{"total":117,"rows":[{"id":"2206.13397","title":"Generative Modelling With Inverse Heat Dissipation","created":"2022-06-21","cited_by_count":167,"area":null},{"id":"2305.19256","title":"Ambient Diffusion: Learning Clean Distributions from Corrupted Data","created":"2023-05-30","cited_by_count":111,"area":null},{"id":"2206.07632","title":"Exploring Chemical Space with Score-based Out-of-distribution Generation","created":"2022-06-06","cited_by_count":95,"area":null},{"id":"2404.09636","title":"All-in-one simulation-based inference","created":"2024-04-15","cited_by_count":74,"area":null},{"id":"2203.12621","title":"MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion","created":"2022-03-23","cited_by_count":67,"area":null},{"id":"2201.09376","title":"ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer","created":"2022-01-23","cited_by_count":52,"area":null},{"id":"2310.10835","title":"Provable Probabilistic Imaging using Score-Based Generative Priors","created":"2023-10-16","cited_by_count":51,"area":null},{"id":"2409.06219","title":"Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning","created":"2024-09-10","cited_by_count":35,"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":"2306.07233","title":"Generative Plug and Play: Posterior Sampling for Inverse Problems","created":"2023-06-12","cited_by_count":24,"area":null},{"id":"2210.13834","title":"Stable Deep MRI Reconstruction using Generative Priors","created":"2022-10-25","cited_by_count":21,"area":null},{"id":"2411.02780","title":"How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion","created":"2024-11-05","cited_by_count":18,"area":null},{"id":"2402.01779","title":"Plug-and-Play image restoration with Stochastic deNOising REgularization","created":"2024-02-01","cited_by_count":17,"area":null},{"id":"2405.04235","title":"LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning","created":"2024-05-07","cited_by_count":16,"area":null},{"id":"2307.13220","title":"One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction","created":"2023-07-25","cited_by_count":15,"area":null},{"id":"2308.04682","title":"Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising","created":"2023-08-09","cited_by_count":15,"area":null},{"id":"2204.07664","title":"Conditional Injective Flows for Bayesian Imaging","created":"2022-04-15","cited_by_count":13,"area":null},{"id":"2403.10064","title":"Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI","created":"2024-03-15","cited_by_count":13,"area":null},{"id":"2406.04239","title":"Solving Inverse Problems in Protein Space Using Diffusion-Based Priors","created":"2024-06-06","cited_by_count":13,"area":null},{"id":"2407.08256","title":"Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling","created":"2024-07-11","cited_by_count":13,"area":null},{"id":"2208.07181","title":"One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction","created":"2022-08-15","cited_by_count":12,"area":null},{"id":"2204.12007","title":"Assessing the ability of generative adversarial networks to learn canonical medical image statistics","created":"2022-04-26","cited_by_count":11,"area":null},{"id":"2206.09104","title":"Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems","created":"2022-06-18","cited_by_count":11,"area":null},{"id":"2306.00945","title":"CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions","created":"2023-06-01","cited_by_count":11,"area":null},{"id":"2310.00133","title":"Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis","created":"2023-09-29","cited_by_count":11,"area":null},{"id":"2405.12202","title":"Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution","created":"2024-05-20","cited_by_count":11,"area":null},{"id":"2306.01630","title":"A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging","created":"2023-06-02","cited_by_count":10,"area":null},{"id":"2308.02631","title":"Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction","created":"2023-08-04","cited_by_count":10,"area":null},{"id":"2312.10271","title":"Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data","created":"2023-12-16","cited_by_count":10,"area":null},{"id":"2203.10078","title":"Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors","created":"2022-03-18","cited_by_count":9,"area":null},{"id":"2210.13389","title":"A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems","created":"2022-10-24","cited_by_count":9,"area":null},{"id":"2305.00608","title":"Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression","created":"2023-05-01","cited_by_count":9,"area":null},{"id":"2403.01124","title":"Text-guided Explorable Image Super-resolution","created":"2024-03-02","cited_by_count":9,"area":null},{"id":"2404.15692","title":"Deep