{"list":"other","total":88,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2304.08485","title":"Visual Instruction Tuning","created":"2023-04-17","cited_by_count":7869},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"2403.03206","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","created":"2024-03-05","cited_by_count":4181},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1411.1784","title":"Conditional Generative Adversarial Nets","created":"2014-11-06","cited_by_count":2162},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"id":"1609.03499","title":"WaveNet: A Generative Model for Raw Audio","created":"2016-09-12","cited_by_count":1722},{"id":"1606.05328","title":"Conditional Image Generation with PixelCNN Decoders","created":"2016-06-16","cited_by_count":862},{"id":"2310.04378","title":"Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference","created":"2023-10-06","cited_by_count":846},{"id":"1601.06759","title":"Pixel Recurrent Neural Networks","created":"2016-01-25","cited_by_count":816},{"id":"2401.08740","title":"SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers","created":"2024-01-16","cited_by_count":737},{"id":"1907.00503","title":"Modeling Tabular data using Conditional GAN","created":"2019-07-01","cited_by_count":699},{"id":"1801.04406","title":"Which Training Methods for GANs do actually Converge?","created":"2018-01-13","cited_by_count":679},{"id":"2107.03502","title":"CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation","created":"2021-07-07","cited_by_count":582},{"id":"2104.02008","title":"Domain Generalization with MixStyle","created":"2021-04-05","cited_by_count":566},{"id":"1606.00709","title":"f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization","created":"2016-06-02","cited_by_count":506},{"id":"1406.5298","title":"Semi-Supervised Learning with Deep Generative Models","created":"2014-06-20","cited_by_count":496},{"id":"1507.00504","title":"Optimal Transport for Domain Adaptation","created":"2015-07-02","cited_by_count":463},{"id":"2406.20094","title":"Scaling Synthetic Data Creation with 1,000,000,000 Personas","created":"2024-06-28","cited_by_count":417},{"id":"2209.15421","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","created":"2022-09-30","cited_by_count":377},{"id":"2112.10510","title":"Transformers Can Do Bayesian Inference","created":"2021-12-20","cited_by_count":360},{"id":"1910.06711","title":"MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis","created":"2019-10-08","cited_by_count":354},{"id":"1610.05755","title":"Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data","created":"2016-10-18","cited_by_count":348},{"id":"2306.04637","title":"Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection","created":"2023-06-07","cited_by_count":298},{"id":"1511.01844","title":"A note on the evaluation of generative models","created":"2015-11-05","cited_by_count":292},{"id":"1805.10572","title":"BRITS: Bidirectional Recurrent Imputation for Time Series","created":"2018-05-27","cited_by_count":290},{"id":"1705.08848","title":"Joint Distribution Optimal Transportation for Domain Adaptation","created":"2017-05-24","cited_by_count":282},{"id":"1711.04340","title":"Data Augmentation Generative Adversarial Networks","created":"2017-11-12","cited_by_count":278},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252},{"id":"2301.09633","title":"Prediction-Powered Inference","created":"2023-01-23","cited_by_count":220},{"id":"2102.08921","title":"How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models","created":"2021-02-17","cited_by_count":196},{"id":"1502.04623","title":"DRAW: A Recurrent Neural Network For Image Generation","created":"2015-02-16","cited_by_count":191},{"id":"1912.00042","title":"Learning Likelihoods with Conditional Normalizing Flows","created":"2019-11-29","cited_by_count":172},{"id":"2102.08369","title":"CTAB-GAN: Effective Table Data Synthesizing","created":"2021-02-16","cited_by_count":156},{"id":"2308.06259","title":"Self-Alignment with Instruction Backtranslation","created":"2023-08-11","cited_by_count":151},{"id":"1903.03303","title":"Learning from Synthetic Data for Crowd Counting in the Wild","created":"2019-03-08","cited_by_count":148},{"id":"2204.07258","title":"Causal Transformer for Estimating Counterfactual Outcomes","created":"2022-04-14","cited_by_count":123},{"id":"1911.02469","title":"Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse","created":"2019-11-06","cited_by_count":121},{"id":"2302.04062","title":"Machine Learning for Synthetic