{"total":28,"rows":[{"id":"2103.05180","title":"An Introduction to Deep Generative Modeling","created":"2021-03-09","cited_by_count":104,"area":null},{"id":"2110.15828","title":"Resampling Base Distributions of Normalizing Flows","created":"2021-10-29","cited_by_count":42,"area":null},{"id":"2206.14476","title":"Can Push-forward Generative Models Fit Multimodal Distributions?","created":"2022-06-29","cited_by_count":41,"area":null},{"id":"2211.12461","title":"A Neural-Network-Based Convex Regularizer for Inverse Problems","created":"2022-11-22","cited_by_count":41,"area":null},{"id":"2109.11375","title":"Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint","created":"2021-09-23","cited_by_count":32,"area":null},{"id":"2210.16222","title":"Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions","created":"2022-10-28","cited_by_count":24,"area":null},{"id":"2111.12506","title":"Generalized Normalizing Flows via Markov Chains","created":"2021-11-24","cited_by_count":23,"area":null},{"id":"2205.12021","title":"PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization","created":"2022-05-24","cited_by_count":22,"area":null},{"id":"2204.06233","title":"Approximation of Lipschitz Functions using Deep Spline Neural Networks","created":"2022-04-13","cited_by_count":15,"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":"2308.02652","title":"A Review of Change of Variable Formulas for Generative Modeling","created":"2023-08-04","cited_by_count":13,"area":null},{"id":"2102.03189","title":"Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence","created":"2021-02-05","cited_by_count":12,"area":null},{"id":"2312.16611","title":"Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction","created":"2023-12-27","cited_by_count":9,"area":null},{"id":"2206.07128","title":"Stability of Image-Reconstruction Algorithms","created":"2022-06-14","cited_by_count":7,"area":null},{"id":"2305.12149","title":"Normalizing flow sampling with Langevin dynamics in the latent space","created":"2023-05-20","cited_by_count":7,"area":null},{"id":"2311.06481","title":"Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning","created":"2023-11-11","cited_by_count":7,"area":null},{"id":"2504.08544","title":"Slicing the Gaussian Mixture Wasserstein Distance","created":"2025-04-11","cited_by_count":7,"area":null},{"id":"2205.14627","title":"Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces","created":"2022-05-29","cited_by_count":6,"area":null},{"id":"2402.16408","title":"Stable Training of Normalizing Flows for High-dimensional Variational Inference","created":"2024-02-26","cited_by_count":6,"area":null},{"id":"2211.17158","title":"Proximal Residual Flows for Bayesian Inverse Problems","created":"2022-11-30","cited_by_count":5,"area":null},{"id":"2410.00169","title":"(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition Number","created":"2024-09-30","cited_by_count":5,"area":null},{"id":"2304.08342","title":"NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems","created":"2023-04-17","cited_by_count":4,"area":null},{"id":"2309.12128","title":"Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems","created":"2023-09-21","cited_by_count":3,"area":null},{"id":"2308.09367","title":"On the Approximation of Bi-Lipschitz Maps by Invertible Neural Networks","created":"2023-08-18","cited_by_count":2,"area":null},{"id":"2305.02930","title":"Piecewise Normalizing Flows","created":"2023-05-04","cited_by_count":1,"area":null},{"id":"2402.12072","title":"Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview","created":"2024-02-19","cited_by_count":1,"area":null},{"id":"2508.09655","title":"Noise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging","created":"2025-08-13","cited_by_count":1,"area":null},{"id":"2510.12636","title":"Adapting Noise to Data: Generative Flows from 1D Processes","created":"2025-10-14","cited_by_count":0,"area":null}],"next_cursor":null}