{"total":28,"rows":[{"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":"2502.06079","title":"Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo","created":"2025-02-10","cited_by_count":31,"area":"diffusion"},{"id":"2506.16471","title":"Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities","created":"2025-06-19","cited_by_count":26,"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":"2512.21829","title":"Tilt Matching for Scalable Sampling and Fine-Tuning","created":"2025-12-26","cited_by_count":22,"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":"2506.03979","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","created":"2025-06-04","cited_by_count":18,"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":"2512.05116","title":"Value Gradient Guidance for Flow Matching Alignment","created":"2025-12-04","cited_by_count":14,"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":"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":"2509.26364","title":"Data-to-Energy Stochastic Dynamics","created":"2025-09-30","cited_by_count":6,"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":"2605.23346","title":"Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion","created":"2026-05-22","cited_by_count":5,"area":"diffusion"},{"id":"2605.18745","title":"SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate","created":"2026-05-18","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":"2602.04928","title":"Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics","created":"2026-02-04","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":"2509.00316","title":"Continuously Tempered Diffusion Samplers","created":"2025-08-30","cited_by_count":1,"area":"diffusion"},{"id":"2605.04013","title":"Conditional Diffusion Sampling","created":"2026-05-05","cited_by_count":1,"area":"diffusion"},{"id":"2609.35947","title":"FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models","created":"2026-09-28","cited_by_count":1,"area":"diffusion"},{"id":"2605.00337","title":"FES-FM: Free Energy Surface Sampling via Reduced Flow Matching","created":"2026-05-01","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}