{"total":19,"rows":[{"id":"2304.14772","title":"Multisample Flow Matching: Straightening Flows with Minibatch Couplings","created":"2023-04-28","cited_by_count":278,"area":"diffusion"},{"id":"2503.07565","title":"Inductive Moment Matching","created":"2025-03-10","cited_by_count":113,"area":"diffusion"},{"id":"2301.13362","title":"Optimizing DDPM Sampling with Shortcut Fine-Tuning","created":"2023-01-31","cited_by_count":81,"area":"diffusion"},{"id":"2410.14919","title":"Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step","created":"2024-10-19","cited_by_count":47,"area":"diffusion"},{"id":"2502.02483","title":"Distributional Diffusion Models with Scoring Rules","created":"2025-02-04","cited_by_count":25,"area":"diffusion"},{"id":"2307.08283","title":"Complexity Matters: Rethinking the Latent Space for Generative Modeling","created":"2023-07-17","cited_by_count":19,"area":"diffusion"},{"id":"2303.12281","title":"Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models","created":"2023-03-22","cited_by_count":11,"area":"diffusion"},{"id":"2603.07514","title":"A Unified View of Score-Based and Drifting Models","created":"2026-03-08","cited_by_count":11,"area":"diffusion"},{"id":"2301.07969","title":"Fast Inference in Denoising Diffusion Models via MMD Finetuning","created":"2023-01-19","cited_by_count":9,"area":"diffusion"},{"id":"2306.06138","title":"Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models","created":"2023-06-09","cited_by_count":7,"area":"diffusion"},{"id":"2303.05456","title":"Restoration based Generative Models","created":"2023-02-20","cited_by_count":6,"area":"diffusion"},{"id":"2404.12314","title":"Guided Discrete Diffusion for Electronic Health Record Generation","created":"2024-04-18","cited_by_count":5,"area":"diffusion"},{"id":"2608.11205","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","created":"2026-08-11","cited_by_count":4,"area":"diffusion"},{"id":"2601.08379","title":"MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance","created":"2026-01-13","cited_by_count":3,"area":"diffusion"},{"id":"2401.14111","title":"Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs","created":"2024-01-25","cited_by_count":2,"area":"diffusion"},{"id":"2306.01654","title":"Insights into Closed-form IPM-GAN Discriminator Guidance for Diffusion Modeling","created":"2023-06-02","cited_by_count":0,"area":"diffusion"},{"id":"2505.04992","title":"Boosting Statistic Learning with Synthetic Data from Pretrained Large Models","created":"2025-05-08","cited_by_count":0,"area":"diffusion"},{"id":"2605.09439","title":"Inverse Design for Conditional Distribution Matching","created":"2026-05-10","cited_by_count":0,"area":"diffusion"},{"id":"2607.06027","title":"Fr\\'echet Distance Loss on Speech Representations for Text-to-Speech Synthesis","created":"2026-07-07","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}