{"total":143,"rows":[{"id":"2104.02939","title":"OpenGAN: Open-Set Recognition via Open Data Generation","created":"2021-04-07","cited_by_count":135,"area":null},{"id":"2405.01147","title":"Why Tabular Foundation Models Should Be a Research Priority","created":"2024-05-02","cited_by_count":106,"area":null},{"id":"2207.02862","title":"Verifying the Union of Manifolds Hypothesis for Image Data","created":"2022-07-06","cited_by_count":64,"area":null},{"id":"2108.09274","title":"MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction","created":"2021-08-20","cited_by_count":61,"area":null},{"id":"2201.05867","title":"Transferability in Deep Learning: A Survey","created":"2022-01-15","cited_by_count":57,"area":null},{"id":"2112.02424","title":"Variational Wasserstein gradient flow","created":"2021-12-04","cited_by_count":53,"area":null},{"id":"2105.13010","title":"An error analysis of generative adversarial networks for learning distributions","created":"2021-05-27","cited_by_count":36,"area":null},{"id":"2106.03632","title":"Quantifying and Improving Transferability in Domain Generalization","created":"2021-06-07","cited_by_count":35,"area":null},{"id":"2404.03502","title":"AI and the Problem of Knowledge Collapse","created":"2024-04-04","cited_by_count":34,"area":null},{"id":"2302.00194","title":"Free Lunch for Domain Adversarial Training: Environment Label Smoothing","created":"2023-02-01","cited_by_count":33,"area":null},{"id":"2106.05275","title":"Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows","created":"2021-06-09","cited_by_count":31,"area":null},{"id":"2304.03722","title":"Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data","created":"2023-04-07","cited_by_count":29,"area":null},{"id":"2202.10725","title":"Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain","created":"2022-02-22","cited_by_count":28,"area":null},{"id":"2204.07172","title":"Diagnosing and Fixing Manifold Overfitting in Deep Generative Models","created":"2022-04-14","cited_by_count":28,"area":null},{"id":"2111.01361","title":"Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis","created":"2021-11-02","cited_by_count":27,"area":null},{"id":"2102.12037","title":"Conditional Image Generation by Conditioning Variational Auto-Encoders","created":"2021-02-24","cited_by_count":25,"area":null},{"id":"2107.06018","title":"This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces","created":"2021-07-13","cited_by_count":25,"area":null},{"id":"2207.10024","title":"Difficulty-Aware Simulator for Open Set Recognition","created":"2022-07-20","cited_by_count":24,"area":null},{"id":"2106.05566","title":"A Neural Tangent Kernel Perspective of GANs","created":"2021-06-10","cited_by_count":21,"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":"2107.02968","title":"Deep Extrapolation for Attribute-Enhanced Generation","created":"2021-07-07","cited_by_count":18,"area":null},{"id":"2108.13054","title":"Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks","created":"2021-08-30","cited_by_count":16,"area":null},{"id":"2206.04502","title":"What is a Good Metric to Study Generalization of Minimax Learners?","created":"2022-06-09","cited_by_count":16,"area":null},{"id":"2310.04935","title":"Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory","created":"2023-10-07","cited_by_count":16,"area":null},{"id":"2101.04039","title":"Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications","created":"2021-01-11","cited_by_count":15,"area":null},{"id":"2103.06923","title":"Non-Asymptotic Performance Guarantees for Neural Estimation of $\\mathsf{f}$-Divergences","created":"2021-03-11","cited_by_count":15,"area":null},{"id":"2110.12076","title":"Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review","created":"2021-10-22","cited_by_count":15,"area":null},{"id":"2210.08216","title":"UDoc-GAN: Unpaired Document Illumination Correction with Background Light Prior","created":"2022-10-15","cited_by_count":15,"area":null},{"id":"2503.04641","title":"Simulating the Real World: A Unified Survey of Multimodal Generative Models","created":"2025-03-06","cited_by_count":15,"area":null},{"id":"2101.07235","title":"Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary","created":"2021-01-18","cited_by_count":14,"area":null},{"id":"2104.05605","title":"Understanding Overparameterization in Generative Adversarial Networks","created":"2021-04-12","cited_by_count":14,"area":null},{"id":"2301.12811","title":"SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer","created":"2023-01-30","cited_by_count":14,"area":null},{"id":"2203.07018","title":"A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources","created":"2022-03-14","cited_by_count":13,"area":null},{"id":"2302.08942","title":"PAC-Bayesian