{"total":21,"rows":[{"id":"2106.00774","title":"Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks","created":"2021-06-01","cited_by_count":53,"area":null},{"id":"2106.05566","title":"A Neural Tangent Kernel Perspective of GANs","created":"2021-06-10","cited_by_count":21,"area":null},{"id":"2311.11475","title":"Gaussian Interpolation Flows","created":"2023-11-20","cited_by_count":17,"area":null},{"id":"2302.01075","title":"MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows","created":"2023-02-02","cited_by_count":13,"area":null},{"id":"2302.00061","title":"Dynamic Flows on Curved Space Generated by Labeled Data","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2208.09938","title":"Instability and Local Minima in GAN Training with Kernel Discriminators","created":"2022-08-21","cited_by_count":7,"area":null},{"id":"2605.11884","title":"Sobolev Regularized MMD Gradient Flow","created":"2026-05-12","cited_by_count":6,"area":null},{"id":"2205.02910","title":"GANs as Gradient Flows that Converge","created":"2022-05-05","cited_by_count":5,"area":null},{"id":"2111.13282","title":"Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey","created":"2021-11-26","cited_by_count":4,"area":null},{"id":"2306.07926","title":"A Theory of Unsupervised Speech Recognition","created":"2023-06-09","cited_by_count":4,"area":null},{"id":"2302.02092","title":"Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics","created":"2023-02-04","cited_by_count":3,"area":null},{"id":"2303.03714","title":"Generative Modeling with Flow-Guided Density Ratio Learning","created":"2023-03-07","cited_by_count":2,"area":null},{"id":"2306.00785","title":"Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are","created":"2023-06-01","cited_by_count":2,"area":null},{"id":"2306.15835","title":"Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures","created":"2023-06-27","cited_by_count":2,"area":null},{"id":"2504.07522","title":"Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data","created":"2025-04-10","cited_by_count":2,"area":null},{"id":"2110.13452","title":"On the Optimization Landscape of Maximum Mean Discrepancy","created":"2021-10-26","cited_by_count":1,"area":null},{"id":"2210.13879","title":"Proximal Mean Field Learning in Shallow Neural Networks","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2212.04580","title":"Effective Dynamics of Generative Adversarial Networks","created":"2022-12-08","cited_by_count":1,"area":null},{"id":"2305.08277","title":"Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks","created":"2023-05-14","cited_by_count":0,"area":null},{"id":"2307.01879","title":"Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow","created":"2023-07-04","cited_by_count":0,"area":null},{"id":"2609.32708","title":"One-Step Generative Modeling via Unbalanced Optimal Transport","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}