Data Generation: A Review","created":"2023-02-08","cited_by_count":120},{"id":"2412.15188","title":"LMFusion: Adapting Pretrained Language Models for Multimodal Generation","created":"2024-12-19","cited_by_count":120},{"id":"2110.12884","title":"DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks","created":"2021-10-25","cited_by_count":109},{"id":"1712.04086","title":"PacGAN: The power of two samples in generative adversarial networks","created":"2017-12-12","cited_by_count":104},{"id":"2010.05862","title":"Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation","created":"2020-10-12","cited_by_count":87},{"id":"1901.03416","title":"Preventing Posterior Collapse with delta-VAEs","created":"2019-01-10","cited_by_count":86},{"id":"2002.09464","title":"Private Mean Estimation of Heavy-Tailed Distributions","created":"2020-02-21","cited_by_count":73},{"id":"2102.05855","title":"Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent","created":"2021-02-11","cited_by_count":73},{"id":"1802.04908","title":"Conditional Density Estimation with Bayesian Normalising Flows","created":"2018-02-14","cited_by_count":54},{"id":"2412.08635","title":"Multimodal Latent Language Modeling with Next-Token Diffusion","created":"2024-12-11","cited_by_count":48},{"id":"2203.11528","title":"Out-of-distribution Generalization with Causal Invariant Transformations","created":"2022-03-22","cited_by_count":45},{"id":"2109.00666","title":"TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks","created":"2021-09-02","cited_by_count":34},{"id":"2501.09731","title":"Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI","created":"2025-01-16","cited_by_count":34},{"id":"1901.06077","title":"Kernel Change-point Detection with Auxiliary Deep Generative Models","created":"2019-01-18","cited_by_count":33},{"id":"2305.09235","title":"Synthetic data, real errors: how (not) to publish and use synthetic data","created":"2023-05-16","cited_by_count":29},{"id":"2410.09695","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","created":"2024-10-13","cited_by_count":23},{"id":"1412.6515","title":"On distinguishability criteria for estimating generative models","created":"2014-12-19","cited_by_count":21},{"id":"2403.06741","title":"Distribution-Aware Data Expansion with Diffusion Models","created":"2024-03-11","cited_by_count":20},{"id":"2406.01355","title":"Differentially Private Fine-Tuning of Diffusion Models","created":"2024-06-03","cited_by_count":20},{"id":"2109.06486","title":"Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data","created":"2021-09-14","cited_by_count":18},{"id":"2302.10160","title":"Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift","created":"2023-02-20","cited_by_count":17},{"id":"2409.16118","title":"TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models","created":"2024-09-24","cited_by_count":16},{"id":"2201.08186","title":"Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations","created":"2022-01-20","cited_by_count":14},{"id":"2301.13334","title":"A Bias-Accuracy-Privacy Trilemma for Statistical Estimation","created":"2023-01-30","cited_by_count":12},{"id":"2409.00547","title":"Data Augmentation for Image Classification using Generative AI","created":"2024-08-31","cited_by_count":11},{"id":"1802.06403","title":"RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks","created":"2018-02-18","cited_by_count":10},{"id":"2509.11950","title":"TabStruct: Measuring Structural Fidelity of Tabular Data","created":"2025-09-15","cited_by_count":9},{"id":"2509.16842","title":"DoubleGen: Debiased Generative Modeling of Counterfactuals","created":"2025-09-20","cited_by_count":9},{"id":"2504.07426","title":"Conditional Data Synthesis Augmentation","created":"2025-04-10","cited_by_count":8},{"id":"2109.12546","title":"Synthetic Data Generation for Fraud Detection using GANs","created":"2021-09-26","cited_by_count":7},{"id":"2304.02107","title":"30 Years of Synthetic Data","created":"2023-04-04","cited_by_count":7},{"id":"2312.07837","title":"The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data","created":"2023-12-13","cited_by_count":6},{"id":"2510.26046","title":"Bias-Corrected Data Synthesis for Imbalanced Learning","created":"2025-10-30","cited_by_count":6},{"id":"2312.12216","title":"Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data","created":"2023-12-19","cited_by_count":2},{"id":"2511.00318","title":"A Technical Exploration of Causal Inference with Hybrid LLM Synthetic Data","created":"2025-10-31","cited_by_count":1}],"next_cursor":null}