{"total":18,"rows":[{"id":"2305.11463","title":"Generative Sliced MMD Flows with Riesz Kernels","created":"2023-05-19","cited_by_count":37,"area":null},{"id":"2410.12456","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","created":"2024-10-16","cited_by_count":21,"area":null},{"id":"2211.13954","title":"Particle-based Variational Inference with Preconditioned Functional Gradient Flow","created":"2022-11-25","cited_by_count":20,"area":null},{"id":"2301.13737","title":"Self-Consistent Velocity Matching of Probability Flows","created":"2023-01-31","cited_by_count":17,"area":null},{"id":"2306.04952","title":"Entropy-based Training Methods for Scalable Neural Implicit Sampler","created":"2023-06-08","cited_by_count":13,"area":null},{"id":"2310.16516","title":"Particle-based Variational Inference with Generalized Wasserstein Gradient Flow","created":"2023-10-25","cited_by_count":10,"area":null},{"id":"2205.13098","title":"Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization","created":"2022-05-26","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":"2212.00219","title":"Are you using test log-likelihood correctly?","created":"2022-12-01","cited_by_count":8,"area":null},{"id":"2412.03312","title":"Path-Guided Particle-based Sampling","created":"2024-12-04","cited_by_count":8,"area":null},{"id":"2401.02080","title":"Energy based diffusion generator for efficient sampling of Boltzmann distributions","created":"2024-01-04","cited_by_count":7,"area":null},{"id":"2410.10390","title":"Stein Variational Evolution Strategies","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2402.01098","title":"Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent","created":"2024-02-02","cited_by_count":1,"area":null},{"id":"2410.17935","title":"Semi-Implicit Functional Gradient Flow for Efficient Sampling","created":"2024-10-23","cited_by_count":1,"area":null},{"id":"2402.15125","title":"Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding","created":"2024-02-23","cited_by_count":0,"area":null},{"id":"2410.23170","title":"Functional Gradient Flows for Constrained Sampling","created":"2024-10-30","cited_by_count":0,"area":null},{"id":"2411.01453","title":"Denoising Fisher Training For Neural Implicit Samplers","created":"2024-11-03","cited_by_count":0,"area":null},{"id":"2510.02067","title":"Adaptive Kernel Selection for Stein Variational Gradient Descent","created":"2025-10-02","cited_by_count":0,"area":null}],"next_cursor":null}