{"total":17,"rows":[{"id":"2412.07081","title":"Sequential Controlled Langevin Diffusions","created":"2024-12-10","cited_by_count":48,"area":null},{"id":"2502.18462","title":"Scalable Equilibrium Sampling with Sequential Boltzmann Generators","created":"2025-02-25","cited_by_count":38,"area":null},{"id":"2305.11463","title":"Generative Sliced MMD Flows with Riesz Kernels","created":"2023-05-19","cited_by_count":37,"area":null},{"id":"2303.15244","title":"Manifold Learning by Mixture Models of VAEs for Inverse Problems","created":"2023-03-27","cited_by_count":17,"area":null},{"id":"2303.15845","title":"Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems","created":"2023-03-28","cited_by_count":13,"area":null},{"id":"2508.18175","title":"Amortized Sampling with Transferable Normalizing Flows","created":"2025-08-25","cited_by_count":12,"area":null},{"id":"2201.08157","title":"WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution","created":"2022-01-20","cited_by_count":10,"area":null},{"id":"2301.11624","title":"Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels","created":"2023-01-27","cited_by_count":9,"area":null},{"id":"2312.16611","title":"Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction","created":"2023-12-27","cited_by_count":9,"area":null},{"id":"2407.20444","title":"Importance Corrected Neural JKO Sampling","created":"2024-07-29","cited_by_count":7,"area":null},{"id":"2211.17158","title":"Proximal Residual Flows for Bayesian Inverse Problems","created":"2022-11-30","cited_by_count":5,"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":"2305.08733","title":"Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics","created":"2023-05-15","cited_by_count":3,"area":null},{"id":"2310.13433","title":"Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances","created":"2023-10-20","cited_by_count":3,"area":null},{"id":"2309.04433","title":"Variations and Relaxations of Normalizing Flows","created":"2023-09-08","cited_by_count":1,"area":null},{"id":"2401.14381","title":"Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs","created":"2024-01-25","cited_by_count":1,"area":null},{"id":"2402.02964","title":"Mixed Noise and Posterior Estimation with Conditional DeepGEM","created":"2024-02-05","cited_by_count":1,"area":null}],"next_cursor":null}