Learning for Accelerated and Robust MRI Reconstruction: a Review","created":"2024-04-24","cited_by_count":9,"area":null},{"id":"2111.08177","title":"Score-Based Generative Models for Robust Channel Estimation","created":"2021-11-16","cited_by_count":8,"area":null},{"id":"2205.03883","title":"WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction","created":"2022-05-08","cited_by_count":8,"area":null},{"id":"2207.09340","title":"A coherence parameter characterizing generative compressed sensing with Fourier measurements","created":"2022-07-19","cited_by_count":8,"area":null},{"id":"2207.13200","title":"Deep Model-Based Architectures for Inverse Problems under Mismatched Priors","created":"2022-07-26","cited_by_count":8,"area":null},{"id":"2405.18527","title":"Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction","created":"2024-05-28","cited_by_count":8,"area":null},{"id":"2411.13443","title":"Nonlinear Assimilation via Score-based Sequential Langevin Sampling","created":"2024-11-20","cited_by_count":8,"area":null},{"id":"2412.09331","title":"Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction","created":"2024-12-12","cited_by_count":8,"area":null},{"id":"2506.04490","title":"Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data","created":"2025-06-04","cited_by_count":8,"area":null},{"id":"2112.09061","title":"Solving Inverse Problems with NerfGANs","created":"2021-12-16","cited_by_count":7,"area":null},{"id":"2310.03758","title":"A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing","created":"2023-09-25","cited_by_count":7,"area":null},{"id":"2410.16290","title":"A Unified Model for Compressed Sensing MRI Across Undersampling Patterns","created":"2024-10-05","cited_by_count":7,"area":null},{"id":"2210.13983","title":"Optimization for Amortized Inverse Problems","created":"2022-10-25","cited_by_count":6,"area":null},{"id":"2305.05014","title":"Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion","created":"2023-05-08","cited_by_count":6,"area":null},{"id":"2307.05352","title":"Leveraging Variational Autoencoders for Parameterized MMSE Estimation","created":"2023-07-11","cited_by_count":6,"area":null},{"id":"2407.18026","title":"Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions","created":"2024-07-25","cited_by_count":6,"area":null},{"id":"2205.12445","title":"Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation","created":"2022-05-25","cited_by_count":5,"area":null},{"id":"2305.10732","title":"BlindHarmony: \"Blind\" Harmonization for MR Images via Flow model","created":"2023-05-18","cited_by_count":5,"area":null},{"id":"2309.16831","title":"Propagation and Attribution of Uncertainty in Medical Imaging Pipelines","created":"2023-09-28","cited_by_count":5,"area":null},{"id":"2502.04521","title":"Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction","created":"2025-02-06","cited_by_count":5,"area":null},{"id":"2507.15397","title":"MAP Estimation with Denoisers: Convergence Rates and Guarantees","created":"2025-07-21","cited_by_count":5,"area":null},{"id":"2203.10776","title":"K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction","created":"2022-03-21","cited_by_count":4,"area":null},{"id":"2210.14586","title":"Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance","created":"2022-10-26","cited_by_count":4,"area":null},{"id":"2302.11173","title":"VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems","created":"2023-02-22","cited_by_count":4,"area":null},{"id":"2404.04550","title":"NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimation","created":"2024-04-06","cited_by_count":4,"area":null},{"id":"2405.14327","title":"Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI","created":"2024-05-23","cited_by_count":4,"area":null},{"id":"2411.12919","title":"Robust multi-coil MRI reconstruction via self-supervised denoising","created":"2024-11-19","cited_by_count":4,"area":null},{"id":"2502.14009","title":"Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction","created":"2025-02-19","cited_by_count":4,"area":null},{"id":"2505.24136","title":"Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction","created":"2025-05-30","cited_by_count":4,"area":null},{"id":"2510.21417","title":"Self-diffusion for Solving Inverse