{"total":57,"rows":[{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114,"area":"diffusion"},{"id":"2101.09258","title":"Maximum Likelihood Training of Score-Based Diffusion Models","created":"2021-01-22","cited_by_count":672,"area":"diffusion"},{"id":"2102.05379","title":"Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions","created":"2021-02-10","cited_by_count":574,"area":"diffusion"},{"id":"2208.04202","title":"Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning","created":"2022-08-08","cited_by_count":373,"area":"diffusion"},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337,"area":"diffusion"},{"id":"2201.06503","title":"Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models","created":"2022-01-17","cited_by_count":334,"area":"diffusion"},{"id":"2209.02646","title":"A Survey on Generative Diffusion Model","created":"2022-09-06","cited_by_count":240,"area":"diffusion"},{"id":"2211.09707","title":"Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models","created":"2022-11-17","cited_by_count":190,"area":"diffusion"},{"id":"2106.05527","title":"Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation","created":"2021-06-10","cited_by_count":97,"area":"diffusion"},{"id":"2206.07309","title":"Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models","created":"2022-06-15","cited_by_count":83,"area":"diffusion"},{"id":"2111.12701","title":"Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes","created":"2021-11-24","cited_by_count":71,"area":"diffusion"},{"id":"2110.00473","title":"Score-Based Generative Classifiers","created":"2021-10-01","cited_by_count":53,"area":"diffusion"},{"id":"2405.16441","title":"Categorical Flow Matching on Statistical Manifolds","created":"2024-05-26","cited_by_count":47,"area":"diffusion"},{"id":"2206.05173","title":"How Much is Enough? A Study on Diffusion Times in Score-based Generative Models","created":"2022-06-10","cited_by_count":35,"area":"diffusion"},{"id":"2304.01565","title":"A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material","created":"2023-04-04","cited_by_count":35,"area":"diffusion"},{"id":"2311.07896","title":"Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation","created":"2023-11-14","cited_by_count":29,"area":"diffusion"},{"id":"2502.20946","title":"Generative Uncertainty in Diffusion Models","created":"2025-02-28","cited_by_count":23,"area":"diffusion"},{"id":"2411.01293","title":"Diffusion Models as Cartoonists: The Curious Case of High Density Regions","created":"2024-11-02","cited_by_count":21,"area":"diffusion"},{"id":"2303.04291","title":"Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition","created":"2023-03-07","cited_by_count":20,"area":"diffusion"},{"id":"2411.18350","title":"TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models","created":"2024-11-27","cited_by_count":19,"area":"diffusion"},{"id":"2310.19789","title":"DiffEnc: Variational Diffusion with a Learned Encoder","created":"2023-10-30","cited_by_count":17,"area":"diffusion"},{"id":"2604.02718","title":"Generative Frontiers: Why Evaluation Matters for Diffusion Language Models","created":"2026-04-03","cited_by_count":16,"area":"diffusion"},{"id":"2303.15342","title":"Exploring Continual Learning of Diffusion Models","created":"2023-03-27","cited_by_count":15,"area":"diffusion"},{"id":"2304.11750","title":"DiffVoice: Text-to-Speech with Latent Diffusion","created":"2023-04-23","cited_by_count":13,"area":"diffusion"},{"id":"2406.11473","title":"Promises, Outlooks and Challenges of Diffusion Language Modeling","created":"2024-06-17","cited_by_count":12,"area":"diffusion"},{"id":"2309.16812","title":"SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models","created":"2023-09-28","cited_by_count":11,"area":"diffusion"},{"id":"2510.06050","title":"Edit-Based Flow Matching for Temporal Point Processes","created":"2025-10-07","cited_by_count":8,"area":"diffusion"},{"id":"2404.00525","title":"Creating synthetic energy meter data using conditional diffusion and building metadata","created":"2024-03-31","cited_by_count":5,"area":"diffusion"},{"id":"2505.23264","title":"Efficiently Access Diffusion Fisher: Within the Outer Product Span Space","created":"2025-05-29","cited_by_count":5,"area":"diffusion"},{"id":"2303.04635","title":"Diffusing