Generalization Bounds for Adversarial Generative Models","created":"2023-02-17","cited_by_count":13,"area":null},{"id":"2302.13183","title":"On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds","created":"2023-02-25","cited_by_count":13,"area":null},{"id":"2105.04046","title":"A likelihood approach to nonparametric estimation of a singular distribution using deep generative models","created":"2021-05-09","cited_by_count":12,"area":null},{"id":"2106.00576","title":"Exposing Previously Undetectable Faults in Deep Neural Networks","created":"2021-06-01","cited_by_count":11,"area":null},{"id":"2309.11617","title":"Statistical Complexity of Quantum Learning","created":"2023-09-20","cited_by_count":11,"area":null},{"id":"2106.05232","title":"Realizing GANs via a Tunable Loss Function","created":"2021-06-09","cited_by_count":10,"area":null},{"id":"2109.14090","title":"Reversible Gromov-Monge Sampler for Simulation-Based Inference","created":"2021-09-28","cited_by_count":10,"area":null},{"id":"2305.12734","title":"EMEF: Ensemble Multi-Exposure Image Fusion","created":"2023-05-22","cited_by_count":10,"area":null},{"id":"2202.10947","title":"Provably convergent quasistatic dynamics for mean-field two-player zero-sum games","created":"2022-02-15","cited_by_count":9,"area":null},{"id":"2206.01132","title":"A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning","created":"2022-06-02","cited_by_count":9,"area":null},{"id":"2206.09238","title":"On the Role of Generalization in Transferability of Adversarial Examples","created":"2022-06-18","cited_by_count":9,"area":null},{"id":"2209.12611","title":"MaxMatch: Semi-Supervised Learning with Worst-Case Consistency","created":"2022-09-26","cited_by_count":9,"area":null},{"id":"2305.16446","title":"The Representation Jensen-Shannon Divergence","created":"2023-05-25","cited_by_count":9,"area":null},{"id":"2107.03633","title":"Generalization Error of GAN from the Discriminator's Perspective","created":"2021-07-08","cited_by_count":8,"area":null},{"id":"2302.01237","title":"Robust Estimation under the Wasserstein Distance","created":"2023-02-02","cited_by_count":8,"area":null},{"id":"2302.14670","title":"Balanced Training for Sparse GANs","created":"2023-02-28","cited_by_count":8,"area":null},{"id":"2405.18997","title":"Kernel Semi-Implicit Variational Inference","created":"2024-05-29","cited_by_count":8,"area":null},{"id":"2106.07537","title":"A Wasserstein Minimax Framework for Mixed Linear Regression","created":"2021-06-14","cited_by_count":7,"area":null},{"id":"2212.11481","title":"A Mathematical Framework for Learning Probability Distributions","created":"2022-12-22","cited_by_count":7,"area":null},{"id":"2307.14709","title":"Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation","created":"2023-07-27","cited_by_count":7,"area":null},{"id":"2102.08577","title":"DO-GAN: A Double Oracle Framework for Generative Adversarial Networks","created":"2021-02-17","cited_by_count":6,"area":null},{"id":"2106.06976","title":"Game of GANs: Game-Theoretical Models for Generative Adversarial Networks","created":"2021-06-13","cited_by_count":6,"area":null},{"id":"2109.05652","title":"Inferential Wasserstein Generative Adversarial Networks","created":"2021-09-13","cited_by_count":6,"area":null},{"id":"2111.14212","title":"On Predicting Generalization using GANs","created":"2021-11-28","cited_by_count":6,"area":null},{"id":"2201.07206","title":"Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs","created":"2022-01-18","cited_by_count":6,"area":null},{"id":"2205.04730","title":"Power of Quantum Generative Learning","created":"2022-05-10","cited_by_count":6,"area":null},{"id":"2307.01171","title":"Quantum Neural Estimation of Entropies","created":"2023-07-03","cited_by_count":6,"area":null},{"id":"2311.07693","title":"Matching aggregate posteriors in the variational autoencoder","created":"2023-11-13","cited_by_count":6,"area":null},{"id":"2205.02043","title":"A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks","created":"2022-05-04","cited_by_count":5,"area":null},{"id":"2205.06393","title":"$\\alpha$-GAN: Convergence and Estimation Guarantees","created":"2022-05-12","cited_by_count":5,"area":null},{"id":"2210.06300","title":"Generalised Mutual Information for Discriminative