by_count":3,"area":null},{"id":"2104.10611","title":"FourierNets enable the design of highly non-local optical encoders for computational imaging","created":"2021-04-21","cited_by_count":3,"area":null},{"id":"2105.01800","title":"Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives","created":"2021-05-04","cited_by_count":3,"area":null},{"id":"2202.11342","title":"Training Adaptive Reconstruction Networks for Blind Inverse Problems","created":"2022-02-23","cited_by_count":3,"area":null},{"id":"2404.05445","title":"Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation","created":"2024-04-08","cited_by_count":3,"area":null},{"id":"2112.09318","title":"Procedural Kernel Networks","created":"2021-12-17","cited_by_count":2,"area":null},{"id":"2203.04674","title":"Deep learning-based reconstruction of highly accelerated 3D MRI","created":"2022-03-09","cited_by_count":2,"area":null},{"id":"2305.11120","title":"A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems","created":"2023-05-18","cited_by_count":2,"area":null},{"id":"2310.10039","title":"TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds","created":"2023-10-16","cited_by_count":2,"area":null},{"id":"2405.03732","title":"Deep Learning-based Accelerated MR Cholangiopancreatography without Fully-sampled Data","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2410.18794","title":"WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2105.07961","title":"Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy","created":"2021-05-17","cited_by_count":1,"area":null},{"id":"2209.02854","title":"Video Restoration with a Deep Plug-and-Play Prior","created":"2022-09-06","cited_by_count":1,"area":null},{"id":"2402.13106","title":"On Generalization Bounds for Deep Compound Gaussian Neural Networks","created":"2024-02-20","cited_by_count":1,"area":null},{"id":"2501.04608","title":"Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems","created":"2025-01-08","cited_by_count":1,"area":null},{"id":"2103.01449","title":"Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing","created":"2021-03-02","cited_by_count":0,"area":null},{"id":"2104.01889","title":"Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation","created":"2021-04-05","cited_by_count":0,"area":null},{"id":"2402.15335","title":"Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection","created":"2024-02-23","cited_by_count":0,"area":null},{"id":"2508.18975","title":"Understanding Benefits and Pitfalls of Current Methods for the Segmentation of Undersampled MRI Data","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2602.03855","title":"Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging","created":"2026-01-23","cited_by_count":0,"area":null}],"next_cursor":null}