{"total":22,"rows":[{"id":"2405.06780","title":"Deep MMD Gradient Flow without adversarial training","created":"2024-05-10","cited_by_count":19,"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":"2401.08260","title":"Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms","created":"2024-01-16","cited_by_count":8,"area":null},{"id":"2407.20444","title":"Importance Corrected Neural JKO Sampling","created":"2024-07-29","cited_by_count":7,"area":null},{"id":"2408.07498","title":"Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functions","created":"2024-08-14","cited_by_count":7,"area":null},{"id":"2410.01316","title":"Fast Summation of Radial Kernels via QMC Slicing","created":"2024-10-02","cited_by_count":7,"area":null},{"id":"2504.08544","title":"Slicing the Gaussian Mixture Wasserstein Distance","created":"2025-04-11","cited_by_count":7,"area":null},{"id":"2605.11884","title":"Sobolev Regularized MMD Gradient Flow","created":"2026-05-12","cited_by_count":6,"area":null},{"id":"2411.00214","title":"Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics","created":"2024-10-31","cited_by_count":4,"area":null},{"id":"2503.01140","title":"DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows","created":"2025-03-03","cited_by_count":4,"area":null},{"id":"2607.18198","title":"Three-Body Scattering for Generative Modeling","created":"2026-07-20","cited_by_count":4,"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":"2504.07820","title":"Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows","created":"2025-04-10","cited_by_count":2,"area":null},{"id":"2402.02964","title":"Mixed Noise and Posterior Estimation with Conditional DeepGEM","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2410.02628","title":"Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2410.08894","title":"Conditional Generative Models for Contrast-Enhanced Synthesis of T1w and T1 Maps in Brain MRI","created":"2024-10-11","cited_by_count":0,"area":null},{"id":"2502.05349","title":"Contextual Scenario Generation for Two-Stage Stochastic Programming","created":"2025-02-07","cited_by_count":0,"area":null},{"id":"2502.07650","title":"Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold","created":"2025-02-11","cited_by_count":0,"area":null},{"id":"2509.25507","title":"One-shot Conditional Sampling: MMD meets Nearest Neighbors","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2602.20758","title":"Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2606.27767","title":"Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2609.27008","title":"Sharp Convergence of Wasserstein Gradient Flows for Spectrally Nonnegative Interaction Energies","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}