{"total":36,"rows":[{"id":"2211.01364","title":"An optimal control perspective on diffusion-based generative modeling","created":"2022-11-02","cited_by_count":147,"area":"diffusion"},{"id":"2306.15030","title":"Equivariant flow matching","created":"2023-06-26","cited_by_count":135,"area":"diffusion"},{"id":"2402.04845","title":"AlphaFold Meets Flow Matching for Generating Protein Ensembles","created":"2024-02-07","cited_by_count":110,"area":"diffusion"},{"id":"2503.02819","title":"Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts","created":"2025-03-04","cited_by_count":96,"area":"diffusion"},{"id":"2504.11713","title":"Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching","created":"2025-04-16","cited_by_count":85,"area":"diffusion"},{"id":"2502.00361","title":"Efficient Online Reinforcement Learning for Diffusion Policy","created":"2025-02-01","cited_by_count":71,"area":"diffusion"},{"id":"2311.01797","title":"On the Generalization Properties of Diffusion Models","created":"2023-11-03","cited_by_count":68,"area":"diffusion"},{"id":"2402.06320","title":"Particle Denoising Diffusion Sampler","created":"2024-02-09","cited_by_count":65,"area":"diffusion"},{"id":"2506.17139","title":"Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models","created":"2025-06-20","cited_by_count":23,"area":"diffusion"},{"id":"2506.22565","title":"Adjoint Schr\\\"odinger Bridge Sampler","created":"2025-06-27","cited_by_count":21,"area":"diffusion"},{"id":"2508.10684","title":"MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control","created":"2025-08-14","cited_by_count":21,"area":"diffusion"},{"id":"2501.06148","title":"From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training","created":"2025-01-10","cited_by_count":19,"area":"diffusion"},{"id":"2502.06999","title":"Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models","created":"2025-02-10","cited_by_count":17,"area":"diffusion"},{"id":"2506.05668","title":"RNE: plug-and-play diffusion inference-time control and energy-based training","created":"2025-06-06","cited_by_count":17,"area":"diffusion"},{"id":"2408.16249","title":"Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities","created":"2024-08-29","cited_by_count":13,"area":"diffusion"},{"id":"2506.05231","title":"Progressive Tempering Sampler with Diffusion","created":"2025-06-05","cited_by_count":13,"area":"diffusion"},{"id":"2505.19552","title":"On scalable and efficient training of diffusion samplers","created":"2025-05-26","cited_by_count":10,"area":"diffusion"},{"id":"2603.00530","title":"Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching","created":"2026-02-28","cited_by_count":10,"area":"diffusion"},{"id":"2405.14392","title":"Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows","created":"2024-05-23","cited_by_count":9,"area":"diffusion"},{"id":"2410.19449","title":"Learned Reference-based Diffusion Sampling for multi-modal distributions","created":"2024-10-25","cited_by_count":9,"area":"diffusion"},{"id":"2506.18165","title":"Non-equilibrium Annealed Adjoint Sampler","created":"2025-06-22","cited_by_count":8,"area":"diffusion"},{"id":"2509.03726","title":"Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling","created":"2025-09-03","cited_by_count":7,"area":"diffusion"},{"id":"2502.04468","title":"Iterative Importance Fine-tuning of Diffusion Models","created":"2025-02-06","cited_by_count":6,"area":"diffusion"},{"id":"2505.21005","title":"Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models","created":"2025-05-27","cited_by_count":6,"area":"diffusion"},{"id":"2506.01541","title":"Adaptive Destruction Processes for Diffusion Samplers","created":"2025-06-02","cited_by_count":6,"area":"diffusion"},{"id":"2512.20003","title":"Control Variate Score Matching for Diffusion Models","created":"2025-12-23","cited_by_count":6,"area":"diffusion"},{"id":"2406.14040","title":"A Practical Diffusion Path for Sampling","created":"2024-06-20","cited_by_count":5,"area":"diffusion"},{"id":"2605.03984","title":"Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes","created":"2026-05-05","cited_by_count":5,"area":"diffusion"},{"id":"2503.00524","title":"End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler","created":"2025-03-01","cited_by_count":4,"area":"diffusion"},{"id":"2505.10139","title":"Path Gradients after Flow Matching","created":"2025-05-15","cited_by_count":3,"area":"diffusion"},{"id":"2505.19431","title":"Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage","created":"2025-05-26","cited_by_count":2,"area":"diffusion"},{"id":"2602.05961","title":"Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces","created":"2026-02-05","cited_by_count":2,"area":"diffusion"},{"id":"2508.20646","title":"VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillation","created":"2025-08-28","cited_by_count":1,"area":"diffusion"},{"id":"2605.04013","title":"Conditional Diffusion Sampling","created":"2026-05-05","cited_by_count":1,"area":"diffusion"},{"id":"2503.14569","title":"Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance","created":"2025-03-18","cited_by_count":0,"area":"diffusion"},{"id":"2607.06841","title":"Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling","created":"2026-07-07","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}