{"total":55,"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":"2206.05431","title":"Learned reconstruction methods with convergence guarantees","created":"2022-06-11","cited_by_count":46,"area":null},{"id":"2211.12461","title":"A Neural-Network-Based Convex Regularizer for Inverse Problems","created":"2022-11-22","cited_by_count":41,"area":null},{"id":"2106.03538","title":"End-to-end reconstruction meets data-driven regularization for inverse problems","created":"2021-06-07","cited_by_count":32,"area":null},{"id":"2308.10542","title":"Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms","created":"2023-08-21","cited_by_count":31,"area":null},{"id":"2106.06513","title":"Learning the optimal Tikhonov regularizer for inverse problems","created":"2021-06-11","cited_by_count":26,"area":null},{"id":"2310.14344","title":"What's in a Prior? Learned Proximal Networks for Inverse Problems","created":"2023-10-22","cited_by_count":25,"area":null},{"id":"2210.16222","title":"Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions","created":"2022-10-28","cited_by_count":24,"area":null},{"id":"2111.13613","title":"The Geometry of Adversarial Training in Binary Classification","created":"2021-11-26","cited_by_count":20,"area":null},{"id":"2104.14090","title":"Feasibility-based Fixed Point Networks","created":"2021-04-29","cited_by_count":17,"area":null},{"id":"2111.12187","title":"Input Convex Gradient Networks","created":"2021-11-23","cited_by_count":17,"area":null},{"id":"2309.01949","title":"Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior","created":"2023-09-05","cited_by_count":17,"area":"diffusion"},{"id":"2405.19097","title":"A study of why we need to reassess full reference image quality assessment with medical images","created":"2024-05-29","cited_by_count":17,"area":null},{"id":"2303.07271","title":"Provably Convergent Plug-and-Play Quasi-Newton Methods","created":"2023-03-09","cited_by_count":15,"area":null},{"id":"2510.01755","title":"Learning Regularization Functionals for Inverse Problems: A Comparative Study","created":"2025-10-02","cited_by_count":15,"area":null},{"id":"2402.01052","title":"Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation","created":"2024-02-01","cited_by_count":14,"area":null},{"id":"2307.09441","title":"Convergent regularization in inverse problems and linear plug-and-play denoisers","created":"2023-07-18","cited_by_count":13,"area":null},{"id":"2110.12520","title":"Learning convex regularizers satisfying the variational source condition for inverse problems","created":"2021-10-24","cited_by_count":8,"area":null},{"id":"2301.04764","title":"Analyzing Inexact Hypergradients for Bilevel Learning","created":"2023-01-11","cited_by_count":8,"area":null},{"id":"2304.01963","title":"Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography","created":"2023-04-04","cited_by_count":6,"area":null},{"id":"2305.18394","title":"On Optimal Regularization Parameters via Bilevel Learning","created":"2023-05-28","cited_by_count":6,"area":null},{"id":"2412.08350","title":"Benchmarking learned algorithms for computed tomography image reconstruction tasks","created":"2024-12-11","cited_by_count":6,"area":null},{"id":"2211.13452","title":"Deep unfolding as iterative regularization for imaging inverse problems","created":"2022-11-24","cited_by_count":5,"area":null},{"id":"2406.12289","title":"Stability of Data-Dependent Ridge-Regularization for Inverse Problems","created":"2024-06-18","cited_by_count":5,"area":null},{"id":"2505.06549","title":"Good Things Come in Pairs: Paired Autoencoders for Inverse Problems","created":"2025-05-10","cited_by_count":5,"area":null},{"id":"2505.21208","title":"Input Convex Kolmogorov Arnold Networks","created":"2025-05-27","cited_by_count":5,"area":null},{"id":"2110.10093","title":"Stochastic Primal-Dual Deep Unrolling","created":"2021-10-19","cited_by_count":4,"area":null},{"id":"2304.08342","title":"NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems","created":"2023-04-17","cited_by_count":4,"area":null},{"id":"2308.09460","title":"Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling","created":"2023-08-18","cited_by_count":4,"area":null},{"id":"2308.10098","title":"An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning","created":"2023-08-19","cited_by_count":4,"area":null},{"id":"2310.05812","title":"Provably Convergent Data-Driven Convex-Nonconvex Regularization","created":"2023-10-09","cited_by_count":4,"area":null},{"id":"2311.08972","title":"Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging","created":"2023-11-15","cited_by_count":4,"area":null},{"id":"2108.13993","title":"Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2309.12128","title":"Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems","created":"2023-09-21","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":"2408.16852","title":"The Star Geometry of Critic-Based Regularizer Learning","created":"2024-08-29","cited_by_count":3,"area":null},{"id":"2410.12441","title":"A primal-dual algorithm for image reconstruction with input-convex neural network regularizers","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2412.16031","title":"Learning sparsity-promoting regularizers for linear inverse problems","created":"2024-12-20","cited_by_count":3,"area":null},{"id":"2509.20511","title":"A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm","created":"2025-09-24","cited_by_count":3,"area":"diffusion"},{"id":"2208.03360","title":"Deep Learning for Material Decomposition in Photon-Counting CT","created":"2022-08-05","cited_by_count":2,"area":null},{"id":"2303.01965","title":"A Lifted Bregman Formulation for the Inversion of Deep Neural Networks","created":"2023-03-01","cited_by_count":2,"area":null},{"id":"2309.09250","title":"Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems","created":"2023-09-17","cited_by_count":2,"area":null},{"id":"2409.13482","title":"Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties","created":"2024-09-20","cited_by_count":2,"area":null},{"id":"2412.06436","title":"An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation","created":"2024-12-09","cited_by_count":2,"area":null},{"id":"2301.10260","title":"Learned Interferometric Imaging for the SPIDER Instrument","created":"2023-01-24","cited_by_count":1,"area":null},{"id":"2312.14078","title":"Learned reconstruction methods for inverse problems: sample error estimates","created":"2023-12-21","cited_by_count":1,"area":null},{"id":"2402.12072","title":"Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview","created":"2024-02-19","cited_by_count":1,"area":null},{"id":"2503.19190","title":"Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers","created":"2025-03-24","cited_by_count":1,"area":null},{"id":"2510.03464","title":"Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints","created":"2025-10-03","cited_by_count":1,"area":null},{"id":"2510.12521","title":"Why the noise model matters: A performance gap in learned regularization","created":"2025-10-14","cited_by_count":1,"area":null},{"id":"2305.01758","title":"Adversarial Generative NMF for Single Channel Source Separation","created":"2023-04-24","cited_by_count":0,"area":null},{"id":"2404.07361","title":"Gradient Networks","created":"2024-04-10","cited_by_count":0,"area":null},{"id":"2502.03302","title":"MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2610.09310","title":"The Geometry of Anisotropic Dilation for Optimal Regularization","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}