{"total":29,"rows":[{"id":"2402.16991","title":"A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data","created":"2024-02-26","cited_by_count":93,"area":"diffusion"},{"id":"2404.07771","title":"An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization","created":"2024-04-11","cited_by_count":73,"area":"diffusion"},{"id":"2403.11968","title":"Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory","created":"2024-03-18","cited_by_count":63,"area":"diffusion"},{"id":"2404.14743","title":"Gradient Guidance for Diffusion Models: An Optimization Perspective","created":"2024-04-23","cited_by_count":61,"area":"diffusion"},{"id":"2403.01639","title":"Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models","created":"2024-03-03","cited_by_count":46,"area":"diffusion"},{"id":"2404.18893","title":"Learning general Gaussian mixtures with efficient score matching","created":"2024-04-29","cited_by_count":44,"area":"diffusion"},{"id":"2401.15604","title":"Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization","created":"2024-01-28","cited_by_count":42,"area":"diffusion"},{"id":"2404.18869","title":"Learning Mixtures of Gaussians Using Diffusion Models","created":"2024-04-29","cited_by_count":42,"area":"diffusion"},{"id":"2405.15986","title":"Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity","created":"2024-05-24","cited_by_count":42,"area":"diffusion"},{"id":"2501.12982","title":"Low-dimensional adaptation of diffusion models: Convergence in total variation","created":"2025-01-22","cited_by_count":39,"area":"diffusion"},{"id":"2410.04760","title":"Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models","created":"2024-10-07","cited_by_count":35,"area":"diffusion"},{"id":"2401.13115","title":"Contractive Diffusion Probabilistic Models","created":"2024-01-23","cited_by_count":29,"area":"diffusion"},{"id":"2502.12089","title":"How Compositional Generalization and Creativity Improve as Diffusion Models are Trained","created":"2025-02-17","cited_by_count":25,"area":"diffusion"},{"id":"2403.13219","title":"Diffusion Model for Data-Driven Black-Box Optimization","created":"2024-03-20","cited_by_count":24,"area":"diffusion"},{"id":"2504.05300","title":"Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures","created":"2025-04-07","cited_by_count":23,"area":"diffusion"},{"id":"2501.04765","title":"TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training","created":"2025-01-08","cited_by_count":21,"area":"diffusion"},{"id":"2408.15094","title":"Constrained Diffusion Models via Dual Training","created":"2024-08-27","cited_by_count":20,"area":"diffusion"},{"id":"2402.13901","title":"Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach","created":"2024-02-21","cited_by_count":19,"area":"diffusion"},{"id":"2404.18444","title":"U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models","created":"2024-04-29","cited_by_count":17,"area":"diffusion"},{"id":"2410.13746","title":"Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers","created":"2024-10-17","cited_by_count":7,"area":"diffusion"},{"id":"2407.16134","title":"Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data","created":"2024-07-23","cited_by_count":5,"area":"diffusion"},{"id":"2505.04417","title":"Localized Diffusion Models","created":"2025-05-07","cited_by_count":5,"area":"diffusion"},{"id":"2503.08028","title":"Computational bottlenecks for denoising diffusions","created":"2025-03-11","cited_by_count":3,"area":"diffusion"},{"id":"2406.16213","title":"Provable Statistical Rates for Consistency Diffusion Models","created":"2024-06-23","cited_by_count":2,"area":"diffusion"},{"id":"2601.01475","title":"Multi-Subspace Multi-Modal Modeling for Diffusion Models: Estimation, Convergence and Mixture of Experts","created":"2026-01-04","cited_by_count":1,"area":"diffusion"},{"id":"2601.21200","title":"Provably Reliable Classifier Guidance via Cross-Entropy Control","created":"2026-01-29","cited_by_count":1,"area":"diffusion"},{"id":"2606.06007","title":"Diffusion Models for Adaptive Sequential Data Generation","created":"2026-06-04","cited_by_count":1,"area":"diffusion"},{"id":"2510.02216","title":"Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification","created":"2025-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2610.02128","title":"Sample complexity bounds for categorical Markov random fields via Discrete Diffusions","created":"2026-10-01","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}