Clustering","created":"2022-10-12","cited_by_count":5,"area":null},{"id":"2306.09618","title":"Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions","created":"2023-06-16","cited_by_count":5,"area":null},{"id":"2310.18291","title":"Addressing GAN Training Instabilities via Tunable Classification Losses","created":"2023-10-27","cited_by_count":5,"area":null},{"id":"2404.08980","title":"Stability and Generalization in Free Adversarial Training","created":"2024-04-13","cited_by_count":5,"area":null},{"id":"2404.10176","title":"Multi-objective evolutionary GAN for tabular data synthesis","created":"2024-04-15","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":"2305.13517","title":"Statistical Guarantees of Group-Invariant GANs","created":"2023-05-22","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":"2102.08929","title":"Signal Propagation in a Gradient-Based and Evolutionary Learning System","created":"2021-02-10","cited_by_count":3,"area":null},{"id":"2103.03460","title":"Vicinal and categorical domain adaptation","created":"2021-03-05","cited_by_count":3,"area":null},{"id":"2104.10601","title":"Statistical inference for generative adversarial networks and other minimax problems","created":"2021-04-21","cited_by_count":3,"area":null},{"id":"2105.04801","title":"On Characterizing GAN Convergence Through Proximal Duality Gap","created":"2021-05-11","cited_by_count":3,"area":null},{"id":"2110.03995","title":"Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency","created":"2021-10-08","cited_by_count":3,"area":null},{"id":"2110.12319","title":"Non-Asymptotic Error Bounds for Bidirectional GANs","created":"2021-10-24","cited_by_count":3,"area":null},{"id":"2110.14150","title":"Training Wasserstein GANs without gradient penalties","created":"2021-10-27","cited_by_count":3,"area":null},{"id":"2204.04209","title":"Learning Polynomial Transformations","created":"2022-04-08","cited_by_count":3,"area":null},{"id":"2210.04319","title":"Dissecting adaptive methods in GANs","created":"2022-10-09","cited_by_count":3,"area":null},{"id":"2301.00383","title":"Discriminative Radial Domain Adaptation","created":"2023-01-01","cited_by_count":3,"area":null},{"id":"2302.01516","title":"Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation","created":"2023-02-03","cited_by_count":3,"area":null},{"id":"2307.16422","title":"Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution","created":"2023-07-31","cited_by_count":3,"area":null},{"id":"2311.01375","title":"Monotone Generative Modeling via a Gromov-Monge Embedding","created":"2023-11-02","cited_by_count":3,"area":null},{"id":"2402.16435","title":"Training Implicit Generative Models via an Invariant Statistical Loss","created":"2024-02-26","cited_by_count":3,"area":null},{"id":"2410.16600","title":"Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2511.19289","title":"Performance Guarantees for Quantum Neural Estimation of Entropies","created":"2025-11-24","cited_by_count":3,"area":null},{"id":"2101.09225","title":"Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence","created":"2021-01-22","cited_by_count":2,"area":null},{"id":"2104.02388","title":"Generalization of GANs and overparameterized models under Lipschitz continuity","created":"2021-04-06","cited_by_count":2,"area":null},{"id":"2105.01129","title":"Towards A Multi-agent System for Online Hate Speech Detection","created":"2021-05-03","cited_by_count":2,"area":null},{"id":"2106.15011","title":"Are conditional GANs explicitly conditional?","created":"2021-06-28","cited_by_count":2,"area":null},{"id":"2204.10495","title":"Adversarial Estimators","created":"2022-04-22","cited_by_count":2,"area":null},{"id":"2205.13684","title":"Learning with Stochastic Orders","created":"2022-05-27","cited_by_count":2,"area":null},{"id":"2207.03406","title":"Neural Stein critics with staged $L^2$-regularization","created":"2022-07-07","cited_by_count":2,"area":null},{"id":"2209.06853","title":"Asymptotic Statistical Analysis of $f$-divergence GAN","created":"2022-09-14","cited_by_count":2,"area":null},{"id":"2302.12823","title":"Generative Models of Huge Objects","created":"2023-02-24","cited_by_count":2,"area":null},{"id":"2302.14320","title":"$(\\alpha_D,\\alpha_G)$-GANs: Addressing GAN Training Instabilities via Dual Objectives","created":"2023-02-28","cited_by_count":2,"area":null},{"id":"2303.00970","title":"PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium","created":"2023-03-02","cited_by_count":2,"area":null},{"id":"2305.00132","title":"LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance Regularization","created":"2023-04-29","cited_by_count":2,"area":null}],"next_cursor":"100"}