{"total":37,"rows":[{"id":"2402.07747","title":"Optimal score estimation via empirical Bayes smoothing","created":"2024-02-12","cited_by_count":61,"area":null},{"id":"2310.02679","title":"Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization","created":"2023-10-04","cited_by_count":57,"area":null},{"id":"2310.03575","title":"Analysis of learning a flow-based generative model from limited sample complexity","created":"2023-10-05","cited_by_count":38,"area":null},{"id":"2310.17582","title":"Convergence of flow-based generative models via proximal gradient descent in Wasserstein space","created":"2023-10-26","cited_by_count":34,"area":null},{"id":"2310.09031","title":"MINDE: Mutual Information Neural Diffusion Estimation","created":"2023-10-13","cited_by_count":31,"area":null},{"id":"2305.17476","title":"Toward Understanding Generative Data Augmentation","created":"2023-05-27","cited_by_count":25,"area":null},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2405.06780","title":"Deep MMD Gradient Flow without adversarial training","created":"2024-05-10","cited_by_count":19,"area":null},{"id":"2406.01461","title":"Hardness of Learning Neural Networks under the Manifold Hypothesis","created":"2024-06-03","cited_by_count":18,"area":null},{"id":"2311.11475","title":"Gaussian Interpolation Flows","created":"2023-11-20","cited_by_count":17,"area":null},{"id":"2402.01779","title":"Plug-and-Play image restoration with Stochastic deNOising REgularization","created":"2024-02-01","cited_by_count":17,"area":null},{"id":"2310.18123","title":"Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling","created":"2023-10-27","cited_by_count":16,"area":null},{"id":"2303.15845","title":"Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems","created":"2023-03-28","cited_by_count":13,"area":null},{"id":"2410.03655","title":"Geometric Representation Condition Improves Equivariant Molecule Generation","created":"2024-10-04","cited_by_count":13,"area":null},{"id":"2312.05579","title":"Conditional Stochastic Interpolation for Generative Learning","created":"2023-12-09","cited_by_count":9,"area":null},{"id":"2410.16710","title":"Influential Language Data Selection via Gradient Trajectory Pursuit","created":"2024-10-22","cited_by_count":7,"area":null},{"id":"2410.20622","title":"Kernel Approximation of Fisher-Rao Gradient Flows","created":"2024-10-27","cited_by_count":7,"area":null},{"id":"2212.01265","title":"Denoising Deep Generative Models","created":"2022-11-30","cited_by_count":6,"area":null},{"id":"2502.09130","title":"Finite-Time Analysis of Discrete-Time Stochastic Interpolants","created":"2025-02-13","cited_by_count":4,"area":null},{"id":"2512.06702","title":"Pathway to $O(\\sqrt{d})$ Complexity bound under Wasserstein metric of flow-based models","created":"2025-12-07","cited_by_count":4,"area":null},{"id":"2512.24152","title":"Fast Score-Based Sampling via Log-Concave Reductions","created":"2025-12-30","cited_by_count":4,"area":null},{"id":"2404.03272","title":"Cryptographic Hardness of Score Estimation","created":"2024-04-04","cited_by_count":3,"area":null},{"id":"2512.23643","title":"Simultaneous Approximation of the Score Function and Its Derivatives by Deep Neural Networks","created":"2025-12-29","cited_by_count":3,"area":null},{"id":"2601.23151","title":"Manifold-Aware Perturbations for Constrained Generative Modeling","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2512.10401","title":"Diffusion differentiable resampling","created":"2025-12-11","cited_by_count":2,"area":null},{"id":"2604.10650","title":"A Deep Generative Approach to Stratified Learning","created":"2026-04-12","cited_by_count":2,"area":null},{"id":"2605.25567","title":"Rao-Blackwellized Score Matching on Manifolds","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2412.05343","title":"Equivariant Denoisers for Image Restoration","created":"2024-12-06","cited_by_count":1,"area":null},{"id":"2604.07213","title":"Diffusion Processes on Implicit Manifolds","created":"2026-04-08","cited_by_count":1,"area":null},{"id":"2606.19084","title":"Optimal score function estimation via derivatives constraints","created":"2026-06-17","cited_by_count":1,"area":null},{"id":"2607.29180","title":"MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation","created":"2026-07-31","cited_by_count":1,"area":null},{"id":"2409.00499","title":"DAP: Diffusion-based Affordance Prediction for Multi-modality Storage","created":"2024-08-31","cited_by_count":0,"area":null},{"id":"2512.10407","title":"Supervised Learning of Random Neural Architectures Structured by Latent Random Fields on Compact Boundaryless Multiply-Connected Manifolds","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.16065","title":"Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2603.18817","title":"Seasoning Generative Models for a Generalization Aftertaste","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2605.21388","title":"On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2609.28338","title":"Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}