Gaussian Mixtures for Generating Categorical Data","created":"2023-03-08","cited_by_count":4,"area":"diffusion"},{"id":"2312.07534","title":"Cosmological Field Emulation and Parameter Inference with Diffusion Models","created":"2023-12-12","cited_by_count":3,"area":"diffusion"},{"id":"2506.00849","title":"Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis","created":"2025-06-01","cited_by_count":3,"area":"diffusion"},{"id":"2507.01516","title":"Loss Functions in Diffusion Models: A Comparative Study","created":"2025-07-02","cited_by_count":3,"area":"diffusion"},{"id":"2312.14220","title":"Single-Cell RNA-seq Synthesis with Latent Diffusion Model","created":"2023-12-21","cited_by_count":2,"area":"diffusion"},{"id":"2407.04495","title":"Speed-accuracy relations for diffusion models: Wisdom from nonequilibrium thermodynamics and optimal transport","created":"2024-07-05","cited_by_count":2,"area":"diffusion"},{"id":"2508.09164","title":"Generating Feasible and Diverse Synthetic Populations Using Diffusion Models","created":"2025-08-06","cited_by_count":2,"area":"diffusion"},{"id":"2511.02986","title":"Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models","created":"2025-11-04","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":"2211.10747","title":"Exploring validation metrics for offline model-based optimisation with diffusion models","created":"2022-11-19","cited_by_count":1,"area":"diffusion"},{"id":"2307.15988","title":"RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects","created":"2023-07-29","cited_by_count":1,"area":"diffusion"},{"id":"2406.19619","title":"ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters","created":"2024-06-28","cited_by_count":1,"area":"diffusion"},{"id":"2601.11444","title":"When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models","created":"2026-01-16","cited_by_count":1,"area":"diffusion"},{"id":"2608.17286","title":"Abra: Scaling Diffusion Image Training","created":"2026-08-18","cited_by_count":1,"area":"diffusion"},{"id":"2310.10691","title":"Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation","created":"2023-10-15","cited_by_count":0,"area":"diffusion"},{"id":"2407.08513","title":"Fine-Tuning Stable Diffusion XL for Stylistic Icon Generation: A Comparison of Caption Size","created":"2024-07-11","cited_by_count":0,"area":"diffusion"},{"id":"2409.06364","title":"What happens to diffusion model likelihood when your model is conditional?","created":"2024-09-10","cited_by_count":0,"area":"diffusion"},{"id":"2505.04173","title":"DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion","created":"2025-05-07","cited_by_count":0,"area":"diffusion"},{"id":"2511.19434","title":"Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts","created":"2025-11-24","cited_by_count":0,"area":"diffusion"},{"id":"2601.22125","title":"Creative Image Generation with Diffusion Models","created":"2026-01-29","cited_by_count":0,"area":"diffusion"},{"id":"2605.02989","title":"Information Theory and Statistical Learning","created":"2026-05-04","cited_by_count":0,"area":"diffusion"},{"id":"2605.08698","title":"Supersampling Stable Diffusion and Beyond: A Seamless, Training-Free Approach for Scaling Neural Networks Using Common Interpolation Methods","created":"2026-05-09","cited_by_count":0,"area":"diffusion"},{"id":"2605.21381","title":"Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration","created":"2026-05-20","cited_by_count":0,"area":"diffusion"},{"id":"2606.13451","title":"Uncertainty Estimation for Molecular Diffusion Models","created":"2026-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2608.12155","title":"Understanding Why Foundation Models Work for Diffusion-Generated Image Detection","created":"2026-08-12","cited_by_count":0,"area":"diffusion"},{"id":"2609.23055","title":"Optimizers for Diffusion Models: A Controlled Benchmark","created":"2026-09-19","cited_by_count":0,"area":"diffusion"},{"id":"2609.27070","title":"The Gaussian Is Enough: Flow-Matching Priors Do Not Help When Fine-Tuning Large Behavior Models","created":"2026-09-22","cited_by_count":0,"area":"diffusion"},{"id":"2609.38918","title":"Flow Matching under Noisy Latent Structure: Beyond Exact Low-Dimensional Support","created":"2026-09-30","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}