Problems","created":"2025-10-24","cited_by_count":4,"area":null},{"id":"2202.05311","title":"Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems","created":"2022-02-10","cited_by_count":3,"area":null},{"id":"2209.00462","title":"MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation","created":"2022-08-31","cited_by_count":3,"area":null},{"id":"2308.02340","title":"Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation","created":"2023-08-04","cited_by_count":3,"area":null},{"id":"2404.15274","title":"Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction","created":"2024-04-23","cited_by_count":3,"area":null},{"id":"2405.02958","title":"Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction","created":"2024-05-05","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":"2412.04639","title":"Multi-dynamic deep image prior for cardiac MRI","created":"2024-12-05","cited_by_count":3,"area":null},{"id":"2511.17038","title":"DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing","created":"2025-11-21","cited_by_count":3,"area":null},{"id":"2603.13439","title":"Bayesian Uncertainty-Aware MRI Reconstruction","created":"2026-03-13","cited_by_count":3,"area":null},{"id":"2110.00075","title":"Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning","created":"2021-09-30","cited_by_count":2,"area":null},{"id":"2307.04946","title":"DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization","created":"2023-07-11","cited_by_count":2,"area":null},{"id":"2402.03383","title":"MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction","created":"2024-02-04","cited_by_count":2,"area":null},{"id":"2402.04646","title":"Block Sparse Bayesian Learning: A Diversified Scheme","created":"2024-02-07","cited_by_count":2,"area":null},{"id":"2405.05564","title":"Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2409.09370","title":"MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI","created":"2024-09-14","cited_by_count":2,"area":null},{"id":"2411.00605","title":"pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization","created":"2024-11-01","cited_by_count":2,"area":null},{"id":"2411.04995","title":"LoFi: Neural Local Fields for Scalable Image Reconstruction","created":"2024-11-07","cited_by_count":2,"area":null},{"id":"2411.13022","title":"Fast MRI for All: Bridging Access Gaps by Training without Raw Data","created":"2024-11-20","cited_by_count":2,"area":null},{"id":"2505.09528","title":"Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems","created":"2025-05-14","cited_by_count":2,"area":null},{"id":"2505.10630","title":"How many measurements are enough? Bayesian recovery in inverse problems with general distributions","created":"2025-05-15","cited_by_count":2,"area":null},{"id":"2507.00670","title":"Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions","created":"2025-07-01","cited_by_count":2,"area":null},{"id":"2510.09676","title":"Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling","created":"2025-10-08","cited_by_count":2,"area":null},{"id":"2510.16004","title":"PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction","created":"2025-10-14","cited_by_count":2,"area":null},{"id":"2510.16321","title":"Time-Embedded Algorithm Unrolling for Computational MRI","created":"2025-10-18","cited_by_count":2,"area":null},{"id":"2601.17374","title":"Error Analysis of Bayesian Inverse Problems with Generative Priors","created":"2026-01-24","cited_by_count":2,"area":null},{"id":"2605.16396","title":"Beyond MMSE: Enhancing PnP Restoration with ProxiMAP","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2203.04382","title":"Regularized Training of Intermediate Layers for Generative Models for Inverse Problems","created":"2022-03-08","cited_by_count":1,"area":null},{"id":"2212.05503","title":"Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction","created":"2022-12-11","cited_by_count":1,"area":null},{"id":"2309.13385","title":"Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement","created":"2023-09-23","cited_by_count":1,"area":null},{"id":"2312.04327","title":"Learning to sample in Cartesian MRI","created":"2023-12-07","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":"2407.00537","title":"Accelerating MRI with Longitudinally-informed Latent Posterior Sampling","created":"2024-06-29","cited_by_count":1,"area":null},{"id":"2502.20877","title":"Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty","created":"2025-02-28","cited_by_count":1,"area":null},{"id":"2505.02007","title":"Efficient Noise Calculation in Deep Learning-based MRI Reconstructions","created":"2025-05-04","cited_by_count":1,"area":null},{"id":"2512.17137","title":"SDUM: A Scalable Deep Unrolled Model for Universal MRI Reconstruction","created":"2025-12-19","cited_by_count":1,"area":null},{"id":"2601.18728","title":"Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data","created":"2026-01-26","cited_by_count":1,"area":null},{"id":"2605.30573","title":"Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems","created":"2026-05-28","cited_by_count":1,"area":null}],"next_cursor":"100"}