{"total":30,"rows":[{"id":"2402.18491","title":"Dynamical Regimes of Diffusion Models","created":"2024-02-28","cited_by_count":142,"area":"diffusion"},{"id":"2412.20292","title":"An analytic theory of creativity in convolutional diffusion models","created":"2024-12-28","cited_by_count":104,"area":"diffusion"},{"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":"2505.17638","title":"Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training","created":"2025-05-23","cited_by_count":81,"area":"diffusion"},{"id":"2408.02320","title":"A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models","created":"2024-08-05","cited_by_count":65,"area":"diffusion"},{"id":"2409.13074","title":"What does guidance do? A fine-grained analysis in a simple setting","created":"2024-09-19","cited_by_count":48,"area":"diffusion"},{"id":"2403.01633","title":"Critical windows: non-asymptotic theory for feature emergence in diffusion models","created":"2024-03-03","cited_by_count":47,"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":"2404.18869","title":"Learning Mixtures of Gaussians Using Diffusion Models","created":"2024-04-29","cited_by_count":42,"area":"diffusion"},{"id":"2410.24060","title":"Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure","created":"2024-10-31","cited_by_count":38,"area":"diffusion"},{"id":"2401.04856","title":"A Good Score Does not Lead to A Good Generative Model","created":"2024-01-10","cited_by_count":36,"area":"diffusion"},{"id":"2410.23285","title":"Provable Acceleration for Diffusion Models under Minimal Assumptions","created":"2024-10-30","cited_by_count":27,"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":"2505.16959","title":"Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models","created":"2025-05-22","cited_by_count":25,"area":"diffusion"},{"id":"2503.09583","title":"Minimax Optimality of the Probability Flow ODE for Diffusion Models","created":"2025-03-12","cited_by_count":23,"area":"diffusion"},{"id":"2502.00336","title":"Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves","created":"2025-02-01","cited_by_count":20,"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":"2501.03937","title":"A solvable model of learning generative diffusion: theory and insights","created":"2025-01-07","cited_by_count":14,"area":"diffusion"},{"id":"2602.04404","title":"Theory of Speciation Transitions in Diffusion Models with General Class Structure","created":"2026-02-04","cited_by_count":10,"area":"diffusion"},{"id":"2603.12901","title":"A theory of learning data statistics in diffusion models, from easy to hard","created":"2026-03-13","cited_by_count":5,"area":"diffusion"},{"id":"2602.04780","title":"Dynamical Regimes of Multimodal Diffusion Models","created":"2026-02-04","cited_by_count":4,"area":"diffusion"},{"id":"2603.22962","title":"Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data","created":"2026-03-24","cited_by_count":3,"area":"diffusion"},{"id":"2607.08041","title":"An exact information theory of generalization phase transitions in Bayesian diffusion models","created":"2026-07-09","cited_by_count":3,"area":"diffusion"},{"id":"2509.23385","title":"Flow Matching Calibration for Simulation-Based Inference under Model Misspecification","created":"2025-09-27","cited_by_count":2,"area":"diffusion"},{"id":"2605.06077","title":"Understanding diffusion models requires rethinking (again) generalization","created":"2026-05-07","cited_by_count":2,"area":"diffusion"},{"id":"2605.06367","title":"The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models","created":"2026-05-07","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":"2606.03820","title":"A Quantitative Approximation Framework for Flow Distillation in Diffusion Models","created":"2026-06-02","cited_by_count":1,"area":"diffusion"},{"id":"2609.26392","title":"Double Descent and Malign Overfitting in Diffusion Models","created":"2026-09-22","cited_by_count":1,"area":"diffusion"},{"id":"2609.35322","title":"Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data","created":"2026-09-28","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}