{"total":30,"rows":[{"id":"2110.11291","title":"Likelihood Training of Schr\\\"odinger Bridge using Forward-Backward SDEs Theory","created":"2021-10-21","cited_by_count":219,"area":null},{"id":"2110.10873","title":"Controllable and Compositional Generation with Latent-Space Energy-Based Models","created":"2021-10-21","cited_by_count":64,"area":null},{"id":"2305.14777","title":"Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport","created":"2023-05-24","cited_by_count":41,"area":null},{"id":"2305.11463","title":"Generative Sliced MMD Flows with Riesz Kernels","created":"2023-05-19","cited_by_count":37,"area":null},{"id":"2304.13534","title":"A mean-field games laboratory for generative modeling","created":"2023-04-26","cited_by_count":28,"area":null},{"id":"2402.05443","title":"Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport","created":"2024-02-08","cited_by_count":23,"area":null},{"id":"2405.06780","title":"Deep MMD Gradient Flow without adversarial training","created":"2024-05-10","cited_by_count":19,"area":null},{"id":"2311.11475","title":"Gaussian Interpolation Flows","created":"2023-11-20","cited_by_count":17,"area":null},{"id":"2302.01075","title":"MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows","created":"2023-02-02","cited_by_count":13,"area":null},{"id":"2210.16486","title":"Learning Probabilistic Models from Generator Latent Spaces with Hat EBM","created":"2022-10-29","cited_by_count":11,"area":null},{"id":"2108.03702","title":"BIGRoC: Boosting Image Generation via a Robust Classifier","created":"2021-08-08","cited_by_count":9,"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":"2401.14069","title":"Neural Sinkhorn Gradient Flow","created":"2024-01-25","cited_by_count":9,"area":null},{"id":"2305.15577","title":"Minimizing $f$-Divergences by Interpolating Velocity Fields","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2310.02611","title":"Analyzing and Improving Optimal-Transport-based Adversarial Networks","created":"2023-10-04","cited_by_count":8,"area":null},{"id":"2310.20090","title":"Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows","created":"2023-10-31","cited_by_count":8,"area":null},{"id":"2205.02910","title":"GANs as Gradient Flows that Converge","created":"2022-05-05","cited_by_count":5,"area":null},{"id":"2311.00460","title":"Optimal Budgeted Rejection Sampling for Generative Models","created":"2023-11-01","cited_by_count":5,"area":null},{"id":"2309.17357","title":"Module-wise Training of Neural Networks via the Minimizing Movement Scheme","created":"2023-09-29","cited_by_count":4,"area":null},{"id":"2404.09402","title":"Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes","created":"2024-04-15","cited_by_count":4,"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":"2408.16543","title":"Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence","created":"2024-08-29","cited_by_count":3,"area":null},{"id":"2102.11757","title":"EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss","created":"2021-02-23","cited_by_count":2,"area":null},{"id":"2303.03714","title":"Generative Modeling with Flow-Guided Density Ratio Learning","created":"2023-03-07","cited_by_count":2,"area":null},{"id":"2304.13742","title":"TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation","created":"2023-04-26","cited_by_count":2,"area":null},{"id":"2109.03378","title":"Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks","created":"2021-09-08","cited_by_count":1,"area":null},{"id":"2210.00949","title":"Block-wise Training of Residual Networks via the Minimizing Movement Scheme","created":"2022-10-03","cited_by_count":1,"area":null},{"id":"2604.12113","title":"PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation","created":"2026-04-13","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":"2609.32708","title":"One-Step Generative Modeling via Unbalanced Optimal Transport","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}